Compare commits
18
Commits
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+151
-1
@@ -23,7 +23,11 @@ nohup python cvae.py --name="lhunor_abl_all" > nohup/iccv/ablation/abl_all_3.out
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nohup python cvae.py --name="lhunor_abl_all_gnn" > nohup/iccv/ablation/abl_all_gnn.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_gnn_2" > nohup/iccv/ablation/abl_all_gnn.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_cape_f" > nohup/iccv/ablation/abl_all_cape_f_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_skirt" > nohup/iccv/ablation/abl_all_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_skirt_var" > nohup/iccv/ablation/abl_all_skirt_var.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_skirt_var" > nohup/iccv/ablation/abl_all_skirt_var_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_curv" > nohup/iccv/ablation/abl_curv.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_curv_skirt" > nohup/iccv/ablation/abl_curv_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin" > nohup/iccv/ablation/abl_skin.out 2>&1 &
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@@ -37,6 +41,11 @@ nohup python cvae.py --name="lhunor_abl_skin_reg" > nohup/iccv/ablation/abl_skin
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nohup python cvae.py --name="lhunor_abl_skin_reg_skirt" > nohup/iccv/ablation/abl_skin_reg_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_var" > nohup/iccv/ablation/abl_skin_var.out 2>&1 &
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nohup python evaluate_ae.py --name abl_all > nohup/iccv/evaluate/abl_all.out 2>&1 &
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nohup python evaluate_ae.py --name abl_all_skirt > nohup/iccv/evaluate/abl_all_skirt.out 2>&1 &
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nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/iccv/ablation/cvae_curv_3.out 2>&1 &
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nohup python upscale_test.py --name="iccv_32_2" > nohup/iccv/upscale/iccv_32_2.out 2>&1 &
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nohup python upscale_test.py --name="iccv_64_2" > nohup/iccv/upscale/iccv_64_2.out 2>&1 &
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nohup python upscale_test.py --name="iccv_128_2" > nohup/iccv/upscale/iccv_128_2.out 2>&1 &
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@@ -66,14 +75,155 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_curv" > nohup/iccv/pyr/i
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_curv_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg" > nohup/iccv/pyr/iccv_pyr_32_reg.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg_skirt" > nohup/iccv/pyr/iccv_pyr_32_reg_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_10p" > nohup/iccv/pyr/iccv_pyr_32_10p.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_25p" > nohup/iccv/pyr/iccv_pyr_32_25p.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_jumpsuit.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_trousers" > nohup/iccv/pyr/iccv_pyr_32_trousers.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_top" > nohup/iccv/pyr/iccv_pyr_32_top.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_tshirt" > nohup/iccv/pyr/iccv_pyr_32_tshirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_skirt_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_skirt_dress.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_batch" > nohup/iccv/pyr/iccv_pyr_32_skirt_batch.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_seq" > nohup/iccv/pyr/iccv_pyr_32_seq_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64" > nohup/iccv/pyr/iccv_pyr_32_64.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skin" > nohup/iccv/pyr/iccv_pyr_32_64_skin.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skin" > nohup/iccv/pyr/iccv_pyr_32_64_skin.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skin_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skin_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_curv" > nohup/iccv/pyr/iccv_pyr_32_64_curv.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_curv_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg" > nohup/iccv/pyr/iccv_pyr_32_64_reg.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_reg_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_10p" > nohup/iccv/pyr/iccv_pyr_32_64_10p.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_25p" > nohup/iccv/pyr/iccv_pyr_32_64_25p.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_tshirt" > nohup/iccv/pyr/iccv_pyr_32_64_tshirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_trousers" > nohup/iccv/pyr/iccv_pyr_32_64_trousers.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_top" > nohup/iccv/pyr/iccv_pyr_32_64_top.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_64_skirt_dress.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_64_jumpsuit.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_seq" > nohup/iccv/pyr/iccv_pyr_32_64_seq.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_seq_2" > nohup/iccv/pyr/iccv_pyr_32_64_seq_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f" > nohup/iccv/pyr/iccv_pyr_64_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_m" > nohup/iccv/pyr/iccv_pyr_64_cape_m.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_m_00032_SL" > nohup/iccv/pyr/iccv_pyr_64_cape_m_00032_SL.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f_03223_SL" > nohup/iccv/pyr/iccv_pyr_64_cape_f_03223_SL.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f_03223_SS" > nohup/iccv/pyr/iccv_pyr_64_cape_f_03223_SS.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_f" > nohup/iccv/pyr/iccv_pyr_64_128_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_m" > nohup/iccv/pyr/iccv_pyr_64_128_cape_m.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_256_cape_f" > nohup/iccv/pyr/iccv_pyr_64_128_256_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_pre_cape_f" > nohup/iccv/pyr/iccv_pyr_64_pre_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_skin_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skin_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skin_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_reg_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_var" > nohup/iccv/pyr/iccv_pyr_32_64_128_var.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_tshirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_tshirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_trousers" > nohup/iccv/pyr/iccv_pyr_32_64_128_trousers.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_top" > nohup/iccv/pyr/iccv_pyr_32_64_128_top.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_64_128_jumpsuit.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt_dress.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_m_00032_SL" > nohup/iccv/pyr/iccv_pyr_64_128_cape_m_00032_SL.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_f_03223_SL" > nohup/iccv/pyr/iccv_pyr_64_128_cape_f_03223_SL.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_f_03223_SS" > nohup/iccv/pyr/iccv_pyr_64_128_cape_f_03223_SS.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_skirt_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_skin.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_reg.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_skin.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_curv.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_reg.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_fine" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_fine.out 2>&1 &
|
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32 > nohup/iccv/evaluate/iccv_pyr_32.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skirt > nohup/iccv/evaluate/iccv_pyr_32_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skin > nohup/iccv/evaluate/iccv_pyr_32_skin.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skin_skirt > nohup/iccv/evaluate/iccv_pyr_32_skin_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_reg > nohup/iccv/evaluate/iccv_pyr_32_reg.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_reg_skirt > nohup/iccv/evaluate/iccv_pyr_32_reg_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_curv > nohup/iccv/evaluate/iccv_pyr_32_curv.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_curv_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_10p > nohup/iccv/evaluate/iccv_pyr_32_10p.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_25p > nohup/iccv/evaluate/iccv_pyr_32_25p.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64 > nohup/iccv/evaluate/iccv_pyr_32_64.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_skin > nohup/iccv/evaluate/iccv_pyr_32_64_skin.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_skin_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_skin_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_reg > nohup/iccv/evaluate/iccv_pyr_32_64_reg.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_skirt.out 2>&1 &
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_curv > nohup/iccv/evaluate/iccv_pyr_32_64_curv.out 2>&1 &
|
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_curv_skirt.out 2>&1 &
|
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_10p > nohup/iccv/evaluate/iccv_pyr_32_64_10p.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_25p > nohup/iccv/evaluate/iccv_pyr_32_64_25p.out 2>&1 &
|
||||
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohcup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_reg.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_trousers > nohup/iccv/evaluate/iccv_pyr_32_64_128_trousers.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_tshirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_tshirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_top > nohup/iccv/evaluate/iccv_pyr_32_64_128_top.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_jumpsuit > nohup/iccv/evaluate/iccv_pyr_32_64_128_jumpsuit.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt_dress> nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt_dress.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
|
||||
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256 > nohup/iccv/evaluate/iccv_pyr_32_64_128_256.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_skin.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_curv.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_reg.out 2>&1 &
|
||||
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512 > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_reg.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_skin.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_curv.out 2>&1 &
|
||||
|
||||
nohup python cvae.py --name="lhunor_cape_f_03223_SL" > nohup/iccv/ablation/abl_all_cape_f_03223_SL.out 2>&1 &
|
||||
nohup python cvae.py --name="lhunor_cape_f_03223_SS" > nohup/iccv/ablation/abl_all_cape_f_03223_SS.out 2>&1 &
|
||||
nohup python cvae.py --name="lhunor_cape_m_00032_SL" > nohup/iccv/ablation/abl_all_cape_m_00032_SL.out 2>&1 &
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
skirts!!!!iccv_pyr_32_curv_skirt
|
||||
pyramid next week
|
||||
|
||||
+72
-30
@@ -1,37 +1,37 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.iccv.config_abl_pyr_32_64_128_tshirt import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
# import tensorflow as tf
|
||||
import tensorflow as tf
|
||||
|
||||
# gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
# if gpus:
|
||||
# try:
|
||||
# # Currently, memory growth needs to be the same across GPUs
|
||||
# for gpu in gpus:
|
||||
# tf.config.experimental.set_memory_growth(gpu, True)
|
||||
# logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
# print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
# except RuntimeError as e:
|
||||
# # Memory growth must be set before GPUs have been initialized
|
||||
# print(e)
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
if gpus:
|
||||
try:
|
||||
# Currently, memory growth needs to be the same across GPUs
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
except RuntimeError as e:
|
||||
# Memory growth must be set before GPUs have been initialized
|
||||
print(e)
|
||||
|
||||
import csv
|
||||
import glob
|
||||
import time
|
||||
import argparse
|
||||
import wandb
|
||||
import tqdm
|
||||
from tqdm import tqdm
|
||||
import random
|
||||
import numpy as np
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as ex
|
||||
|
||||
# if config['is_skirt']:
|
||||
# from losses import edge_loss_skirt as edge_loss
|
||||
# else:
|
||||
# from losses import edge_loss
|
||||
from losses import collision_loss, normal_loss
|
||||
if config['is_skirt']:
|
||||
from losses import edge_loss_skirt as edge_loss
|
||||
else:
|
||||
from losses import edge_loss
|
||||
|
||||
|
||||
|
||||
@@ -53,40 +53,62 @@ if __name__ == '__main__':
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
print(config['name'])
|
||||
|
||||
# smpl = SMPL(use_one_hot=config['use_one_hot'])
|
||||
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||
|
||||
error_type = 'euclidean' # 'euclidean' 'collision' edge normal
|
||||
|
||||
folder = config['test_dir']
|
||||
|
||||
checkpoint_name = config['name']
|
||||
iteration = ''
|
||||
# iteration = '200000'
|
||||
|
||||
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
|
||||
garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
|
||||
|
||||
|
||||
output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration
|
||||
output_folder = '/home/workspace/local-data/results/iccv/' + checkpoint_name + '-'
|
||||
# check if there is a folder that starts with this name using glob
|
||||
folders = glob.glob(output_folder + '*')
|
||||
assert len(folders) == 1, f'There should be only one checkpoint that starts with this name, but found {len(folders)}'
|
||||
output_folder = folders[0]
|
||||
iteration = output_folder.split('-')[-1]
|
||||
|
||||
|
||||
faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
|
||||
ids = os.listdir(faces_folder)
|
||||
ids = [id.split('.')[0] for id in ids]
|
||||
faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
|
||||
# faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
|
||||
# ids = os.listdir(faces_folder)
|
||||
# ids = [id.split('.')[0] for id in ids]
|
||||
# faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
|
||||
|
||||
masks_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
|
||||
print('Reading masks...')
