calcualte error second level

aaa_helpers.txt

@@ -1,209 +1,15 @@
+nohup python cvae_pyramid.py --name="lhunor_abl_pyr_32_no_curv_r1" > nohup/review/abl_pyr_32_no_curv_r1.out 2>&1 &
+nohup python auto_encoder_static.py --name="lhunor_ae_static_orig_10p_r1" > nohup/review/static_orig_10p_r1.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_ae_body_orig_10p_r1" > nohup/review/body_orig_10p_r1.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_32_64_skirt_no_curv_r1" > nohup/review/abl_pyr_32_64_skirt_no_curv_r1.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_no_curv_r1 > nohup/evaluate/abl_pyr_32_no_curv_r1.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_32_64_skirt_no_curv_r1 > nohup/evaluate/abl_32_64_skirt_no_curv_r1.out 2>&1 &
+nohup python calculate_error.py --name error_abl_pyr_32_no_curv_r1 > nohup/error/abl_pyr_32_no_curv_r1.out 2>&1 &
+nohup python calculate_error.py --name error_pyr_abl_32_64_skirt_no_curv_r1 > nohup/error/pyr_abl_32_64_skirt_no_curv_r1.out 2>&1 &
+
 one final convolutional layer for smoothing, on the final coordinates
-nohup python calculate_error.py --name error_al_all_150k > nohup/error/post_al_all_150k.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1" > nohup/iccv/encoders/static_iccv_1.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_skirt" > nohup/iccv/encoders/static_iccv_1_skirt.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_f" > nohup/iccv/encoders/static_iccv_1_cape_f.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_m" > nohup/iccv/encoders/static_iccv_1_cape_m.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_10p" > nohup/iccv/encoders/static_iccv_1_10p.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_25p" > nohup/iccv/encoders/static_iccv_1_25p.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1" > nohup/iccv/encoders/body_iccv_1.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv/encoders/body_iccv_1_skirt.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_cape_m" > nohup/iccv/encoders/body_iccv_1_cape_m.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_10p" > nohup/iccv/encoders/body_iccv_1_10p.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_25p" > nohup/iccv/encoders/body_iccv_1_25p.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1" > nohup/iccv/encoders/curv_iccv_1.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_skirt" > nohup/iccv/encoders/curv_iccv_1_skirt.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_f" > nohup/iccv/encoders/curv_iccv_1_cape_f.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_m" > nohup/iccv/encoders/curv_iccv_1_cape_m.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_10p" > nohup/iccv/encoders/curv_iccv_1_10p.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_25p" > nohup/iccv/encoders/curv_iccv_1_25p.out 2>&1 &
-nohup python cvae.py --name="lhunor_cvae_iccv_gnn_1" > nohup/iccv/cvae_iccv_gnn_1.out 2>&1 &
 
