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@@ -1,49 +0,0 @@
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# stylegan2-tf-2.x
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* This is tensorflow 2.0 based keras subclassing reimplementation of official [StyleGAN2 Repo](https://github.com/NVlabs/stylegan2)
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## Inference from official weights
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* Please check [how_to_extract_official_weights.md](./how_to_extract_official_weights.md)
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| Official repo result | Official weight copied result from this repo |
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| :---: | :---: |
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| ![official_result] | ![restored_result] |
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## Training on 256x256
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* batch size 32
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* around 300k steps
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| d_loss | g_loss |
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| :---: | :---: |
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| ![256x256_d_loss]| ![256x256_g_loss]|
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* result samples
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| | | |
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| :---: | :---: | :---: |
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| ![256x256_result_0]| ![256x256_result_1]| ![256x256_result_2]|
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## Using custom data
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* Create dataset(tf.data.Dataset) loader which returns
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* shape: `[None, 3, res, res]` where `res` is desired image resolution
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* image value range: `[-1, +1]`
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## Etc
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### Using with pycharm
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* If .bashrc file's paths don't work in pycharm environment (e.g. `nvcc -h`), open pycharm with following
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```bash
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# move to pycharm installed location (location may vary)
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moono@moono-ubuntu:~$ cd .local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin/
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# launch pycharm with .bashrc
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moono@moono-ubuntu:~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin$ bash pycharm.sh
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```
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[loss_tensorboard]: assets/tf-keras-stylegan2-loss.PNG
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[generation_tensorboard]: assets/tf-keras-stylegan2-fake-images.PNG
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[official_result]: assets/seed6600-official.png
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[restored_result]: assets/seed6600-restored.png
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[256x256_d_loss]: assets/d_loss_256x256.png
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[256x256_g_loss]: assets/g_loss_256x256.png
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[256x256_result_0]: assets/out_256x256_0.png
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[256x256_result_1]: assets/out_256x256_1.png
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[256x256_result_2]: assets/out_256x256_2.png
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+221
@@ -0,0 +1,221 @@
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nohup python cvae_pyramid.py --name="lhunor_abl_pyr_32_no_curv_r1" > nohup/review/abl_pyr_32_no_curv_r1.out 2>&1 &
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nohup python auto_encoder_static.py --name="lhunor_ae_static_orig_10p_r1" > nohup/review/static_orig_10p_r1.out 2>&1 &
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nohup python auto_encoder_body.py --name="lhunor_ae_body_orig_10p_r1" > nohup/review/body_orig_10p_r1.out 2>&1 &
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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 &
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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 &
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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 &
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nohup python calculate_error.py --name error_abl_pyr_32_no_curv_r1 > nohup/error/abl_pyr_32_no_curv_r1.out 2>&1 &
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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 &
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one final convolutional layer for smoothing, on the final coordinates
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skirts!!!!
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pyramid next week
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basic final, basic final skirts,
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basic pyramid
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removing curvatures test in ablation