|
||||
masks_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
|
||||
ids = os.listdir(masks_folder)
|
||||
ids = [id.split('.')[0] for id in ids]
|
||||
masks = {id : np.load(masks_folder + '/' + id + '.npy') for id in ids }
|
||||
|
||||
frames = os.listdir(output_folder)
|
||||
frames = [frame for frame in frames if not frame.endswith('_fixed_coll.npy')]
|
||||
# frames = frames[:100]
|
||||
|
||||
# if 'frames_file_test' in config.keys():
|
||||
# with open(os.path.join(folder, config['frames_file_test']), 'r') as f:
|
||||
# lines = f.readlines()
|
||||
# lines = [line.strip() for line in lines]
|
||||
# frames = lines
|
||||
|
||||
if error_type == 'collision':
|
||||
print('Reading bodies...')
|
||||
bodies_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_posed_body_no_orient'
|
||||
bodies = {frame : np.load(bodies_folder + '/' + frame[:-1] + 'z')['mesh_posed_body_no_orient'] for frame in tqdm(frames) }
|
||||
|
||||
if config['remove_global_orientation']:
|
||||
gt_name = 'mesh_posed_no_orient'
|
||||
else:
|
||||
gt_name = 'mesh_posed'
|
||||
|
||||
orig_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
|
||||
orig_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
|
||||
|
||||
errors = dict()
|
||||
if config['is_skirt']:
|
||||
@@ -94,12 +116,32 @@ if __name__ == '__main__':
|
||||
else:
|
||||
per_garment_errors = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
|
||||
|
||||
for frame in tqdm.tqdm(frames):
|
||||
for frame in tqdm(frames):
|
||||
id = frame.split('_')[0]
|
||||
frame_nr = frame.split('.')[0].split('_')[1]
|
||||
gt = np.load(os.path.join(orig_folder, id + '_' + frame_nr + '.npz'))[gt_name][masks[id], :].astype(np.float32)
|
||||
pred = np.load(os.path.join(output_folder, id + '_' + frame_nr + '.npy'))
|
||||
metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
|
||||
if error_type == 'edge':
|
||||
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
|
||||
pred_unmasked[0, masks[id], :] = pred
|
||||
gt_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
|
||||
gt_unmasked[0, masks[id], :] = gt
|
||||
metric = edge_loss(gt_unmasked, pred_unmasked, masks[id][None,:].astype(np.float32)).numpy()[0]
|
||||
elif error_type == 'euclidean':
|
||||
metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
|
||||
elif error_type == 'collision':
|
||||
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
|
||||
pred_unmasked[0, masks[id], :] = pred
|
||||
metric = collision_loss(pred_unmasked, bodies[frame][None,:,:], masks[id][None,:].astype(np.float32))[0].numpy()[0]
|
||||
elif error_type == 'normal':
|
||||
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
|
||||
pred_unmasked[0, masks[id], :] = pred
|
||||
gt_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
|
||||
gt_unmasked[0, masks[id], :] = gt
|
||||
metric = normal_loss(gt_unmasked, pred_unmasked, masks[id][None,:].astype(np.float32)).numpy()
|
||||
else:
|
||||
raise ValueError('Error type not supported')
|
||||
|
||||
errors[id + '_' + frame_nr] = metric
|
||||
per_garment_errors[garment_type_table[id]].append(metric)
|
||||
|
||||
@@ -107,7 +149,7 @@ if __name__ == '__main__':
|
||||
for key in per_garment_errors.keys():
|
||||
print(key + ': ' + str(np.mean(per_garment_errors[key])))
|
||||
|
||||
with open('/home/workspace/data/results/errors/post/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
|
||||
with open(f'/home/workspace/data/results/errors/iccv/{checkpoint_name}-{iteration}-{error_type}.csv', mode='w') as f:
|
||||
file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
|
||||
|
||||
for key in sorted(errors.keys()):
|
||||
|
||||
+23
-12
@@ -23,7 +23,7 @@ import numpy as np
|
||||
import trimesh as tm
|
||||
import plotly.express as ex
|
||||
import plotly.graph_objects as go
|
||||
from custom_utils.custom_utils import _maskFaces
|
||||
from custom_utils.custom_utils import _maskFaces, save_stl
|
||||
from custom_utils.smpl import SMPL
|
||||
from plotly.subplots import make_subplots
|
||||
from scipy.spatial.transform import Rotation as R
|
||||
@@ -215,8 +215,8 @@ if __name__ == '__main__':
|
||||
errors_cape_m = get_cape_errors('CAPE/results/CAPE-affineconv_nz18_pose24_clotype8_male/errors.npy', frames_m)
|
||||
errors_cape_m = dict(reversed(sorted(errors_cape_m.items(), key=lambda item: item[1])))
|
||||
|
||||
errors_f = get_errors('temp/error_abl_2_pyr_64_128_256_512_cape_f.txt')
|
||||
errors_m = get_errors('temp/error_abl_2_pyr_64_128_256_512_cape_m.txt')
|
||||
errors_f = get_errors('temp/archive/error_abl_2_pyr_64_128_256_512_cape_f.txt')
|
||||
errors_m = get_errors('temp/archive/error_abl_2_pyr_64_128_256_512_cape_m.txt')
|
||||
|
||||
folder_pyr_f = '/home/workspace/data/results/cape/abl_2_pyr_64_128_256_512_cape_f'
|
||||
folder_pyr_m = '/home/workspace/data/results/cape/abl_2_pyr_64_128_256_512_cape_m'
|
||||
@@ -238,9 +238,10 @@ if __name__ == '__main__':
|
||||
|
||||
|
||||
_, faces = _maskFaces(np.zeros((3638, 3)), np.load('custom_utils/smpl_faces_orig.npy'), cape_mask)
|
||||
faces = faces.astype(np.int32)
|
||||
colors = np.ones((3627, 3), np.int8) * 230
|
||||
|
||||
gender = 'male'
|
||||
gender = 'female'
|
||||
|
||||
if gender == 'male':
|
||||
folder_cape = folder_m
|
||||
@@ -262,11 +263,18 @@ if __name__ == '__main__':
|
||||
all_errors_1 = []
|
||||
all_errors_2 = []
|
||||
|
||||
# for key in tqdm.tqdm(errors_cape.keys()):
|
||||
count = 0
|
||||
while True:
|
||||
key = random.choice(list(errors_cape.keys()))
|
||||
for key in tqdm.tqdm(errors_cape.keys()):
|
||||
# count = 0
|
||||
# while True:
|
||||
if os.path.exists(f'temp/cape_stl/{key}_gt.stl'):
|
||||
continue
|
||||
# key = random.choice(list(errors_cape.keys()))
|
||||
cape = read_obj(os.path.join(folder_cape, '{:0>4d}.obj'.format(frames_to_i[key])))[clothing_verts_idx, :] - J0[np.newaxis, :]
|
||||
split = key.split('_')
|
||||
file = '_'.join(split[:-1]) + '.' + split[-1] + '.npy'
|
||||
pose_shape_path = os.path.join(pose_folder, file)
|
||||
if not os.path.exists(pose_shape_path):
|
||||
continue
|
||||
cape = add_global_orientation(cape, key, pose_folder, inverse=True)
|
||||
if not os.path.exists(os.path.join(folder, key + '_posed.npy')):
|
||||
continue
|
||||
@@ -284,10 +292,13 @@ if __name__ == '__main__':
|
||||
# print(np.mean(all_errors_1))
|
||||
# print(np.mean(all_errors_2))
|
||||
|
||||
fig = plot_fixed_all(gt, cape, ours, errors_cape[key], errors[key], faces, colors)
|
||||
fig.write_image('temp/cape/' + key + '.png')
|
||||
# fig = plot_fixed_all(gt, cape, ours, errors_cape[key], errors[key], faces, colors)
|
||||
# fig.write_image('temp/cape_2/' + key + '.png')
|
||||
save_stl(f'temp/cape_stl/{key}_gt.stl', gt, faces)
|
||||
save_stl(f'temp/cape_stl/{key}_cape.stl', cape, faces)
|
||||
save_stl(f'temp/cape_stl/{key}_ours.stl', ours, faces)
|
||||
|
||||
print(count)
|
||||
count += 1
|
||||
# print(count)
|
||||
# count += 1
|
||||
|
||||
print()
|
||||
@@ -0,0 +1,65 @@
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
import trimesh as tm
|
||||
from stl import mesh
|
||||
|
||||
# os.environ["CUDA_VISIBLE_DEVICES"] = '1'
|
||||
# import tensorflow as tf
|
||||
|
||||
# gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
# if gpus:
|
||||
# try:
|
||||
# # Currently, memory growth needs to be the same across GPUs
|
||||
# for gpu in gpus:
|
||||
# tf.config.experimental.set_memory_growth(gpu, True)
|
||||
# logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
# print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
# except RuntimeError as e:
|
||||
# # Memory growth must be set before GPUs have been initialized
|
||||
# print(e)
|
||||
|
||||
# from losses import collision_loss, normal_loss
|
||||
|
||||
|
||||
# from losses import edge_loss_skirt as edge_loss
|
||||
# from losses import edge_loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
frame = '04173_193' # '02276_232', '02978_242', '04173_193'
|
||||
folder = 'examples/sampling_analysis'
|
||||
# original_folder = '/home/workspace/data/dataset/local-cache/train_skirt'
|
||||
original_folder = '/home/workspace/local-data/train'
|
||||
# folder = 'examples/sampling'
|
||||
|
||||
id = frame.split('_')[0]
|
||||
mask = np.load(os.path.join(original_folder, 'mesh_mask', f'{id}.npy'))
|
||||
gt = np.load(os.path.join(folder, frame, f'{frame}_gt.npy'))
|
||||
faces = np.load(os.path.join(folder, frame, f'{frame}_faces.npy'))
|
||||
edges = tm.Trimesh(vertices=gt, faces=faces, validate=False, process=False).edges
|
||||
edges_gt = np.linalg.norm(gt[edges[:, 0]] - gt[edges[:, 1]], axis=1)
|
||||
area_gt = np.sum(np.linalg.norm(np.cross(gt[faces[:, 0]] - gt[faces[:, 1]], gt[faces[:, 0]] - gt[faces[:, 2]]), axis=1))
|
||||
|
||||
errors = []
|
||||
vars = []
|
||||
area_diffs = []
|
||||
# for i in [1,2,4,10]:
|
||||
for i in range(15):
|
||||
sample = np.load(os.path.join(folder, frame, f'{frame}_{i}.npy'))
|
||||
edges_sample = np.linalg.norm(sample[edges[:, 0]] - sample[edges[:, 1]], axis=1)
|
||||
error = np.mean(np.abs(edges_sample - edges_gt))
|
||||
var = np.var(np.abs(edges_sample - edges_gt))
|
||||
errors.append(error)
|
||||
vars.append(var)
|
||||
|
||||
area_sample = np.sum(np.linalg.norm(np.cross(sample[faces[:, 0]] - sample[faces[:, 1]], sample[faces[:, 0]] - sample[faces[:, 2]]), axis=1))
|
||||
area_diff = np.abs(area_sample - area_gt)
|
||||
area_diffs.append(area_diff)
|
||||
|
||||
print(np.array(errors).mean())
|
||||
print(np.array(vars).mean())
|
||||
print(np.array(area_diffs).mean())
|
||||
print(area_gt)
|
||||
|
||||
|
||||
@@ -35,9 +35,9 @@ config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'abl_all_cape_f_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'abl_all']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_cape_f_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/abl_all_cape_f'
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.0001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,128 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'abl_all_cape_f_03223_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'abl_all']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_cape_f_03223_SL_2_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['frames_file'] = 'frames_03223_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SL_test_2.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.0001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,128 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'abl_all_cape_f_03223_SS'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'abl_all']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_cape_f_03223_SS_2_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['frames_file'] = 'frames_03223_SS_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SS_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SS_test_2.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.0001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,128 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'abl_all_cape_m_00032_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'abl_all']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_cape_m_00032_SL_2_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['frames_file'] = 'frames_00032_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_00032_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_00032_SL_test_2.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_m'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_m'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_m'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.0001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -37,7 +37,7 @@ config['is_cape'] = False
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'abl_all_skirt_var_dasda'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'abl_all', 'skirt']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = True
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/abl_all_skirt'
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.1
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (14475, 3,),
|
||||
'mesh_posed': (14475, 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (14475,),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -32,7 +32,8 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
@@ -93,11 +94,10 @@ for res in config['pyramid']['resolutions']:
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
@@ -106,13 +106,12 @@ for res in config['pyramid']['resolutions']:
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
@@ -120,13 +119,12 @@ for res in config['pyramid']['resolutions']:
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
@@ -139,7 +137,7 @@ for res in config['pyramid']['resolutions']:
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_10p'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