-nohup python cvae.py --name="lhunor_abl_all" > nohup/iccv/ablation/abl_all_3.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_gnn" > nohup/iccv/ablation/abl_all_gnn.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_gnn_2" > nohup/iccv/ablation/abl_all_gnn.out 2>&1 &
-
-nohup python cvae.py --name="lhunor_abl_all_cape_f" > nohup/iccv/ablation/abl_all_cape_f_2.out 2>&1 &
-
-nohup python cvae.py --name="lhunor_abl_all_skirt" > nohup/iccv/ablation/abl_all_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_skirt_var" > nohup/iccv/ablation/abl_all_skirt_var.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_skirt_var" > nohup/iccv/ablation/abl_all_skirt_var_2.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_curv" > nohup/iccv/ablation/abl_curv.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_curv_skirt" > nohup/iccv/ablation/abl_curv_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin" > nohup/iccv/ablation/abl_skin.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_skirt" > nohup/iccv/ablation/abl_skin_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_reg" > nohup/iccv/ablation/abl_reg.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_reg_skirt" > nohup/iccv/ablation/abl_reg_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_off" > nohup/iccv/ablation/abl_off.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_curv" > nohup/iccv/ablation/abl_skin_curv.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_curv_skirt" > nohup/iccv/ablation/abl_skin_curv_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_reg" > nohup/iccv/ablation/abl_skin_reg.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_reg_skirt" > nohup/iccv/ablation/abl_skin_reg_skirt.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_skin_var" > nohup/iccv/ablation/abl_skin_var.out 2>&1 &
-
-nohup python evaluate_ae.py --name abl_all > nohup/iccv/evaluate/abl_all.out 2>&1 &
-nohup python evaluate_ae.py --name abl_all_skirt > nohup/iccv/evaluate/abl_all_skirt.out 2>&1 &
-
-nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/iccv/ablation/cvae_curv_3.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32_2" > nohup/iccv/upscale/iccv_32_2.out 2>&1 &
-nohup python upscale_test.py --name="iccv_64_2" > nohup/iccv/upscale/iccv_64_2.out 2>&1 &
-nohup python upscale_test.py --name="iccv_128_2" > nohup/iccv/upscale/iccv_128_2.out 2>&1 &
-nohup python upscale_test.py --name="iccv_256_2" > nohup/iccv/upscale/iccv_256_2.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32_2_skirt" > nohup/iccv/upscale/iccv_32_2_skirt.out 2>&1 &
-nohup python upscale_test.py --name="iccv_64_2_skirt" > nohup/iccv/upscale/iccv_64_2_skirt.out 2>&1 &
-nohup python upscale_test.py --name="iccv_128_2_skirt" > nohup/iccv/upscale/iccv_128_2_skirt.out 2>&1 &
-nohup python upscale_test.py --name="iccv_256_2_skirt" > nohup/iccv/upscale/iccv_256_2_skirt.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32_2_cape_f" > nohup/iccv/upscale/iccv_32_2_cape_f_real.out 2>&1 &
-nohup python upscale_test.py --name="iccv_64_2_cape_f" > nohup/iccv/upscale/iccv_64_2_cape_f.out 2>&1 &
-nohup python upscale_test.py --name="iccv_128_2_cape_f" > nohup/iccv/upscale/iccv_128_2_cape_f.out 2>&1 &
-nohup python upscale_test.py --name="iccv_256_2_cape_f" > nohup/iccv/upscale/iccv_256_2_cape_f.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32_2_cape_m" > nohup/iccv/upscale/iccv_32_2_cape_m.out 2>&1 &
-nohup python upscale_test.py --name="iccv_64_2_cape_m" > nohup/iccv/upscale/iccv_64_2_cape_m.out 2>&1 &
-nohup python upscale_test.py --name="iccv_128_2_cape_m" > nohup/iccv/upscale/iccv_128_2_cape_m.out 2>&1 &
-nohup python upscale_test.py --name="iccv_256_2_cape_m" > nohup/iccv/upscale/iccv_256_2_cape_m.out 2>&1 &
-
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32" > nohup/iccv/pyr/iccv_pyr_32.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_var" > nohup/iccv/pyr/iccv_pyr_32_var.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt" > nohup/iccv/pyr/iccv_pyr_32_skirt_2.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skin" > nohup/iccv/pyr/iccv_pyr_32_skin.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skin_skirt" > nohup/iccv/pyr/iccv_pyr_32_skin_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_curv" > nohup/iccv/pyr/iccv_pyr_32_curv.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_curv_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg" > nohup/iccv/pyr/iccv_pyr_32_reg.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg_skirt" > nohup/iccv/pyr/iccv_pyr_32_reg_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_10p" > nohup/iccv/pyr/iccv_pyr_32_10p.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_25p" > nohup/iccv/pyr/iccv_pyr_32_25p.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_jumpsuit.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_trousers" > nohup/iccv/pyr/iccv_pyr_32_trousers.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_top" > nohup/iccv/pyr/iccv_pyr_32_top.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_tshirt" > nohup/iccv/pyr/iccv_pyr_32_tshirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_skirt_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_skirt_dress.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_batch" > nohup/iccv/pyr/iccv_pyr_32_skirt_batch.out 2>&1 &
-
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64" > nohup/iccv/pyr/iccv_pyr_32_64.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skin" > nohup/iccv/pyr/iccv_pyr_32_64_skin.out 2>&1 &
-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 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_curv" > nohup/iccv/pyr/iccv_pyr_32_64_curv.out 2>&1 &
-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 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg" > nohup/iccv/pyr/iccv_pyr_32_64_reg.out 2>&1 &
-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 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_10p" > nohup/iccv/pyr/iccv_pyr_32_64_10p.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_25p" > nohup/iccv/pyr/iccv_pyr_32_64_25p.out 2>&1 &
-
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f" > nohup/iccv/pyr/iccv_pyr_64_cape_f.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_m" > nohup/iccv/pyr/iccv_pyr_64_cape_m.out 2>&1 &
-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 &
-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 &
-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 &
-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 &
-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 &
-
-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 &
-
-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 &
-
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
-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 &
-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 &
-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 &
-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 &
-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 &
-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 &
-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 &
-
-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 &
-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 &
-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 &
-
-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 &
-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 &
-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 &
-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 &
-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 &
-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 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_seq_2_fine" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_seq_2_fine.out 2>&1 &
-
-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 &
-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 &
-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 &
-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 &
-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 &
-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 &
-
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32 > nohup/iccv/evaluate/iccv_pyr_32.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skirt > nohup/iccv/evaluate/iccv_pyr_32_skirt.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skin > nohup/iccv/evaluate/iccv_pyr_32_skin.out 2>&1 &
-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 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_reg > nohup/iccv/evaluate/iccv_pyr_32_reg.out 2>&1 &
-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 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_curv > nohup/iccv/evaluate/iccv_pyr_32_curv.out 2>&1 &
-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 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_10p > nohup/iccv/evaluate/iccv_pyr_32_10p.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_25p > nohup/iccv/evaluate/iccv_pyr_32_25p.out 2>&1 &
-
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64 > nohup/iccv/evaluate/iccv_pyr_32_64.out 2>&1 &
-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 &
-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 &
-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 &
-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 &
-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 &
-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 &
-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 > 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_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_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_seq > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_seq.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 &
-
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-skirts!!!!iccv_pyr_32_curv_skirt
+skirts!!!!
 pyramid next week
 