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nohup python cvae_low.py --name="lhunor_cvae_low_4_3_skirt_d32" > nohup/nohup_cvae_low_4_3_skirt_d32.out 2>&1 &
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nohup python auto_encoder_body.py --name="lhunor_ae_body_post_2" > nohup/post/ae_body_2.out 2>&1 &
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nohup python auto_encoder_static.py --name="lhunor_ae_static_post_4" > nohup/post/ae_static_4.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_post_1" > nohup/post/ae_curv_1.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_abl_pyr_64_128_3" > nohup/post/abl_pyr_64_128_3.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_abl_pyr_32_64" > nohup/post/abl_pyr_32_64.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_cvae_pyr_3_32_2_64_skirt_test_60k" > nohup/cvae_pyr_3_32_2_64_skirt_test_60k.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32" > nohup/ablation/pyr_abl_32.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_2" > nohup/ablation/pyr_abl_32_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skin" > nohup/ablation/pyr_abl_32_skin.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skin_offset" > nohup/ablation/pyr_abl_32_skin_offset.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skirt" > nohup/ablation/pyr_abl_32_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_extra_loss_skirt" > nohup/ablation/pyr_abl_32_extra_loss_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skirt_2" > nohup/ablation/pyr_abl_32_skirt_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64" > nohup/ablation/pyr_abl_32_64.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_2" > nohup/ablation/pyr_abl_32_64_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_skirt" > nohup/ablation/pyr_abl_32_64_skirt.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_skirt_2" > nohup/ablation/pyr_abl_32_64_skirt_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_m" > nohup/ablation/pyr_abl_64_cape_m.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_f" > nohup/ablation/pyr_abl_64_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_f_2" > nohup/ablation/pyr_abl_64_cape_f_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_cape_f" > nohup/ablation/pyr_abl_64_128_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_cape_m" > nohup/ablation/pyr_abl_64_128_cape_m.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_256_cape_f_2" > nohup/ablation/pyr_abl_64_128_256_cape_f_2.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_256_512_cape_f" > nohup/ablation/pyr_abl_64_128_256_512_cape_f.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_256_512_cape_f_2" > nohup/ablation/pyr_abl_64_128_256_512_cape_f_2.out 2>&1 &
|
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nohup python upscale_test.py --name="lhunor_256_512_skirt" > nohup/up_256_512_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_cvae_v2_7_4_3" > nohup/cvae_v2_7_4_3.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_extra_loss" > nohup/ablation/abl_extra_loss.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_extra_loss_skirt" > nohup/ablation/abl_extra_loss_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_extra_loss_skirt_2" > nohup/ablation/abl_extra_loss_skirt_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_curv" > nohup/ablation/abl_curv.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_curv_skirt" > nohup/ablation/abl_curv_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_abs" > nohup/ablation/abl_abs.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_abs_skirt" > nohup/ablation/abl_abs_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin" > nohup/ablation/abl_skin.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_norm" > nohup/ablation/abl_skin_norm.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_skirt" > nohup/ablation/abl_skin_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all" > nohup/ablation/abl_all.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_skirt" > nohup/ablation/abl_all_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_orient" > nohup/ablation/abl_orient.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_orient_skirt" > nohup/ablation/abl_orient_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_one_hot_skirt" > nohup/ablation/abl_one_hot_skirt.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_norm" > nohup/ablation/abl_norm.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_10p" > nohup/ablation/abl_10p.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_25p" > nohup/ablation/abl_25p.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_10p_skin" > nohup/ablation/abl_10p_skin.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_25p_skin" > nohup/ablation/abl_25p_skin.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_50p_skin" > nohup/ablation/abl_50p_skin.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_var" > nohup/ablation/abl_skin_var.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_var_2" > nohup/ablation/abl_skin_var_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_var_3" > nohup/ablation/abl_skin_var_3.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_skin_var_4" > nohup/ablation/abl_skin_var_4.out 2>&1 &
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|
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nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/ablation/cvae_curv.out 2>&1 &