|
||||
config['frames_file'] = 'frames_10p.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_10p'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_10p'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_10p'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_25p'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
|
||||
config['frames_file'] = 'frames_25p.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_25p'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_25p'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_25p'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_64_10p'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_10p'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
|
||||
config['frames_file'] = 'frames_10p.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_10p'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_10p'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_10p'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,7 +1,7 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128'
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_128_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
@@ -27,12 +27,12 @@ config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64'
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,188 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_512'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_2'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '90000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/train'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
# config['frames_file'] = 'frames_00761_0.txt'
|
||||
# config['frames_file_valid'] = 'frames_00761_0.txt'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = True
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512_curv'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_curv'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512_fine'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_512'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/train'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
# config['frames_file'] = 'frames_00761_0.txt'
|
||||
# config['frames_file_valid'] = 'frames_00761_0.txt'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = True
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.000001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512_reg'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_reg'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,195 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512_skin'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_skin'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_512_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_skirt_2'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '90000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_curv'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_curv'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_curv_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_curv_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_reg'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_reg_2'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,195 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_skin'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_skin_2'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_256_skirt_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_skirt'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_curv'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_curv'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '110000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_curv_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_curv_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '60000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_jumpsuit'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_jumpsuit'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['frames_file'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_valid'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_test'] = 'frames_jumpsuit.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_64_128_reg_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_reg'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_reg_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_reg_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,196 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_64_128_skin_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_skin'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_64_128_skin_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skin_skirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_128_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '235000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '7'
|
||||
config['name'] = 'iccv_pyr_32_64_128_skirt_dress'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skirt_dress'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_dress.txt'
|
||||
config['frames_file_valid'] = 'frames_dress.txt'
|
||||
config['frames_file_test'] = 'frames_dress.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_skirt_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skirt_skirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_skirt.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt.txt'
|
||||
config['frames_file_test'] = 'frames_skirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,197 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_top'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_top'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_top.txt'
|
||||
config['frames_file_valid'] = 'frames_top.txt'
|
||||
config['frames_file_test'] = 'frames_top.txt'
|
||||
config['frames_file_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_top.txt'
|
||||
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,197 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_128_top_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_top'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_top.txt'
|
||||
config['frames_file_valid'] = 'frames_top.txt'
|
||||
config['frames_file_test'] = 'frames_top.txt'
|
||||
config['frames_file_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_top.txt'
|
||||
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_trousers'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_trousers'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['frames_file'] = 'frames_trousers.txt'
|
||||
config['frames_file_valid'] = 'frames_trousers.txt'
|
||||
config['frames_file_test'] = 'frames_trousers.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,196 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_tshirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_tshirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test'] = 'frames_tshirt.txt'
|
||||
config['frames_file_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_tshirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,196 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_tshirt_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_tshirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test'] = 'frames_tshirt.txt'
|
||||
config['frames_file_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_tshirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,188 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_128_var'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_var'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.1
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_25p'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_25p'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
|
||||
config['frames_file'] = 'frames_25p.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_25p'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_25p'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_25p'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_curv'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_curv'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '150000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,186 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_curv_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = False
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_curv_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '225000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_jumpsuit'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_jumpsuit'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['frames_file'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_valid'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_test'] = 'frames_jumpsuit.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_reg'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_reg/'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '150000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,186 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_reg_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = False # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = False
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_curv_skirt'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '190000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_skin'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -27,12 +27,13 @@ config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skin_165k'
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skin'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '235000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
@@ -119,7 +120,7 @@ for res in config['pyramid']['resolutions']:
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
@@ -133,7 +134,7 @@ for res in config['pyramid']['resolutions']:
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_skin_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skin_skirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_skin_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = False # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skin_skirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_64_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_skirt_dress'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skirt_dress'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_dress.txt'
|
||||
config['frames_file_valid'] = 'frames_dress.txt'
|
||||
config['frames_file_test'] = 'frames_dress.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_skirt_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skirt_skirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_skirt.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt.txt'
|
||||
config['frames_file_test'] = 'frames_skirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,197 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_64_top'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_top'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_top.txt'
|
||||
config['frames_file_valid'] = 'frames_top.txt'
|
||||
config['frames_file_test'] = 'frames_top.txt'
|
||||
config['frames_file_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_top.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_top.txt'
|
||||
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_trousers'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_trousers'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['frames_file'] = 'frames_trousers.txt'
|
||||
config['frames_file_valid'] = 'frames_trousers.txt'
|
||||
config['frames_file_test'] = 'frames_trousers.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,196 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_64_tshirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_tshirt'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test'] = 'frames_tshirt.txt'
|
||||
config['frames_file_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test_skirt'] = 'frames_tshirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '6'
|
||||
config['name'] = 'iccv_pyr_32_64_var'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32, 64]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_var'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 1.0
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,187 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '7'
|
||||
config['name'] = 'iccv_pyr_32_batch'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
|
||||
config['batch_size'] = 64
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '6'
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_curv_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'iccv_pyr_32_jumpsuit'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['frames_file'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_valid'] = 'frames_jumpsuit.txt'