 basic final, basic final skirts,

auto_encoder_body.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_body_25p import config
+from configs.config_ae_body import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -30,12 +30,11 @@ from tf_utils import check_tf_version, allow_memory_growth
 from losses import edge_loss, uv_mask_loss
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict, plot_3d
-from models.cond_encoder import AutoEncoderNew as AutoEncoder
-from custom_utils.custom_utils import Accumulator
+from models.cond_encoder import AutoEncoder
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('auto_encoder_body.py')
     artifact.add_file('losses.py')
@@ -45,9 +44,7 @@ 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:
-        file_name = '/'.join(config_file_name.split('.')) + '.py'
-        if os.path.exists(file_name):
-            artifact.add_file(file_name)
+        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_smpl_orig=config['is_cape'], is_skirt=config['is_skirt'])
@@ -127,27 +124,20 @@ def train():
     for batch in dataset_train:
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
         train_loss = train_step(batch)
-        train_accumulator.update(train_loss)
 
-        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)
-            valid_accumulator.update(valid_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 % 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 = { **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 == 0:
+            to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
 
-                
-            wandb.log(to_log, step=step)
-            train_accumulator.reset()
-            valid_accumulator.reset() 
+        wandb.log(to_log, step=step)  
             
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)
@@ -160,6 +150,7 @@ def train():
 
 if __name__ == '__main__':
     init_wandb()
+    allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -180,7 +171,6 @@ if __name__ == '__main__':
                                         signature=config['signature_dynamic'],
                                         resolutions=[config['resolution']], 
                                         in_memory=config['dataset_in_memory'],
-                                        use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
                                         is_cape=config['is_cape'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
@@ -194,7 +184,6 @@ if __name__ == '__main__':
                                         signature=config['signature_dynamic'], 
                                         resolutions=[config['resolution']], 
                                         in_memory=config['dataset_in_memory'], 
-                                        use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
                                         is_cape=config['is_cape'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
@@ -205,15 +194,13 @@ if __name__ == '__main__':
 
     model = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
-    train_accumulator = Accumulator()
-    valid_accumulator = Accumulator()
 
     # load_model(model.encoder, 'checkpoints/body_1_skirt_2_enc')
     # load_model(model.decoder, 'checkpoints/body_1_skirt_2_dec')
 
     ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
     ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
 
     train()

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_curv_25p import config
+from configs.config_ae_curv_post import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -32,13 +32,12 @@ from tf_utils import check_tf_version, allow_memory_growth
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict
 from models.cond_encoder import AutoEncoderNew as AutoEncoder
-from custom_utils.custom_utils import Accumulator
 
 
 testing = False
 def init_wandb():
     if not testing:
-        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
+        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('losses.py')
@@ -48,9 +47,7 @@ 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:
-            file_name = '/'.join(config_file_name.split('.')) + '.py'
-            if os.path.exists(file_name):
-                artifact.add_file(file_name)
+            artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
         run.log_artifact(artifact)
 
 uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -167,27 +164,21 @@ def train():
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
 
         train_loss = train_step(batch)
-        train_accumulator.update(train_loss)
 