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nohup python cvae_curv.py --name="lhunor_cvae_curv_2" > nohup/ablation/cvae_curv_2.out 2>&1 &
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|
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|
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abl_2_pyr_32_64_128
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abl_2_pyr_64_cape_f_03223_SS
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abl_2_pyr_32_64_128_256_512
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nohup python cvae.py --name="lhunor_cape_f_03223_SL_2" > nohup/ablation/cape_f_03223_SL_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_cape_f_03223_SS_2" > nohup/ablation/cape_f_03223_SS_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_post" > nohup/post/abl_all_post.out 2>&1 &
|
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|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32" > nohup/ablation_2/abl_2_pyr_32.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skin" > nohup/ablation_2/abl_2_pyr_32_skin.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skin_2" > nohup/ablation_2/abl_2_pyr_32_skin_2.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skin_norm" > nohup/ablation_2/abl_2_pyr_32_skin_norm.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64" > nohup/ablation_2/abl_2_pyr_32_64.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_2" > nohup/ablation_2/abl_2_pyr_32_64_2.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128" > nohup/ablation_2/abl_2_pyr_32_64_128.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_256" > nohup/ablation_2/abl_2_pyr_32_64_128_256.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_256_512" > nohup/ablation_2/abl_2_pyr_32_64_128_256_512.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_256_512_norm" > nohup/ablation_2/abl_2_pyr_32_64_128_256_512_norm.out 2>&1 &
|
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|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skirt" > nohup/ablation_2/abl_2_pyr_32_skirt.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skirt_extra_loss" > nohup/ablation_2/abl_2_pyr_32_skirt_extra_loss.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_skirt" > nohup/ablation_2/abl_2_pyr_32_64_skirt.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_skirt" > nohup/ablation_2/abl_2_pyr_32_64_128_skirt.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_256_skirt" > nohup/ablation_2/abl_2_pyr_32_64_128_256_skirt.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64_128_256_512_skirt" > nohup/ablation_2/abl_2_pyr_32_64_128_256_512_skirt.out 2>&1 &
|
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|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_cape_f" > nohup/ablation_2/abl_2_pyr_64_cape_f.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_cape_f.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_256_cape_f.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_512_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_256_512_cape_f.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_cape_m" > nohup/ablation_2/abl_2_pyr_64_cape_m.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_cape_m" > nohup/ablation_2/abl_2_pyr_64_128_cape_m.out 2>&1 &
|
||||
nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_cape_m" > nohup/ablation_2/abl_2_pyr_64_128_256_cape_m.out 2>&1 &
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_512_cape_m" > nohup/ablation_2/abl_2_pyr_64_128_256_512_cape_m.out 2>&1 &
|
||||
|
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nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_cape_m_00032_SL" > nohup/ablation_2/abl_2_pyr_64_128_256_cape_m_00032_SL.out 2>&1 &
|
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|
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abl_2_all_skirt
|
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abl_2_pyr_64_cape_m_00032_SL
|
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|
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python convert_to_uv.py -f '/home/workspace/data/dataset/validation' -s 0 -e 150 -n "lhunor"
|
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python convert_to_uv.py -f '/home/workspace/data/dataset/skirt_test' -s 0 -e 1 -n "lhunor"
|
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|
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cp configs/config_abl_all.py configs/config_abl_abs.py
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|
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|
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python merge_data.py -fi '/home/workspace/data/dataset/test' -fo '/home/workspace/data/dataset/test_ready' -s 0 -e 1500
|
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python merge_data.py -fi '/home/workspace/data/dataset/valid_skirt' -fo '/home/workspace/local-data/valid_skirt' -s 0 -e 1500
|
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|
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nohup python upscale_test.py > nohup/u.out 2>&1 &
|
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nohup python unpose_one_hot.py > nohup/unpose_one_hot_train_skirt.out 2>&1 &
|
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nohup python preprocessing.py > nohup/unshape_one_hot_test_skirt.out 2>&1 &
|
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nohup python dataset.py > nohup/avg_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae_support.py > nohup/evaluate_ae_support.out 2>&1 &
|
||||