|
||||
config['frames_file_test'] = 'frames_jumpsuit.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_reg'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_reg_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'reg']
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_skin'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -20,6 +20,7 @@ config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
@@ -31,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_skin_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -20,6 +20,7 @@ config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
@@ -31,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_skirt_3'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
@@ -20,6 +20,7 @@ config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
@@ -31,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '7'
|
||||
config['name'] = 'iccv_pyr_32_skirt_batch'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_skirt.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt.txt'
|
||||
config['frames_file_test'] = 'frames_skirt.txt'
|
||||
|
||||
config['batch_size'] = 128
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_skirt_dress'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_dress.txt'
|
||||
config['frames_file_valid'] = 'frames_dress.txt'
|
||||
config['frames_file_test'] = 'frames_dress.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_32_skirt_skirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train_skirt'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_skirt.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt.txt'
|
||||
config['frames_file_test'] = 'frames_skirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_test'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_top.txt'
|
||||
config['frames_file_valid'] = 'frames_top.txt'
|
||||
config['frames_file_test'] = 'frames_top.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_32_top'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_top.txt'
|
||||
config['frames_file_valid'] = 'frames_top.txt'
|
||||
config['frames_file_test'] = 'frames_top.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,190 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_32_trousers'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['frames_file'] = 'frames_trousers.txt'
|
||||
config['frames_file_valid'] = 'frames_trousers.txt'
|
||||
config['frames_file_test'] = 'frames_trousers.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_32_tshirt'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [32]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
|
||||
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
|
||||
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
config['frames_file'] = 'frames_tshirt.txt'
|
||||
config['frames_file_valid'] = 'frames_tshirt.txt'
|
||||
config['frames_file_test'] = 'frames_tshirt.txt'
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
@@ -0,0 +1,188 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_64_128_256_cape_f'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128, 256]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_128_cape_f'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '170000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_cape_f'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_f'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['name'] = 'iccv_pyr_64_128_cape_f'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_cape_f'
|
||||
config['pyramid']['pretrained_pyramid_iteration'] = '140000'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_f'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,191 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_64_128_cape_f_03223_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_cape_f_03223_SL'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
config['frames_file'] = 'frames_03223_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SL_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_f'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,191 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_64_128_cape_f_03223_SS'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_cape_f_03223_SS'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
config['frames_file'] = 'frames_03223_SS_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SS_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SS_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_f'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,188 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_64_128_cape_m'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_m']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_cape_m'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_m'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_m'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_m'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_m'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_m'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_m'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,192 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_64_128_cape_m_00032_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_m']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64, 128]
|
||||
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_64_cape_m_00032_SL'
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['frames_file'] = 'frames_00032_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_00032_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_00032_SL_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_m'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_m'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_m'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_m'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_m'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_m'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -1,6 +1,6 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '6'
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_64_cape_f'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
@@ -20,8 +20,9 @@ config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_64_cape_f'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
@@ -29,9 +30,9 @@ config['pyramid']['resolutions'] = [64]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/test/female'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/test/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
@@ -0,0 +1,192 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'iccv_pyr_64_cape_f_03223_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/train/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
config['frames_file'] = 'frames_03223_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SL_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,192 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['name'] = 'iccv_pyr_64_cape_f_03223_SS'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_f']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['valid_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/train/female'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
config['frames_file'] = 'frames_03223_SS_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_03223_SS_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_03223_SS_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'iccv_pyr_64_cape_m'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_m']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_64_cape_m'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/test/male'
|
||||
config['test_dir'] = '/home/workspace/local-data_127/cape_processed/test/male'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_m'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_m'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_m'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_m'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_m'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,193 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['name'] = 'iccv_pyr_64_cape_m_00032_SL'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['pyr', 'iccv', 'cape_m']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK IF SKIRT
|
||||
# DON'T FORGET TO CHECK IF CAPE
|
||||
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
|
||||
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = False
|
||||
config['use_norm_loss'] = True
|
||||
config['use_curvature'] = True
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
config['pyramid'] = dict()
|
||||
config['pyramid']['resolutions'] = [64]
|
||||
config['pyramid']['pretrained_pyramid'] = ''
|
||||
config['pyramid']['final_upscale_enhancer'] = ''
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['valid_dir'] = '/home/workspace/local-data_127/cape_processed/train/male'
|
||||
config['test_dir'] = '/home/workspace/local-data/cape_processed/train/male'
|
||||
config['batch_size'] = 4
|
||||
config['is_skirt'] = False
|
||||
config['is_cape'] = True
|
||||
|
||||
config['frames_file'] = 'frames_00032_SL_train_2.txt'
|
||||
config['frames_file_valid'] = 'frames_00032_SL_test_2.txt'
|
||||
config['frames_file_test'] = 'frames_00032_SL_test_2.txt'
|
||||
|
||||
config['dataset_in_memory'] = False
|
||||
config['resolution'] = None
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'mesh_unposed': (config['smpl_res'], 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
config['pyramid']['is_label'] = False
|
||||
config['pyramid']['add_noise'] = False
|
||||
config['pyramid']['noise_amount'] = 0.0
|
||||
|
||||
config['pyramid']['loss_weights'] = [1,1,1,1,1]
|
||||
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res != 512:
|
||||
res_modifier = '_' + str(res)
|
||||
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
|
||||
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
|
||||
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
|
||||
|
||||
|
||||
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_m'
|
||||
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_m'
|
||||
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_m'
|
||||
config['pyramid']['freeze_encoders'] = True
|
||||
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (16, 8, 80)
|
||||
level['decoder_depths'] = [3, 3, 9, 3]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
|
||||
level['decoder_input'] = (8, 4, 128)
|
||||
level['decoder_depths'] = [9, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 64, 16]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 128:
|
||||
level = dict()
|
||||
level['resolution'] = 128
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
level['encoder_dims'] = [64, 128, 128, 64]
|
||||
level['decoder_input'] = (4, 2, 128)
|
||||
level['decoder_depths'] = [16, 9, 9, 9]
|
||||
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 64:
|
||||
level = dict()
|
||||
level['resolution'] = 64
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [9, 9]
|
||||
level['encoder_dims'] = [64, level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_m'#checkpoints/up_3_d64_128_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 32:
|
||||
level = dict()
|
||||
level['resolution'] = 32
|
||||
level['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
level['kernel_size'] = 3
|
||||
level['encoder_depths'] = [16]
|
||||
level['encoder_dims'] = [level['latent_dim']]
|
||||
level['decoder_depths'] = level['encoder_depths']
|
||||
level['decoder_dims'] = level['encoder_dims']
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_m'#'checkpoints/up_3_d32_64_2'
|
||||
level['upscale_enhancer_freeze'] = True
|
||||
config['pyramid']['levels'].append(level)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,121 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'cvae_curv_3'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['iccv', 'cvae_curv']
|
||||
|
||||
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
# if config['use_skinning']:
|
||||
# assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['train_dir'] = '/home/workspace/local-data/train'
|
||||
config['valid_dir'] = '/home/workspace/local-data/valid'
|
||||
config['test_dir'] = '/home/workspace/local-data/test_ready'
|
||||
|
||||
config['static_model'] = 'checkpoints/iccv/static_iccv_1'
|
||||
config['body_model'] = 'checkpoints/iccv/body_iccv_1'
|
||||
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['pretrained_model'] = 'checkpoints/iccv/cvae_curv'
|
||||
config['pretrained_model_iteration'] = ''
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 1.0
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (14475, 3,),