-        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)
-            valid_accumulator.update(valid_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 % 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)) } }
-
-                
-            wandb.log(to_log, step=step)
-            train_accumulator.reset()
-            valid_accumulator.reset()
+        to_log = { **train_loss, **valid_loss }
+        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 == 0:
+            to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+ 
+        wandb.log(to_log, step=step)  
+        print(step)
 
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)
@@ -240,7 +231,7 @@ if __name__ == '__main__':
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
                                          in_memory=True, 
-                                         is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'])
 
     dataset_valid = factory.get_dataset(folder=config['valid_dir'],
                                         batch_size=config['batch_size'], 
@@ -254,17 +245,15 @@ if __name__ == '__main__':
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
                                          in_memory=True, 
-                                         is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'])
 
     model = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
-    train_accumulator = Accumulator()
-    valid_accumulator = Accumulator()
 
     ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
     ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
 
     if testing:
         test()

auto_encoder_static.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_static_25p import config
+from configs.config_ae_static import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -33,12 +33,11 @@ else:
 from tf_utils import check_tf_version, allow_memory_growth
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict, plot_3d
-from models.cond_encoder import AutoEncoderNew as AutoEncoder
-from custom_utils.custom_utils import Accumulator
+from models.cond_encoder import AutoEncoder
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('auto_encoder_static.py')
     artifact.add_file('losses.py')
@@ -48,9 +47,7 @@ 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:
-        file_name = '/'.join(config_file_name.split('.')) + '.py'
-        if os.path.exists(file_name):
-            artifact.add_file(file_name)
+        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -128,33 +125,27 @@ def train():
     for batch in dataset_train:
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
         train_loss = train_step(batch)
-        train_accumulator.update(train_loss)
 
-        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)
-            valid_accumulator.update(valid_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 % 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 = dict({ **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 == 0:
+            to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
 
-                
-            wandb.log(to_log, step=step)
-            train_accumulator.reset()
-            valid_accumulator.reset()
+            
+        wandb.log(to_log, step=step)
 
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)
             manager_dec.save(checkpoint_number=step)
 
-            print(step)
+        print(step)
 
         step += 1
 
@@ -214,7 +205,6 @@ if __name__ == '__main__':
                                         signature=config['signature_dynamic'],
                                         resolutions=[config['resolution']], 
                                         in_memory=config['dataset_in_memory'],
-                                        use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
                                         is_cape=config['is_cape'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
@@ -228,7 +218,6 @@ if __name__ == '__main__':
                                         signature=config['signature_dynamic'], 
                                         resolutions=[config['resolution']], 
                                         in_memory=config['dataset_in_memory'], 
-                                        use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
                                         is_cape=config['is_cape'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
@@ -240,17 +229,14 @@ if __name__ == '__main__':
     model : AutoEncoder = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
 
-    train_accumulator = Accumulator()
-    valid_accumulator = Accumulator()
-
     if config['pretrained_model'] != '':
         load_model(model.encoder, config['pretrained_model'] + '_enc')
         load_model(model.decoder, config['pretrained_model'] + '_dec')
 
     ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
     ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
 
     # test()
     train()

calculate_error.py

@@ -1,37 +1,37 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_seq import config
+from configs.config_abl_pyr_32_64_skirt_r1 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
-from tqdm import tqdm
+import tqdm
 import random
 import numpy as np
 import plotly.graph_objects as go
 import plotly.express as ex
-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
+
+# if config['is_skirt']:
+#     from losses import edge_loss_skirt as edge_loss
+# else:
+#     from losses import edge_loss
 
 
 
@@ -53,55 +53,40 @@ 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 = '200000'
+    iteration = '50000'
 
     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/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]
+    output_folder = '/home/workspace/data/results/' + checkpoint_name + '-' + iteration
 
 
-    # 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 }
 
-    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'
+    masks_folder = '/home/workspace/data/dataset/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]
-
-    # 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/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
+    orig_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
 
     errors = dict()
     if config['is_skirt']:
@@ -109,32 +94,12 @@ if __name__ == '__main__':
     else:
         per_garment_errors = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
 
-    for frame in tqdm(frames):
+    for frame in tqdm.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'))
-        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')
-            
+        metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
         errors[id + '_' + frame_nr] = metric
         per_garment_errors[garment_type_table[id]].append(metric)
 
@@ -142,7 +107,7 @@ if __name__ == '__main__':
     for key in per_garment_errors.keys():
         print(key + ': ' + str(np.mean(per_garment_errors[key])))
 