nohup python evaluate_cape.py --name evaluate_abl_pyr_64_128_256_512_cape_f > nohup/evaluate_abl_pyr_64_128_256_512_cape_f.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_64_110k > nohup/evaluate_ae_abl_pyr_32_64_110k.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_64_2_50k > nohup/evaluate_ae_abl_pyr_32_64_2_50k.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_skirt > nohup/evaluate_ae_abl_pyr_32_skirt.out 2>&1 &
|
||||
nohup python evaluate_ae.py --name abl_norm_skirt_70k > nohup/evaluate/abl_norm_skirt_70k.out 2>&1 &
|
||||
nohup python calculate_error.py --name error_abl_2_pyr_32_64_128_skirt > nohup/error_ae_abl_skin_skirt_70k.out 2>&1 &
|
||||
|
||||
nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_100k > nohup/evaluate/abl_2_pyr_32_64_100k.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_80k > nohup/evaluate/abl_2_pyr_32_64_128_80k.out 2>&1 &
|
||||
nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_256_512_skirt_50k > nohup/evaluate/abl_2_pyr_32_64_128_256_512_skirt_50k.out 2>&1 &
|
||||
|
||||
nohup python calculate_error.py --name abl_2_pyr_32_64_128_256_512_50k > nohup/error/abl_2_pyr_32_64_128_256_512_50k.out 2>&1 &
|
||||
|
||||
nohup 7z x CAPE_dataset.zip.001 > nohup/unzip.out 2>&1 &
|
||||
|
||||
nohup python main.py --config configs/CAPE-affineconv_nz18_pose24_clotype8_male.yaml --name CAPE-affineconv_nz18_pose24_clotype8_male --gender male --mode demo > cape_male.out 2>&1 &
|
||||
nohup python3 main.py --config configs/CAPE-affineconv_nz18_pose24_clotype8_female.yaml --name CAPE-affineconv_nz18_pose24_clotype8_female --gender female --mode demo > cape_female.out 2>&1 &
|
||||
|
||||
nohup python curvature_labels.py > nohup/dijkstra_skirt.out 2>&1 &
|
||||
nohup python downscale.py > nohup/downscale_2_test_skirt.out 2>&1 &
|
||||
nohup python downscale_cape.py > nohup/downscale_cape_train_m.out 2>&1 &
|
||||
nohup python merge_data.py -fi /home/workspace/data/dataset/train -fo /home/workspace/local-data/train -s 0 -e 1500 > nohup/merge_data_valid_skirt.out 2>&1 &
|
||||
nohup python merge_data.py -fi /home/workspace/data/dataset/test_skirt -fo /home/workspace/data/dataset/test_skirt_ready -s 0 -e 1500 > nohup/merge_data_test_skirt_ready.out 2>&1 &
|
||||
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 0 -e 100 -n lhunor_unpose_skirt_0_100 > nohup/unpose_skirt_0_100.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 100 -e 200 -n lhunor_unpose_skirt_100_200 > nohup/unpose_skirt_100_200.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 200 -e 300 -n lhunor_unpose_skirt_200_300 > nohup/unpose_skirt_200_300.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 300 -e 400 -n lhunor_unpose_skirt_300_400 > nohup/unpose_skirt_300_400.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 400 -e 500 -n lhunor_unpose_skirt_400_500 > nohup/unpose_skirt_400_500.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 500 -e 600 -n lhunor_unpose_skirt_500_600 > nohup/unpose_skirt_500_600.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 600 -e 700 -n lhunor_unpose_skirt_600_700 > nohup/unpose_skirt_600_700.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 700 -e 800 -n lhunor_unpose_skirt_700_800 > nohup/unpose_skirt_700_800.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test_skirt -s 0 -e 100 -n lhunor_unpose_test_skirt_0_100 > nohup/unpose_test_skirt_0_100_2.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test_skirt -s 100 -e 200 -n lhunor_unpose_test_skirt_100_200 > nohup/unpose_test_skirt_100_200_2.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 0 -e 100 -n lhunor_unpose_extra_skirt_0_100 > nohup/unpose_extra_skirt_0_100.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 100 -e 200 -n lhunor_unpose_extra_skirt_100_200 > nohup/unpose_extra_skirt_100_200.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 200 -e 300 -n lhunor_unpose_extra_skirt_200_300 > nohup/unpose_extra_skirt_200_300_2.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 300 -e 400 -n lhunor_unpose_extra_skirt_300_400 > nohup/unpose_extra_skirt_300_400.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 0 -e 100 -n lhunor_unpose_test_0_100 > nohup/unpose_test_0_100.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 100 -e 200 -n lhunor_unpose_test_100_200 > nohup/unpose_test_100_200.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 200 -e 300 -n lhunor_unpose_test_200_300 > nohup/unpose_test_200_300.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 300 -e 400 -n lhunor_unpose_test_300_400 > nohup/unpose_test_300_400.out 2>&1 &
|
||||
nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 00 -e 00 -n lhunor_unpose_test_00_00 > nohup/unpose_test_00_00.out 2>&1 &
|
||||
|
||||
|
||||
calculate avg garment for skirt
|
||||
need dijkstra/dijsktra skirt
|
||||
bodies have to be converted in non-skirt mode
|
||||
need to pretrain encoders again...
|
||||
not enough sapce to preprocess data
|
||||
|
||||
add one more layer and train, repeat
|
||||
|
||||
curvature binning
|
||||
do it according to trainingn set distribution for validation, or it's own for validation
|
||||
merge curvatures for validation from data-net to local
|
||||
avg garment for big datasets
|
||||
check ranges of curvatur ranges
|
||||
|
||||
collision in pecentage
|
||||
bend loss?
|
||||
evaluation pipeline
|
||||
look tailernot dataset
|
||||
blender for visualization
|
||||
|
||||
|
||||
gpu needed estimation
|
||||
let's say if we want to finish in on eweek
|
||||
|
||||
we use the same skinning weights for skirts. okay?