|
||||
'mesh_posed': (14475, 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
# 'mesh_curvature': (14475)
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (14475,),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = True
|
||||
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
|
||||
config['cvae']['add_noise'] = False
|
||||
config['cvae']['noise_amount'] = 0.0
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,381 @@
|
||||
import os
|
||||
import sys
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '4'
|
||||
|
||||
|
||||
import tqdm
|
||||
import argparse
|
||||
import random
|
||||
import string
|
||||
import time
|
||||
import numpy as np
|
||||
import trimesh as tm
|
||||
import cmapy as cm
|
||||
import scipy as sp
|
||||
import scipy.io as sio
|
||||
import plotly.graph_objects as go
|
||||
from pyntcloud import PyntCloud
|
||||
from scipy.sparse import csr_matrix, coo_matrix
|
||||
from scipy.sparse.csgraph import floyd_warshall, dijkstra
|
||||
from plotly.subplots import make_subplots
|
||||
from custom_utils.custom_utils import _maskFaces
|
||||
from postprocessing import fix_collision
|
||||
|
||||
from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
|
||||
from custom_utils.custom_utils import _maskFaces, save_stl, write_obj
|
||||
from auto_encoder_curv import dice_dense
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def postprocess(mesh, faces, mask=None, body=None, do_smoothing=True):
|
||||
if body is not None:
|
||||
mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
|
||||
else:
|
||||
mesh_fixed = mesh
|
||||
|
||||
if do_smoothing:
|
||||
m = tm.Trimesh(mesh_fixed, faces, process=False, validate=False)
|
||||
outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
|
||||
outliers = np.nonzero(outliers)[0]
|
||||
outliers_n_neighbours = get_closest_n_points(mesh_fixed, outliers, 2)
|
||||
outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
|
||||
faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
|
||||
m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
|
||||
tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
|
||||
|
||||
mesh = m.vertices
|
||||
else:
|
||||
mesh = mesh_fixed
|
||||
|
||||
return mesh
|
||||
|
||||
def export_to_ply(mesh, file):
|
||||
ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
|
||||
output_file = open(file, "wb+")
|
||||
output_file.write(ply)
|
||||
output_file.close()
|
||||
|
||||
|
||||
def get_closest_n_points(verts, idx, n):
|
||||
dist = sp.spatial.distance.cdist(verts[idx, :], verts)
|
||||
dist[dist == 0] = 1e9
|
||||
idx_sort = np.argsort(dist, axis=-1)
|
||||
closest = idx_sort[:, :n]
|
||||
return closest
|
||||
|
||||
|
||||
def calculate_dist(folder, neighbourhood):
|
||||
out = dict()
|
||||
frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
|
||||
|
||||
for frame in tqdm.tqdm(frames):
|
||||
id = frame.split('_')[0]
|
||||
if id in out.keys():
|
||||
continue
|
||||
|
||||
mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
|
||||
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
|
||||
out[id] = k_neighbors
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def histedges_equalN(x, nbin):
|
||||
npt = len(x)
|
||||
return np.interp(np.linspace(0, npt, nbin + 1),
|
||||
np.arange(npt),
|
||||
np.sort(x))
|
||||
|
||||
|
||||
def get_bins(values, nr_of_bins):
|
||||
flattened = []
|
||||
for c in values:
|
||||
flattened.extend(c.flatten())
|
||||
flattened = np.array(flattened)
|
||||
bins = histedges_equalN(flattened, nr_of_bins)
|
||||
return bins
|
||||
|
||||
|
||||
def generate_curvatures(v, faces, mask, neighbourhood=25, nr_of_labels=6):
|
||||
# id = frame.split('_')[0]
|
||||
# frame_nr = int(frame.split('_')[1].split('.')[0])
|
||||
|
||||
# mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
|
||||
# _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
|
||||
# v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
|
||||
|
||||
m = tm.Trimesh(v, faces, process=False, validate=False)
|
||||
|
||||
outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
|
||||
outliers = np.nonzero(outliers)[0]
|
||||
outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
|
||||
outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
|
||||
faces = np.concatenate((faces, outliers_faces), axis=0)
|
||||
|
||||
verts = v
|
||||
|
||||
m = tm.Trimesh(verts, faces, process=False, validate=False)
|
||||
export_to_ply(m, temp_file)
|
||||
|
||||
cloud = PyntCloud.from_file(temp_file)
|
||||
|
||||
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
|
||||
#k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
|
||||
ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
|
||||
cloud.add_scalar_field("curvature", ev=ev)
|
||||
curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
|
||||
|
||||
# bins = np.load(f'custom_data/train_bins.npy') # get_bins(curvature, nr_of_labels)
|
||||
bins = get_bins(curvature, nr_of_labels)
|
||||
labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
|
||||
|
||||
frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
|
||||
for j in range(bins.shape[0] - 2):
|
||||
frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
|
||||
frame_labels[curvature >= bins[-2]] = labels[-1]
|
||||
|
||||
return frame_labels
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
|
||||
def visualize_curvature_labels(folder, results_folder, frame):
|
||||
id = frame.split('_')[0]
|
||||
frame_nr = frame.split('_')[1].split('.')[0]
|
||||
|
||||
mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
|
||||
verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
|
||||
pred = np.load(os.path.join(results_folder, frame + '.npy'))
|
||||
pred = postprocess(pred, faces)
|
||||
|
||||
curvature_labels_gt = generate_curvatures(verts, faces, mask, 25)
|
||||
curvature_labels_pred = generate_curvatures(pred, faces, mask, 25)
|
||||
# dice = dice_dense(curvature_labels_pred[None,:], curvature_labels_gt[None,:])
|
||||
dice = dice_dense(tf.one_hot(curvature_labels_pred, 7)[None,:], tf.one_hot(curvature_labels_gt, 7)[None,:]).numpy()
|
||||
colors_binned_all = []
|
||||
|
||||
for curvature_labels in [curvature_labels_gt, curvature_labels_pred]:
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
|
||||
colors_binned = np.zeros((curvature_labels.shape[0], 3))
|
||||
for i in range(int(curvature_labels.max())+1):
|
||||
# print(np.count_nonzero(curvature_labels == i))
|
||||
colors_binned[curvature_labels == i, :] = colors[i]
|
||||
colors_binned_all.append(colors_binned)
|
||||
|
||||
fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=-pred[:,0],
|
||||
y=pred[:,2],
|
||||
z=pred[:,1],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
|
||||
),
|
||||
row=1, col=2)
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=verts[:,0],
|
||||
y=verts[:,1],
|
||||
z=verts[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=colors_binned_all[0]
|
||||
),
|
||||
row=1, col=1)
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=-pred[:,0],
|
||||
y=pred[:,2],
|
||||
z=pred[:,1],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=colors_binned_all[1],
|
||||
text=f'dice: {dice}'
|
||||
),
|
||||
row=1, col=3)
|
||||
|
||||
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
|
||||
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
|
||||
scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
|
||||
# scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||
# scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
|
||||
)
|
||||
helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
|
||||
|
||||
fig.update_layout(scene = helper,
|
||||
scene2 = helper,
|
||||
scene3 = helper,
|
||||
# scene4 = helper,
|
||||
# scene5 = helper,
|
||||
)
|
||||
|
||||
|
||||
# meshes = [go.Mesh3d(
|
||||
# x=x[:,0],
|
||||
# y=x[:,1],
|
||||
# z=x[:,2],
|
||||
# i = faces[:,0],
|
||||
# j = faces[:,1],
|
||||
# k = faces[:,2],
|
||||
# vertexcolor=colors_binned
|
||||
# )]
|
||||
|
||||
# fig = go.Figure(data=meshes)
|
||||
# fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
|
||||
# fig.update_layout(
|
||||
# scene = dict(
|
||||
# xaxis = dict(nticks=4, range=[-1,1],),
|
||||
# yaxis = dict(nticks=4, range=[-1,1],),
|
||||
# zaxis = dict(nticks=4, range=[-1,1],),)
|
||||
# )
|
||||
|
||||
return fig, dice
|
||||
|
||||
import wandb
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='plots', name='curvatures', dir='/home/workspace/local-data/temp/')
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('curvature_analysis.py')
|
||||
run.log_artifact(artifact)
|
||||
|
||||
|
||||
def visualize_neighbourhoods(folder, frame):
|
||||
id = frame.split('_')[0]
|
||||
frame_nr = frame.split('_')[1].split('.')[0]
|
||||
|
||||
mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
|
||||
verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
|
||||
|
||||
curvature_labels_10 = generate_curvatures(verts, faces, mask, 10)
|
||||
curvature_labels_25 = generate_curvatures(verts, faces, mask, 25)
|
||||
curvature_labels_100 = generate_curvatures(verts, faces, mask, 100)
|
||||
colors_binned_all = []
|
||||
|
||||
for curvature_labels in [curvature_labels_10, curvature_labels_25, curvature_labels_100]:
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
|
||||
colors_binned = np.zeros((curvature_labels.shape[0], 3))
|
||||
for i in range(int(curvature_labels.max())+1):
|
||||
# print(np.count_nonzero(curvature_labels == i))
|
||||
colors_binned[curvature_labels == i, :] = colors[i]
|
||||
colors_binned_all.append(colors_binned)
|
||||
|
||||
fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=-verts[:,0],
|
||||
y=verts[:,1],
|
||||
z=verts[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=colors_binned_all[0]
|
||||
),
|
||||
row=1, col=1)
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=verts[:,0],
|
||||
y=verts[:,1],
|
||||
z=verts[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=colors_binned_all[1]
|
||||
),
|
||||
row=1, col=2)
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=-verts[:,0],
|
||||
y=verts[:,1],
|
||||
z=verts[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=colors_binned_all[2],
|
||||
),
|
||||
row=1, col=3)
|
||||
|
||||
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
|
||||
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
|
||||
scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
|
||||
# scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
|
||||
# scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
|
||||
)
|
||||
helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
|
||||
|
||||
fig.update_layout(scene = helper,
|
||||
scene2 = helper,
|
||||
scene3 = helper,
|
||||
# scene4 = helper,
|
||||
# scene5 = helper,
|
||||
)
|
||||
|
||||
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_10.obj', verts, faces.astype(np.int32), colors_binned_all[0].astype(np.int32))
|
||||
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_25.obj', verts, faces.astype(np.int32), colors_binned_all[1].astype(np.int32))
|
||||
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_100.obj', verts, faces.astype(np.int32), colors_binned_all[2].astype(np.int32))
|
||||
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_mesh.obj', verts, faces.astype(np.int32), np.ones((verts.shape[0], 3), dtype=np.int32) * 200)
|
||||
save_stl(f'examples/curvature_analysis/{id}_{frame_nr}_mesh.stl', verts, faces.astype(np.int32))
|
||||
|
||||
fig.show()
|
||||
return fig
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init_wandb()
|
||||
|
||||
is_skirt = True
|
||||
skirt_modifier = '_skirt' if is_skirt else ''
|
||||
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
|
||||
# k_neighbours(k=25)
|
||||
|
||||
folder = '/home/workspace/local-data_128/test_skirt_ready_2'
|
||||
results_folder = '/home/workspace/data/results/iccv/iccv_pyr_32_64_128_256_512-120000'
|
||||
|
||||
# frames_file = 'frames_temp.txt'
|
||||
# with open(os.path.join(folder, frames_file), 'r') as f:
|
||||
# lines = f.readlines()
|
||||
# lines = [line.strip() for line in lines]
|
||||
# frames = lines
|
||||
frames = os.listdir(os.path.join(folder, 'pose_shape'))
|
||||
frames = [frame.split('.')[0] for frame in frames]
|
||||
|
||||
temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
|
||||
if not is_skirt:
|
||||
dist = np.load('custom_data/dijkstra.npy')
|
||||
else:
|
||||
dist = np.load('custom_data/dijkstra_skirt.npy')
|
||||
|
||||
i = 0
|
||||
while True:
|
||||
frame = np.random.choice(frames)
|
||||
print(frame)
|
||||
# frame = '03265_193'
|
||||
# frame = '03102_49'
|
||||
# fig, dice = visualize_curvature_labels(folder, results_folder, frame)
|
||||
# wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
|
||||
visualize_neighbourhoods(folder, frame)
|
||||
i += 1
|
||||
Binary file not shown.