-    with open(f'/home/workspace/data/results/errors/iccv/{checkpoint_name}-{iteration}-{error_type}.csv', mode='w') as f:
+    with open('/home/workspace/data/results/errors/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
         file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
 
         for key in sorted(errors.keys()):

compare_cape.py

@@ -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, save_stl
+from custom_utils.custom_utils import _maskFaces
 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/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')
+    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')
 
     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'
@@ -238,10 +238,9 @@ 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 = 'female'
+    gender = 'male'
 
     if gender == 'male':
         folder_cape = folder_m
@@ -263,18 +262,11 @@ if __name__ == '__main__':
     all_errors_1 = []
     all_errors_2 = []
 
-    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()))
+    # for key in tqdm.tqdm(errors_cape.keys()):
+    count = 0
+    while True:
+        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
@@ -292,13 +284,10 @@ 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_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)
+        fig = plot_fixed_all(gt, cape, ours, errors_cape[key], errors[key], faces, colors)
+        fig.write_image('temp/cape/' + key + '.png')
 
-        # print(count)
-        # count += 1
+        print(count)
+        count += 1
 
     print()

compare_sampling.py

@@ -1,65 +0,0 @@
-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)
-
-

configs/config_abl_all_post.py

@@ -19,7 +19,7 @@ config['only_curvatures'] =           False
 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['gpu'] = '1'
 config['name'] = 'al_all_post'
 config['notes'] = ''
 

configs/config_abl_pyr_32.py

@@ -1,7 +1,7 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32'
+config['name'] = 'pyr_abl_32_r1'
 config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE

configs/config_abl_pyr_32_64_skirt_r1.py

@@ -1,9 +1,9 @@
 config = dict()
 
 config['gpu'] = '1'
-config['name'] = 'iccv_pyr_32_64_skirt'
-config['notes'] = ''
-config['tags'] = ['pyr', 'iccv']
+config['name'] = 'pyr_abl_32_64_skirt_no_curv_r1'
+config['notes'] = 'no curv, 10p data'
+config['tags'] = ['review_1']
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -19,36 +19,41 @@ 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['use_curvature'] =             False
+
+config['pyramid'] = dict()
+config['pyramid']['resolutions'] = [32, 64]
+config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_32_skirt_no_curv_r1'
+config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_skirt'
 
 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_3_175k'
-config['pyramid']['final_upscale_enhancer'] = ''
-
-config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
 config['is_skirt'] = True
 config['is_cape'] = False
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
 
-config['dataset_in_memory'] = False
+config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
+config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
+
+config['dataset_in_memory'] = True
 config['resolution'] = None
+config['latent_dim'] = 128
 
 config['smpl_res'] = 14475
 if config['is_cape']:
   config['smpl_res'] = 3638
 
 config['lr'] = 0.0001
-config['starting_balancing_coeff'] = 0.001
+config['starting_balancing_coeff'] = 0.0001
 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,),
@@ -65,8 +70,9 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
                               }
 
 config['pyramid']['is_label'] = False
-config['pyramid']['add_noise'] = False
-config['pyramid']['noise_amount'] = 0.0
+config['pyramid']['latent_dim'] = config['latent_dim']
+config['pyramid']['add_noise'] = True
+config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 
@@ -80,21 +86,18 @@ for res in config['pyramid']['resolutions']:
       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']['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['latent_dim'] = config['latent_dim']
     level['kernel_size'] = 7
     level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
+    level['encoder_dims'] = [32, 64, 128, 256]
     level['decoder_input'] = (16, 8, 80)
     level['decoder_depths'] = [3, 3, 9, 3]
     level['decoder_dims'] = [256, 128, 64, 16]
@@ -104,86 +107,83 @@ for res in config['pyramid']['resolutions']:
   elif res == 256:
     level = dict()
     level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
+    level['latent_dim'] = config['latent_dim']
+    level['kernel_size'] = 7
     level['encoder_depths'] = [9, 9, 9, 9]
-    level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
+    level['encoder_dims'] = [32, 64, 128, 256]
     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'] = ''#'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['latent_dim'] = config['latent_dim']
     level['kernel_size'] = 5
     level['encoder_depths'] = [16, 9, 9, 9]
-    level['encoder_dims'] = [64, 128, 128, 64]
+    level['encoder_dims'] = [64, 128, 128, 256]
     level['decoder_input'] = (4, 2, 128)
     level['decoder_depths'] = [16, 9, 9, 9]
-    level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
+    level['decoder_dims'] = [256, 128, 128, 64]
     level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
+    level['upscale_enhancer'] = ''#'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['latent_dim'] = config['latent_dim']
+    level['kernel_size'] = 5
+    level['encoder_depths'] = [9, 9, 9]
+    level['encoder_dims'] = [64, 128, 256]
+    level['decoder_input'] = (4, 2, 256)
+    level['decoder_depths'] = [9, 9, 9]
+    level['decoder_dims'] = [256, 128, 64]
     level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
-    level['upscale_enhancer_freeze'] = True
+    level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
+    level['upscale_enhancer_freeze'] = False
     config['pyramid']['levels'].append(level)
   elif res == 32:
     level = dict()
     level['resolution'] = 32
-    level['latent_dim'] = 64 * config['bottleneck_scale']
+    level['latent_dim'] = config['latent_dim']
     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['encoder_depths'] = [9, 9]
+    level['encoder_dims'] = [256, 256]
+    level['decoder_input'] = (4, 2, 256)
+    level['decoder_depths'] = [9, 9]
+    level['decoder_dims'] = [256, 256]
     level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
+    level['upscale_enhancer'] = 'checkpoints/32_512_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'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
 