|
||||
|
||||
|
||||
redo curvatures for skirt train.valid
|
||||
train_skirt: array([1.83511008e-06, 1.84299448e-03, 3.38148023e-03, 5.67459036e-03, 1.02263279e-02, 2.21962575e-02, 3.24614763e-01])
|
||||
array([1.83511008e-06, 1.82755420e-03, 3.35242413e-03, 5.61911613e-03, 1.01151643e-02, 2.20824350e-02, 3.25994939e-01])
|
||||
valid_skirt: array([4.51388405e-06, 1.90253550e-03, 3.44673195e-03, 5.73266158e-03, 1.02412868e-02, 2.22220197e-02, 3.12285781e-01])
|
||||
array([4.51388405e-06, 1.88061886e-03, 3.40514188e-03, 5.64374123e-03, 1.00161384e-02, 2.18187869e-02, 3.17445695e-01])
|
||||
test_skirt: array([7.21150764e-06, 1.77989923e-03, 3.26598203e-03, 5.47702867e-03, 9.79944225e-03, 2.07518619e-02, 3.23069602e-01])
|
||||
|
||||
how to include curvature in evaluation?
|
||||
use gt, predict with vae?
|
||||
|
||||
|
||||
docker run -d --rm -p 61208-61209:61208-61209 -e GLANCES_OPT="-w" -v /var/run/docker.sock:/var/run/docker.sock:ro --pid host nicolargo/glances:alpine-dev
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_ae_body_post import config
|
||||
from configs.config_ae_body import config
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
|
||||
import tensorflow as tf
|
||||
@@ -30,7 +30,7 @@ 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 models.cond_encoder import AutoEncoder
|
||||
|
||||
|
||||
def init_wandb():
|
||||
@@ -171,7 +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'],
|
||||
@@ -185,7 +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'],
|
||||
|
||||
@@ -33,7 +33,7 @@ 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 models.cond_encoder import AutoEncoder
|
||||
|
||||
|
||||
def init_wandb():
|
||||
@@ -205,7 +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'],
|
||||
@@ -219,7 +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'],
|
||||
|
||||
+4
-4
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.config_abl_pyr_32_64_skirt_r1 import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
# import tensorflow as tf
|
||||
@@ -59,13 +59,13 @@ if __name__ == '__main__':
|
||||
folder = config['test_dir']
|
||||
|
||||
checkpoint_name = config['name']
|
||||
iteration = ''
|
||||
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/data/results/post/' + checkpoint_name + '-' + iteration
|
||||
output_folder = '/home/workspace/data/results/' + checkpoint_name + '-' + iteration
|
||||
|
||||
|
||||
faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
|
||||
@@ -107,7 +107,7 @@ 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('/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()):
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -0,0 +1,189 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '1'
|
||||
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
|
||||
# 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_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['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'] = 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'] = 0.0001
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
config['avg_garment_path'] = ''
|
||||
|
||||
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']['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]
|
||||
|
||||
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']['freeze_encoders'] = True
|
||||
|
||||
config['pyramid']['levels'] = []
|
||||
for res in config['pyramid']['resolutions']:
|
||||
if res == 512:
|
||||
level = dict()
|
||||
level['resolution'] = 512
|
||||
level['latent_dim'] = config['latent_dim']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [3, 3, 9, 3]
|
||||
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]
|
||||
level['avg_garment_path'] = ''
|
||||
level['upscale_enhancer'] = ''
|
||||
config['pyramid']['levels'].append(level)
|
||||
elif res == 256:
|
||||
level = dict()
|
||||
level['resolution'] = 256
|
||||
level['latent_dim'] = config['latent_dim']
|
||||
level['kernel_size'] = 7
|
||||
level['encoder_depths'] = [9, 9, 9, 9]
|
||||
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/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'] = config['latent_dim']
|
||||
level['kernel_size'] = 5
|
||||
level['encoder_depths'] = [16, 9, 9, 9]
|
||||
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, 64]
|
||||
level['avg_garment_path'] = ''
|
||||
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'] = 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/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'] = config['latent_dim']
|
||||
level['kernel_size'] = 3
|
||||
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/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'] = 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']['latent_dim'] = config['latent_dim']
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
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]
|
||||
@@ -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]
|
||||
|
||||
|
||||
@@ -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': (),
|
||||
|
||||
@@ -1,44 +1,14 @@
|
||||
config = dict()
|
||||
from configs.config_base import config
|
||||
|
||||
|
||||
config['batch_size'] = 4
|
||||
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['use_filled'] = False
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
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'] = 8
|
||||
config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae']['encoder_dims'] = [8, 16, 32, 32, config['cond_ae']['bottleneck_size']]
|
||||
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
|
||||
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
|
||||
config['cond_ae']['activation'] = 'linear'
|
||||
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['gpu'] = '3'
|
||||
config['lr'] = 0.0005
|
||||
config['name'] = 'static_post_4'
|
||||
config['notes'] = 'convnext + convnext, | \
|
||||
\
|
||||
| \
|
||||
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 | \
|
||||
linear activation| \
|
||||
larger batch size | \
|
||||
'
|
||||
|
||||
config['cond_ae']['upscale_output'] = False
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
config = dict()
|
||||
|
||||
|
||||
config['batch_size'] = 4
|
||||
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['use_filled'] = False
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
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'] = 8
|
||||
config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
|
||||
config['cond_ae']['encoder_dims'] = [8, 16, 32, 32, config['cond_ae']['bottleneck_size']]
|
||||
config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
|
||||
config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
|
||||
config['cond_ae']['activation'] = 'linear'
|
||||
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['lr'] = 0.0005
|
||||
config['name'] = 'static_post_4'
|
||||
config['notes'] = 'convnext + convnext, | \
|
||||
\
|
||||
| \
|
||||
deeper model | \
|
||||
linear activation| \
|
||||
'
|
||||
|
||||
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,),
|
||||
}
|
||||
@@ -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']:
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '2'
|
||||
config['gpu'] = '6'
|
||||
config['name'] = 'abl_pyr_32'
|
||||
config['notes'] = 'fixed noise handling'
|
||||
config['tags'] = ['pyr_post']
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_1']
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
|
||||
@@ -311,7 +311,7 @@ def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False
|
||||
|
||||
def get_summary_str(model):
|
||||
lines = []
|
||||
model.summary(print_fn=lines.append, line_length=150,expand_nested=True,show_trainable=True)
|
||||
model.summary(print_fn=lines.append, line_length=150, expand_nested=True, show_trainable=True)
|
||||
# Add initial spaces to avoid markdown formatting in TensorBoard
|
||||
return lines
|
||||
|
||||
|
||||
+20
-16
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.post.config_abl_pyr_64_128_5 import config
|
||||
from configs.config_abl_pyr_32_64_skirt_r1 import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.pyramid_model_optimized import PyramidNew as Pyramid
|
||||
from models.pyramid_model_optimized import Pyramid
|
||||
from postprocessing import fix_collision
|
||||
|
||||
|
||||
@@ -59,9 +59,11 @@ def init_wandb():
|
||||
artifact.add_file('models/cond_encoder.py')
|
||||
artifact.add_file('dataset.py')
|
||||
artifact.add_file('convnext/models/convnext_tf.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.post.')]
|
||||
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 = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
@@ -261,8 +263,8 @@ def train_step(batch):
|
||||
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']
|
||||
# if config['use_norm_loss'] and step > 2000:
|
||||
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
@@ -334,8 +336,8 @@ def valid_step(batch):
|
||||
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']
|
||||
# if config['use_norm_loss'] and step > 2000:
|
||||
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
@@ -790,7 +792,7 @@ if __name__ == '__main__':
|
||||
# generate_results()
|
||||
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
|
||||
|
||||
# load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
|
||||
|
||||
@@ -801,24 +803,26 @@ if __name__ == '__main__':
|
||||
if len(config['pyramid']['resolutions']) == 1:
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
model.curv_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
|
||||
model.curv_enc.trainable = False
|
||||
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
model.cond_encoder.trainable = False
|
||||
|
||||
# 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
|
||||
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
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_2_pyr_32_64_128_256_512 import config
|
||||
from configs.config_abl_pyr_32_64_skirt_r1 import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
@@ -43,7 +43,7 @@ class CVAE(tf.keras.Model):
|
||||
self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dense(256, activation='relu'),
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Lambda(lambda x: x / 1000),
|
||||
# tf.keras.layers.Lambda(lambda x: x / 1000),
|
||||
tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
|
||||
|
||||
self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
|
||||
|
||||
@@ -24,15 +24,17 @@ class Pyramid(tf.keras.Model):
|
||||
self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
|
||||
if config['pyramid']['body_model'] != '':
|
||||
load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
|
||||
|
||||
self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
if config['pyramid']['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
if config['use_curvature']:
|
||||
self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
if config['pyramid']['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
if config['pyramid']['freeze_encoders']:
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
self.curv_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dense(256, activation='relu'),
|
||||
@@ -70,11 +72,12 @@ class Pyramid(tf.keras.Model):
|
||||
self.add_noise = config['pyramid']['add_noise']
|
||||
self.noise_amount = config['pyramid']['noise_amount']
|
||||
self.use_offsets = config['use_offsets']
|
||||
self.use_curvature = config['use_curvature']
|
||||
|
||||
self.upscale_enhancer = None
|
||||
if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
self.upscale_enhancer : tf.keras.Model = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
self.upscale_enhancer.trainable = False
|
||||
|
||||
@@ -85,9 +88,11 @@ class Pyramid(tf.keras.Model):
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
z_static = self.static_enc(uv_template, training)
|
||||
z_body = self.body_enc(uv_body_posed, training)
|
||||
z_curv = self.curv_enc(uv_curv, training)
|
||||
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
if self.use_curvature:
|
||||
z_curv = self.curv_enc(uv_curv, training=training)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
else:
|
||||
z_cond = tf.concat([z_static, z_body], axis=-1)
|
||||
z_cond = self.cond_encoder(z_cond, training=training)
|
||||
|
||||
return z_cond
|
||||
@@ -294,7 +299,8 @@ class PyramidNew(tf.keras.Model):
|
||||
self.add_noise = config['pyramid']['add_noise']
|
||||
self.noise_amount = config['pyramid']['noise_amount']
|
||||
self.use_offsets = config['use_offsets']
|
||||
|
||||
self.use_curvature = config['use_curvature']
|
||||
|
||||
self.upscale_enhancer = None
|
||||
if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
@@ -309,9 +315,11 @@ class PyramidNew(tf.keras.Model):
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
z_static = self.static_enc(uv_template, training)
|
||||
z_body = self.body_enc(uv_body_posed, training)
|
||||
z_curv = self.curv_enc(uv_curv, training)
|
||||
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
if self.use_curvature:
|
||||
z_curv = self.curv_enc(uv_curv, training=training)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
else:
|
||||
z_cond = tf.concat([z_static, z_body], axis=-1)
|
||||
|
||||
return z_cond
|
||||
|
||||
|
||||
+8
-8
@@ -232,17 +232,17 @@ def generate_frames_file(folder, ratio=0.1):
|
||||
selected = []
|
||||
|
||||
for id in tqdm.tqdm(sub_ids):
|
||||
if garment_type_table[id] != 'Dress':
|
||||
continue
|
||||
if len(selected) > 30000:
|
||||
break
|
||||
# if garment_type_table[id] != 'Dress':
|
||||
# continue
|
||||
# if len(selected) > 30000:
|
||||
# break
|
||||
|
||||
# frames = glob.glob(os.path.join(folder, 'pose_shape', id + '_*'))
|
||||
frames = [frame for frame in all_frames if frame.startswith(id + '_')]
|
||||
frames = [frame.split('/')[-1].split('.')[0] for frame in frames]
|
||||
selected.extend(frames)
|
||||
|
||||
with open(os.path.join(folder, 'frames_dress_30k.txt'), 'w') as f:
|
||||
with open(os.path.join(folder, 'frames_skirt_10p.txt'), 'w') as f:
|
||||
for i, frame in enumerate(selected):
|
||||
f.write(frame + '\n')
|
||||
|
||||
@@ -293,9 +293,9 @@ def generate_frames_file_filtered_cape():
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
src_folder = '/home/workspace/local-data/train_skirt' #cloth3d_skirt
|
||||
src_folder = '/home/workspace/local-data/valid_skirt' #cloth3d_skirt
|
||||
# src_folder = r'D:\Work\Dataset\validation_2'
|
||||
dest_folder = '/home/workspace/data/dataset/validation'
|
||||
dest_folder = '/home/workspace/data/dataset/local-cache/train_skirt'
|
||||
|
||||
|
||||
# create_no_orient_body(src_folder)
|
||||
@@ -306,5 +306,5 @@ if __name__ == '__main__':
|
||||
# verify_poses(src_folder)
|
||||
# create_garment_type_table()
|
||||
# generate_faces()
|
||||
generate_frames_file(src_folder, ratio=1)
|
||||
generate_frames_file(src_folder, ratio=0.1)
|
||||
# generate_frames_file_filtered_cape()
|
||||
|
||||
Reference in New Issue
Block a user