Binary file not shown.
@@ -292,7 +292,7 @@ def sparse_to_tensor(L):
|
||||
return tf.SparseTensor(idx, data, shape)
|
||||
|
||||
|
||||
def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False, iteration=''):
|
||||
def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False, iteration='', return_iteration=False):
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, checkpoint, max_to_keep=1)
|
||||
if not disable_assert:
|
||||
@@ -306,7 +306,14 @@ def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False
|
||||
ckpt_ind = [i for i in range(len(manager.checkpoints)) if manager.checkpoints[i].endswith(name_base + '-' + iteration)][0]
|
||||
ckpt.restore(manager.checkpoints[ckpt_ind]).assert_existing_objects_matched()#.assert_consumed()
|
||||
else:
|
||||
ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()#.assert_consumed()
|
||||
if not return_iteration:
|
||||
ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()#.assert_consumed()
|
||||
else:
|
||||
ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()
|
||||
if return_iteration:
|
||||
iteration = manager.latest_checkpoint.split('-')[-1]
|
||||
return iteration
|
||||
|
||||
|
||||
|
||||
def get_summary_str(model):
|
||||
@@ -425,3 +432,24 @@ class Accumulator():
|
||||
|
||||
def reset(self):
|
||||
self.dict = dict()
|
||||
|
||||
|
||||
from stl import mesh as mesh_stl
|
||||
|
||||
def save_stl(file, mesh, faces):
|
||||
obj = mesh_stl.Mesh(np.zeros(faces.shape[0], dtype=mesh_stl.Mesh.dtype))
|
||||
for i, f in enumerate(faces):
|
||||
for j in range(3):
|
||||
obj.vectors[i][j] = mesh[f[j],:]
|
||||
|
||||
obj.save(file)
|
||||
|
||||
|
||||
def write_obj(file, vertices, faces, colors):
|
||||
with open(file, 'w') as file:
|
||||
for v, c in zip(vertices, colors):
|
||||
line = 'v ' + ' '.join([str(_) for _ in v]) + ' ' + ' '.join([str(_) for _ in c]) + '\n'
|
||||
file.write(line)
|
||||
for f in faces:
|
||||
line = 'f ' + ' '.join([str(_) for _ in f]) + '\n'
|
||||
file.write(line)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,7 +1,8 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.iccv.config_abl_skin_var import config
|
||||
from configs.iccv.config_abl_all_skirt_var import config
|
||||
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
@@ -479,7 +480,7 @@ def test():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
+77
-100
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_cvae_curv import config
|
||||
from configs.iccv.config_cvae_curv import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
@@ -30,7 +30,6 @@ import plotly.express as ex
|
||||
from plotly.subplots import make_subplots
|
||||
from PIL import Image
|
||||
|
||||
from tf_utils import check_tf_version, allow_memory_growth
|
||||
# from losses import collision_penalty as collision_penalty
|
||||
from losses import collision_loss as collision_penalty
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
@@ -38,15 +37,17 @@ from custom_utils.custom_utils import batch_to_dict
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.cvae_curv_model import CVAE
|
||||
from losses import kl_loss
|
||||
from custom_utils.custom_utils import Accumulator
|
||||
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb')
|
||||
run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
|
||||
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('cvae_curv.py')
|
||||
artifact.add_file('models/cvae_curv_model.py')
|
||||
artifact.add_file('models/cvae_model.py')
|
||||
artifact.add_file('dataset.py')
|
||||
artifact.add_file('losses.py')
|
||||
artifact.add_file('models/base.py')
|
||||
@@ -55,7 +56,9 @@ def init_wandb():
|
||||
artifact.add_file('convnext/models/convnext_tf.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
||||
for config_file_name in config_file_names:
|
||||
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
|
||||
file_name = '/'.join(config_file_name.split('.')) + '.py'
|
||||
if os.path.exists(file_name):
|
||||
artifact.add_file(file_name)
|
||||
run.log_artifact(artifact)
|
||||
|
||||
uv2mesh = UV2MeshLabel(res=256)
|
||||
@@ -154,7 +157,7 @@ def dice_dense(prediction, ground_truth, weight_map=None):
|
||||
def train_step(batch):
|
||||
with tf.GradientTape() as tape:
|
||||
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
|
||||
|
||||
ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
dice = dice_dense(pred, batch['uv_curvature'])
|
||||
@@ -186,7 +189,7 @@ def train_step(batch):
|
||||
|
||||
@tf.function
|
||||
def valid_step(batch):
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
loss = ce_uv_loss
|
||||
@@ -213,7 +216,7 @@ def valid_step(batch):
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
pred = np.argmax(pred.numpy(), axis=-1)
|
||||
mesh_pred = uv2mesh(pred)
|
||||
@@ -248,90 +251,54 @@ def plot_everything(batch: dict, type: str):
|
||||
|
||||
|
||||
|
||||
def sample(folder):
|
||||
load_model(model,folder)
|
||||
def sample():
|
||||
iteration = load_model(model, f'checkpoints/iccv/cvae_curv_3', return_iteration=True)
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
|
||||
uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
|
||||
nr_of_samples = 16
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
nr_of_samples = 4
|
||||
|
||||
# pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
|
||||
pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
|
||||
|
||||
ce_uv_loss = scce(tf.tile(batch['uv_curvature'][0][tf.newaxis,:,:], [nr_of_samples,1,1]), pred)
|
||||
|
||||
|
||||
mesh_pred = uv2mesh(pred).numpy().argmax(axis=-1)
|
||||
mesh_orig = batch['mesh_curvature'][0].numpy()
|
||||
|
||||
F = np.load("custom_utils/smpl_faces.npy")
|
||||
mask = batch['mesh_mask'].numpy().astype(bool)[0]
|
||||
_, faces = _maskFaces(np.ones((14475, 3)), F, mask)
|
||||
mesh_pred = [mesh_pred[i, mask] for i in range(mesh_pred.shape[0])]
|
||||
curv_color = [np.zeros((mesh_pred[i].shape[0], 3)) for i in range(len(mesh_pred))]
|
||||
mesh_orig = mesh_orig[mask] + 1
|
||||
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
||||
curv_color_gt = np.zeros((mesh_pred[0].shape[0], 3))
|
||||
for i in range(6+1):
|
||||
for j in range(len(mesh_pred)):
|
||||
curv_color[j][mesh_pred[j] == i, :] = colors[i]
|
||||
curv_color_gt[mesh_orig == i, :] = colors[i]
|
||||
|
||||
mesh_base = batch['mesh_posed'][0].numpy()[mask, :]
|
||||
meshes = []
|
||||
meshes.append(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=mesh_base[:,0],
|
||||
y=mesh_base[:,1],
|
||||
z=mesh_base[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=curv_color_gt,
|
||||
text='gt'
|
||||
))
|
||||
|
||||
for i in range(nr_of_samples):
|
||||
x = mesh_base.copy()
|
||||
x[:, 1] += 1 * (i + 1)
|
||||
meshes.append(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
x=x[:,0],
|
||||
y=x[:,1],
|
||||
z=x[:,2],
|
||||
# i, j and k give the vertices of triangles
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
vertexcolor=curv_color[i]
|
||||
))
|
||||
|
||||
|
||||
|
||||
|
||||
fig = go.Figure(data=meshes)
|
||||
|
||||
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1))
|
||||
fig.update_layout(
|
||||
scene = dict(
|
||||
xaxis = dict(nticks=4, range=[-1,1],),
|
||||
yaxis = dict(nticks=4, range=[-1,nr_of_samples + 1],),
|
||||
zaxis = dict(nticks=4, range=[-1,1],),)
|
||||
)
|
||||
|
||||
# fig.show()
|
||||
|
||||
pred = pred.numpy().argmax(-1)
|
||||
frames = [Image.fromarray(pred[i]/6*255) for i in range(pred.shape[0])]
|
||||
frames[0].save("temp/curv2.gif", format="GIF", append_images=frames[1:],
|
||||
save_all=True, duration=500, loop=0)
|
||||
for i in range(4):
|
||||
# pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
|
||||
# pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
|
||||
pred = model.sample(tf.tile(batch['uv_static'][i][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][i][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][i][None,:,:], [nr_of_samples,1,1]))
|
||||
# pred,_,_ = model([tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1])], training=False)
|
||||
# pred = model.call_sample([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
|
||||
orig_img = np.argmax(batch['uv_curvature'][i].numpy(), axis=-1)
|
||||
pred_img = np.argmax(pred.numpy(), axis=-1)
|
||||
img = np.concatenate((orig_img, np.concatenate(pred_img, axis=1)), axis=1)
|
||||
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
||||
color_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
|
||||
for i in range(7):
|
||||
color_img[img == i, :] = colors[i]
|
||||
ex.imshow(color_img).show()
|
||||
print()
|
||||
print()
|
||||
|
||||
def sample_2(batch):
|
||||
nr_of_samples = 4
|
||||
|
||||
# pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
|
||||
# pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
|
||||
# pred = model.sample(tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1]))
|
||||
pred,_,_ = model([tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]),tf.tile(batch['uv_curvature'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1])], training=False)
|
||||
# pred = model.call_sample([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
|
||||
orig_img = np.argmax(batch['uv_curvature'][0].numpy(), axis=-1)
|
||||
pred_img = np.argmax(pred.numpy(), axis=-1)
|
||||
img = np.concatenate((orig_img, np.concatenate(pred_img, axis=1)), axis=1)
|
||||
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
||||
color_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
|
||||
for i in range(7):
|
||||
color_img[img == i, :] = colors[i]
|
||||
ex.imshow(color_img).show()
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
@@ -369,6 +336,8 @@ if __name__ == '__main__':
|
||||
|
||||
model = CVAE(config)
|
||||
|
||||
sample()
|
||||
|
||||
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False)
|
||||
optimizer = tf.optimizers.Adam(learning_rate=config['lr']) #0.0001
|
||||
g_balancing_coeff = config['starting_balancing_coeff']
|
||||
@@ -376,7 +345,9 @@ if __name__ == '__main__':
|
||||
valid_iterator = iter(dataset_valid)
|
||||
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
|
||||
train_accumulator = Accumulator()
|
||||
valid_accumulator = Accumulator()
|
||||
|
||||
if config['pretrained_model'] != '':
|
||||
load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
|
||||
@@ -386,29 +357,35 @@ if __name__ == '__main__':
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
train_loss = train_step(batch)
|
||||
train_accumulator.update(train_loss)
|
||||
|
||||
data_valid = valid_iterator.get_next()
|
||||
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
|
||||
valid_loss = valid_step(data_valid)
|
||||
if step % 25 == 0:
|
||||
data_valid = valid_iterator.get_next()
|
||||
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
|
||||
valid_loss = valid_step(data_valid)
|
||||
train_accumulator.update(train_loss)
|
||||
|
||||
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
||||
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
|
||||
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
|
||||
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } }
|
||||
|
||||
wandb.log(to_log, step=step)
|
||||
train_accumulator.reset()
|
||||
valid_accumulator.reset()
|
||||
|
||||
if step % 10000 == 0:
|
||||
manager.save(checkpoint_number=step)
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
if step > 40000:
|
||||
g_balancing_coeff = g_balancing_coeff + 1 / 20000
|
||||
|
||||
to_log = { **to_log, **{ 'train/g_balancing_coeff': g_balancing_coeff } }
|
||||
|
||||
wandb.log(to_log, step = step)
|
||||
|
||||
print(step)
|
||||
|
||||
step += 1
|
||||
+184
-131
@@ -1,7 +1,8 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.iccv.config_abl_pyr_32_64_128 import config
|
||||
from configs.iccv.config_abl_pyr_32_64_128_256_512_fine import config
|
||||
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
@@ -38,12 +39,13 @@ else:
|
||||
from losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio, normal_loss
|
||||
# from losses import collision_penalty as collision_penalty
|
||||
from losses import collision_loss as collision_penalty
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model, save_stl
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.pyramid_model_optimized import PyramidNew as Pyramid
|
||||
from postprocessing import fix_collision
|
||||
from custom_utils.custom_utils import Accumulator
|
||||
from sample_pro_2 import postprocess
|
||||
|
||||
|
||||
def init_wandb():
|
||||
@@ -212,7 +214,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
|
||||
return fig
|
||||
|
||||
|
||||
# @tf.function
|
||||
@tf.function
|
||||
def train_step(batch):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
@@ -220,65 +222,70 @@ def train_step(batch):
|
||||
out_levels = model([levels_input, conds], training=True)
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
mesh_orig = batch['mesh_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
mesh_orig = batch['mesh_posed']
|
||||
|
||||
output = dict()
|
||||
reconstr_loss_total = 0
|
||||
kl_total = 0
|
||||
|
||||
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
i = -1
|
||||
res = config['pyramid']['resolutions'][-1]
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
# i = -1
|
||||
res = config['pyramid']['resolutions'][i] if i != len(config['pyramid']['resolutions']) - 1 else 512
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
if config['use_skinning']:
|
||||
uv_orig = batch['uv_unposed' + res_modifier]
|
||||
else:
|
||||
uv_orig = batch['uv_posed' + res_modifier]
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
# is the output already masked?
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
# is the output already masked?
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1) * batch['uv_mask' + res_modifier], axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
|
||||
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
|
||||
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
|
||||
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
|
||||
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
|
||||
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss' + res_modifier: n_loss,
|
||||
'train/euclid_error' + res_modifier: metric, 'train/l2_mesh_loss' + res_modifier: l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
@@ -294,64 +301,69 @@ def valid_step(batch):
|
||||
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
mesh_orig = batch['mesh_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
mesh_orig = batch['mesh_posed']
|
||||
|
||||
output = dict()
|
||||
reconstr_loss_total = 0
|
||||
kl_total = 0
|
||||
|
||||
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
i = -1
|
||||
res = config['pyramid']['resolutions'][-1]
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
# i = -1
|
||||
res = config['pyramid']['resolutions'][i] if i != len(config['pyramid']['resolutions']) - 1 else 512
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
if config['use_skinning']:
|
||||
uv_orig = batch['uv_unposed' + res_modifier]
|
||||
else:
|
||||
uv_orig = batch['uv_posed' + res_modifier]
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
# is the output already masked?
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
# is the output already masked?
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1) * batch['uv_mask' + res_modifier], axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
kl_total += kl
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
|
||||
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss' + res_modifier: n_loss,
|
||||
'valid/euclid_error' + res_modifier: metric, 'valid/l2_mesh_loss' + res_modifier: l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
@@ -417,6 +429,7 @@ def test_step(batch):
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
nr_to_plot = 4
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'].numpy(), batch['uv_unposed'].numpy()
|
||||
else:
|
||||
@@ -426,8 +439,8 @@ def plot_everything(batch: dict, type: str):
|
||||
output_levels = model(levels_input, training=False)
|
||||
|
||||
mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))]
|
||||
mesh_orig_posed = batch['mesh_posed'].numpy()
|
||||
body = batch['mesh_body_posed'].numpy()
|
||||
mesh_orig_posed = batch['mesh_posed'].numpy()[:nr_to_plot]
|
||||
body = batch['mesh_body_posed'].numpy()[:nr_to_plot]
|
||||
|
||||
orig_img = (uv_orig + 1) / 2
|
||||
uv_static_img = (batch['uv_static'].numpy() + 1) / 2
|
||||
@@ -436,10 +449,10 @@ def plot_everything(batch: dict, type: str):
|
||||
pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2)
|
||||
pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
|
||||
img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image(img[i]) for i in range(4)]
|
||||
imgs = [wandb.Image(img[i]) for i in range(nr_to_plot)]
|
||||
|
||||
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
mask = batch['mesh_mask'].numpy().astype(bool)
|
||||
mask = batch['mesh_mask'].numpy().astype(bool)[:nr_to_plot]
|
||||
_, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask)
|
||||
|
||||
if config['use_skinning']:
|
||||
@@ -448,30 +461,30 @@ def plot_everything(batch: dict, type: str):
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]).numpy()
|
||||
|
||||
|
||||
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(nr_to_plot)]
|
||||
|
||||
mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])]
|
||||
mesh_unposed_shaped_levels = [[] for _ in range(nr_to_plot)]
|
||||
for i in range(len(output_levels)):
|
||||
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])]
|
||||
for k in range(mask.shape[0]):
|
||||
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(nr_to_plot)]
|
||||
for k in range(nr_to_plot):
|
||||
mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k])
|
||||
|
||||
# full res version
|
||||
mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32)
|
||||
for i in range(mask.shape[0]):
|
||||
mesh_unposed_shaped_full = np.zeros((nr_to_plot, config['smpl_res'], 3), dtype=np.float32)
|
||||
for i in range(nr_to_plot):
|
||||
mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i]
|
||||
|
||||
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy()
|
||||
mesh_posed = smpl.run_garment(batch['pose_shape'][:nr_to_plot, :72], batch['pose_shape'][:nr_to_plot, 72:], mesh_unposed_shaped_full).numpy()
|
||||
else:
|
||||
mesh_posed = mesh_pred_levels[-1]
|
||||
gt = [mesh_orig[i][mask[i], :] for i in range(mesh_posed.shape[0])]
|
||||
mesh_unposed_shaped_levels = [[mesh_pred_levels[i][j, mask[j], :] for i in range(len(output_levels))] for j in range(mask.shape[0])]
|
||||
gt = [mesh_orig[i][mask[i], :] for i in range(nr_to_plot)]
|
||||
mesh_unposed_shaped_levels = [[mesh_pred_levels[i][j, mask[j], :] for i in range(len(output_levels))] for j in range(nr_to_plot)]
|
||||
|
||||
|
||||
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(nr_to_plot)]
|
||||
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
|
||||
|
||||
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])]
|
||||
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(nr_to_plot)]
|
||||
|
||||
if config['is_cape']:
|
||||
collision = mesh_posed
|
||||
@@ -487,11 +500,11 @@ def plot_everything(batch: dict, type: str):
|
||||
if config['is_cape']:
|
||||
figs_coll = dict()
|
||||
else:
|
||||
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(nr_to_plot)]
|
||||
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(mesh_posed))]
|
||||
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(nr_to_plot)]
|
||||
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))]
|
||||
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(nr_to_plot) }
|
||||
|
||||
return { **figs, **figs_coll, **{ type + '/images': imgs, } }
|
||||
|
||||
@@ -680,9 +693,10 @@ def test():
|
||||
|
||||
|
||||
def generate_results():
|
||||
load_model(model, 'checkpoints/' + config['name'])
|
||||
load_model(model, 'checkpoints/iccv/' + config['name'])
|
||||
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
|
||||
|
||||
dataset_test = factory.get_dataset(folder=config['test_dir'],
|
||||
dataset_test = factory.get_dataset(folder=config['train_dir'],
|
||||
batch_size=config['batch_size'],
|
||||
# batch_size=1,
|
||||
signature=config['signature_dynamic'],
|
||||
@@ -691,15 +705,16 @@ def generate_results():
|
||||
is_cape=config['is_cape'],
|
||||
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
|
||||
do_repeat=False,
|
||||
shuffle=False)
|
||||
static_dataset_test = StaticDataset(folder=config['test_dir'],
|
||||
shuffle=True)
|
||||
static_dataset_test = StaticDataset(folder=config['train_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
folder = os.path.join('/home/workspace/data/results/cape', config['name'])
|
||||
folder = os.path.join('examples/qualitative', config['name'])
|
||||
os.makedirs(folder, exist_ok=True)
|
||||
|
||||
|
||||
for batch in tqdm.tqdm(dataset_test):
|
||||
batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
@@ -716,21 +731,44 @@ def generate_results():
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
mesh_gt = batch['mesh_posed'].numpy()
|
||||
body = batch['mesh_body_posed'].numpy()
|
||||
|
||||
for i in range(batch['id'].shape[0]):
|
||||
mask = batch['mesh_mask'][i].numpy().astype(bool)
|
||||
id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
|
||||
unposed = mesh_pred[i].numpy()[mask, :]
|
||||
posed = mesh_pred_posed[i].numpy()[mask, :]
|
||||
np.save(os.path.join(folder, id + '_' + frame_nr + '_posed.npy'), posed)
|
||||
np.save(os.path.join(folder, id + '_' + frame_nr + '_unposed.npy'), unposed)
|
||||
_, faces_garment = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
|
||||
faces_garment = faces_garment.astype(np.int32)
|
||||
posed_unmasked = np.zeros((1, 14475, 3), np.float32)
|
||||
posed_unmasked[0, mask, :] = posed
|
||||
posed = postprocess(posed_unmasked, body[i], faces_garment, mask, do_smoothing=True)
|
||||
unposed_unmasked = np.zeros((1, 14475, 3), np.float32)
|
||||
unposed_unmasked[0, mask, :] = unposed
|
||||
unposed = postprocess(unposed_unmasked, body[i], faces_garment, mask, do_smoothing=True)
|
||||
save_stl(os.path.join(folder, id + '_' + frame_nr + '_posed.stl'), posed, faces_garment)
|
||||
save_stl(os.path.join(folder, id + '_' + frame_nr + '_unposed.stl'), unposed, faces_garment)
|
||||
save_stl(os.path.join(folder, id + '_' + frame_nr + '_gt.stl'), mesh_gt[i][mask, :], faces_garment)
|
||||
print(id + '_' + frame_nr)
|
||||
# np.save(os.path.join(folder, id + '_' + frame_nr + '_posed.npy'), posed)
|
||||
# np.save(os.path.join(folder, id + '_' + frame_nr + '_unposed.npy'), unposed)
|
||||
|
||||
pass
|
||||
|
||||
@tf.function
|
||||
def speed_step(levels_input, conds):
|
||||
def speed_step(levels_input, conds, pose_shape, mask):
|
||||
shape = pose_shape[:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
return model.sample([levels_input, conds], training=False)
|
||||
# pred = model.sample([levels_input, conds])
|
||||
out_levels = model([levels_input, conds], training=False)
|
||||
pred = out_levels[-1][0]
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * mask[:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(pose_shape[:, :72], pose_shape[:, 72:], mesh_pred_shaped)
|
||||
return mesh_pred_posed
|
||||
|
||||
|
||||
|
||||
@@ -760,8 +798,12 @@ if __name__ == '__main__':
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=config['dataset_in_memory'],
|
||||
is_cape=config['is_cape'],
|
||||
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None)
|
||||
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
|
||||
frames_file_skirt=config['frames_file_skirt'] if 'frames_file_skirt' in config.keys() else '',
|
||||
folder_skirt=config['train_dir_skirt'] if 'train_dir_skirt' in config.keys() else '',
|
||||
)
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
||||
folder_skirt=config['train_dir_skirt'] if 'train_dir_skirt' in config.keys() else '',
|
||||
signature=config['signature_static'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=True,
|
||||
@@ -772,8 +814,12 @@ if __name__ == '__main__':
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=config['dataset_in_memory'],
|
||||
is_cape=config['is_cape'],
|
||||
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None)
|
||||
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
|
||||
frames_file_skirt=config['frames_file_valid_skirt'] if 'frames_file_valid_skirt' in config.keys() else '',
|
||||
folder_skirt=config['valid_dir_skirt'] if 'valid_dir_skirt' in config.keys() else '',
|
||||
)
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
||||
folder_skirt=config['valid_dir_skirt'] if 'valid_dir_skirt' in config.keys() else '',
|
||||
signature=config['signature_static'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=True,
|
||||
@@ -793,6 +839,8 @@ if __name__ == '__main__':
|
||||
|
||||
# generate_results()
|
||||
|
||||
|
||||
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
|
||||
train_accumulator = Accumulator()
|
||||
@@ -803,33 +851,38 @@ if __name__ == '__main__':
|
||||
if config['pretrained_model'] != '':
|
||||
# assert len(config['pyramid']['resolutions']) == 1
|
||||
load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
|
||||
|
||||
if len(config['pyramid']['resolutions']) == 1:
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
else:
|
||||
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
if len(config['pyramid']['resolutions']) == 1:
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
else:
|
||||
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
|
||||
# load_model(model.levels[0], 'checkpoints/iccv/iccv_pyr_64_cape_f')
|
||||
|
||||
# load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
|
||||
# model.levels[0].upscale_enhancer.trainable = False
|
||||
|
||||
|
||||
for i in range(len(model.levels)):
|
||||
for i in range(len(model.levels)-1):
|
||||
load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
|
||||
model.levels[i].upscale_enhancer.trainable = False
|
||||
# if config['pyramid']['resolutions'][-1] != 512:
|
||||
# load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# model.upscale_enhancer.trainable = False
|
||||
|
||||
for i in range(len(model.levels)-1):
|
||||
model.levels[i].trainable = False
|
||||
# for i in range(len(model.levels)-1):
|
||||
# model.levels[i].trainable = False
|
||||
|
||||
# load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
|
||||
# load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])
|
||||
@@ -841,10 +894,10 @@ if __name__ == '__main__':
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
# levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
# start = time.perf_counter()
|
||||
# out = speed_step(levels_input, conds)
|
||||
# out = speed_step(levels_input, conds, batch['pose_shape'] ,batch['mesh_mask'])
|
||||
# t = time.perf_counter() - start
|
||||
# print(t)
|
||||
# times.append(t)
|
||||
@@ -889,4 +942,4 @@ if __name__ == '__main__':
|
||||
|
||||
step += 1
|
||||
# print('mean:')
|
||||
# print(np.mean(times[1:]))
|
||||
# print(np.mean(times[10:]))
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user