 config['cond_ae_body'] = dict()
-config['cond_ae_body']['is_label'] = False
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
 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'
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_pyr_32_skirt.py

@@ -1,8 +1,9 @@
 config = dict()
 
-config['gpu'] = '3'
-config['name'] = 'pyr_abl_32_skirt_2'
-config['notes'] = 'continued from pyr_abl_32_skirt-180k'
+config['gpu'] = '4'
+config['name'] = 'pyr_abl_32_skirt_no_curv_r1'
+config['notes'] = 'no curv, 10p data'
+config['tags'] = ['review_1']
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -18,24 +19,30 @@ 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_curvature'] =             False
 
 config['pyramid'] = dict()
 config['pyramid']['resolutions'] = [32]
 config['pyramid']['pretrained_pyramid'] = ''
 config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
 
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
+config['pretrained_model'] = ''
+config['pretrained_model_iteration'] = ''
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
 config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
 config['is_skirt'] = True
 config['is_cape'] = False
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
 
 config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
 config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
 config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
 
-config['dataset_in_memory'] = False
+config['dataset_in_memory'] = True
 config['resolution'] = None
 config['latent_dim'] = 128
 
@@ -65,6 +72,7 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
 config['pyramid']['is_label'] = False
 config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
+config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 

configs/config_ae_body.py

@@ -1,8 +1,9 @@
 from configs.config_base import config
 
-config['gpu'] = '2'
+config['gpu'] = '1'
 config['lr'] = 0.0001
-config['name'] = 'body_1_skirt_2_2'
+config['name'] = 'body_orig_10p_r1'
+config['tags'] = ['review_1']
 
 config['notes'] = 'convnext + convnext, add dense with tanh at the end of reverse convnext, | \
     flattened bottleneck with 128 latent_dim\ remove global orientation | \
@@ -15,7 +16,7 @@ config['notes'] = 'convnext + convnext, add dense with tanh at the end of revers
     continued from body_1_skirt_2 | \
     '
 
-config['dataset_in_memory'] = False
+config['dataset_in_memory'] = True
 config['batch_size'] = 32
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (),

configs/config_ae_static.py

@@ -0,0 +1,23 @@
+from configs.config_base import config
+
+config['gpu'] = '3'
+config['lr'] = 0.0005
+config['name'] = 'static_orig_10p_r1'
+config['tags'] = ['review_1']
+config['notes'] = 'convnext + convnext, add dense with tanh at the end of reverse convnext, | \
+    flattened latent dim to 128 | \
+    train on 100 garments | \
+    deeper model | \
+    larger batch size | \
+    ' 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (), }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                         'mesh_static': (config['smpl_res'], 3,),
+                         'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }

configs/config_base.py

@@ -5,17 +5,19 @@ config['batch_size'] = 4
 # 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/train'
-config['valid_dir'] = '/home/workspace/local-data/valid'
-config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready_skirt_2'
 # 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'
 config['use_filled'] = False
 config['resolution'] = 512
 config['is_skirt'] = False
-config['dataset_in_memory'] = False
+config['dataset_in_memory'] = True
 config['is_cape'] = False
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
 
 config['smpl_res'] = 14475
 if config['is_cape']: