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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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+228
@@ -0,0 +1,228 @@
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nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2_2" > nohup/review/curv_1_orig_curv_100_10p_r2_2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/review/abl_all_10p_curv_25_r2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/review/abl_all_10p_curv_100_r2.out 2>&1 &
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nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_100_r2.out 2>&1 &
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nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_10_r2.out 2>&1 &
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nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_25_r2.out 2>&1 &
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nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/error/abl_all_10p_curv_100_r2.out 2>&1 &
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nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/error/abl_all_10p_curv_10_r2.out 2>&1 &
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nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/error/abl_all_10p_curv_25_r2.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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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 &
|
||||
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 &
|
||||
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 &
|
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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 &
|
||||
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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|
||||
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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|
||||
nohup python upscale_test.py > nohup/u.out 2>&1 &
|
||||
nohup python unpose_one_hot.py > nohup/unpose_one_hot_train_skirt.out 2>&1 &
|
||||
nohup python preprocessing.py > nohup/unshape_one_hot_test_skirt.out 2>&1 &
|
||||
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
|
||||
|
||||
|
||||
+59
-28
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_ae_curv_post import config
|
||||
from configs.config_ae_curv_1_orig_curv_10_10p_r2_2 import config
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
|
||||
import tensorflow as tf
|
||||
@@ -31,13 +31,13 @@ import plotly.express as ex
|
||||
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 models.cond_encoder import AutoEncoder
|
||||
|
||||
|
||||
testing = False
|
||||
def init_wandb():
|
||||
if not testing:
|
||||
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
|
||||
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('auto_encoder_curv.py')
|
||||
artifact.add_file('losses.py')
|
||||
@@ -47,7 +47,9 @@ def init_wandb():
|
||||
artifact.add_file('convnext/models/convnext_tf.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
||||
for config_file_name in config_file_names:
|
||||
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
|
||||
file_name = '/'.join(config_file_name.split('.')) + '.py'
|
||||
if os.path.exists(file_name):
|
||||
artifact.add_file(file_name)
|
||||
run.log_artifact(artifact)
|
||||
|
||||
uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
@@ -99,43 +101,70 @@ def dice_dense(prediction, ground_truth, weight_map=None):
|
||||
def train_step(batch):
|
||||
with tf.GradientTape() as tape:
|
||||
pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
|
||||
|
||||
ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
|
||||
|
||||
mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
|
||||
ce_mesh_loss = scce(mesh_orig, mesh_pred)
|
||||
|
||||
loss = ce_uv_loss + ce_mesh_loss
|
||||
|
||||
loss = ce_uv_loss
|
||||
pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)
|
||||
mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
|
||||
mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])
|
||||
dice = dice_dense(pred, batch['uv_curvature'])
|
||||
|
||||
gradients = tape.gradient(loss, model.trainable_variables)
|
||||
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
||||
return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
|
||||
'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
|
||||
|
||||
|
||||
return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/mean_iou_mesh': mean_iou_mesh, 'train/dice': dice }
|
||||
|
||||
@tf.function
|
||||
def valid_step(batch):
|
||||
pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
|
||||
|
||||
mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
|
||||
ce_mesh_loss = scce(mesh_orig, mesh_pred)
|
||||
loss = ce_uv_loss + ce_mesh_loss
|
||||
|
||||
loss = ce_uv_loss
|
||||
pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)
|
||||
mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
|
||||
mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])
|
||||
dice = dice_dense(pred, batch['uv_curvature'])
|
||||
return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/mean_iou_mesh': mean_iou_mesh, 'valid/dice': dice }
|
||||
|
||||
return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
|
||||
'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
|
||||
# def train_step(batch):
|
||||
# with tf.GradientTape() as tape:
|
||||
# pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
|
||||
|
||||
# ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
|
||||
|
||||
# mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
# mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
|
||||
# ce_mesh_loss = scce(mesh_orig, mesh_pred)
|
||||
|
||||
# loss = ce_uv_loss + ce_mesh_loss
|
||||
|
||||
# mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
|
||||
# dice = dice_dense(pred, batch['uv_curvature'])
|
||||
|
||||
# gradients = tape.gradient(loss, model.trainable_variables)
|
||||
# optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
||||
# return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
|
||||
# 'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
|
||||
|
||||
|
||||
|
||||
# @tf.function
|
||||
# def valid_step(batch):
|
||||
# pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
# ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
|
||||
|
||||
# mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
# mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
|
||||
# ce_mesh_loss = scce(mesh_orig, mesh_pred)
|
||||
# loss = ce_uv_loss + ce_mesh_loss
|
||||
|
||||
# mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
|
||||
# dice = dice_dense(pred, batch['uv_curvature'])
|
||||
|
||||
# return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
|
||||
# 'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
@@ -206,7 +235,7 @@ def test():
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
allow_memory_growth()
|
||||
# allow_memory_growth()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
@@ -226,7 +255,8 @@ if __name__ == '__main__':
|
||||
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'])
|
||||
is_cape=config['is_cape'],
|
||||
curv_neighbourhood=config['curv_neighbourhood'])
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
@@ -240,7 +270,8 @@ if __name__ == '__main__':
|
||||
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'])
|
||||
is_cape=config['is_cape'],
|
||||
curv_neighbourhood=config['curv_neighbourhood'])
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.config_abl_all_10p_curv_25_r2 import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
# import tensorflow as tf
|
||||
@@ -59,7 +59,7 @@ if __name__ == '__main__':
|
||||
folder = config['test_dir']
|
||||
|
||||
checkpoint_name = config['name']
|
||||
iteration = ''
|
||||
iteration = '70000'
|
||||
|
||||
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()
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
config['only_curvatures'] = False
|
||||
|
||||
config['curv_neighbourhood'] = 100
|
||||
|
||||
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'] = '1'
|
||||
config['name'] = 'abl_all_10p_curv_100_r2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_2']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = True
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
config['train_dir'] = '/home/workspace/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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
# config['frames_file_test'] = 'frames_dress_30k.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/static_1_skirt'
|
||||
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_100_10p_r2_2'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['latent_dim'] = 128
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = config['latent_dim']
|
||||
config['cvae']['add_noise'] = True
|
||||
config['cvae']['noise_amount'] = 0.025
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
|
||||
config['cvae']['decoder_input'] = (16, 8, 80)
|
||||
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
|
||||
|
||||
|
||||
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]
|
||||
@@ -0,0 +1,125 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
config['only_curvatures'] = False
|
||||
|
||||
config['curv_neighbourhood'] = 10
|
||||
|
||||
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'] = '6'
|
||||
config['name'] = 'abl_all_10p_curv_10_r2_2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_2']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
# config['frames_file_test'] = 'frames_dress_30k.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/static_1_skirt'
|
||||
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_10_10p_r2_2'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['latent_dim'] = 128
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = config['latent_dim']
|
||||
config['cvae']['add_noise'] = True
|
||||
config['cvae']['noise_amount'] = 0.025
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
|
||||
config['cvae']['decoder_input'] = (16, 8, 80)
|
||||
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
|
||||
|
||||
|
||||
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]
|
||||
@@ -0,0 +1,125 @@
|
||||
config = dict()
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
# DON'T FORGET TO CHECK IF STORE IN MEMORY
|
||||
# DON'T FORGET TO CHECK REG LOSS COEFF
|
||||
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
|
||||
# DON'T FORGET TO CHECK AVG GARMENT USED
|
||||
|
||||
config['use_skinning'] = True # and deshaping
|
||||
config['remove_global_orientation'] = True
|
||||
config['use_curvature'] = True
|
||||
config['use_extra_losses'] = True # edge, collision, 3d mesh
|
||||
config['use_offsets'] = True
|
||||
|
||||
config['use_norm_loss'] = True
|
||||
config['only_curvatures'] = False
|
||||
|
||||
config['curv_neighbourhood'] = 25
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'abl_all_10p_curv_25_r2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_2']
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
# config['frames_file_test'] = 'frames_dress_30k.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/static_1_skirt'
|
||||
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_25_10p_r2'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['latent_dim'] = 128
|
||||
|
||||
###############
|
||||
config['lr'] = 0.0001# 0.0001
|
||||
###############
|
||||
config['starting_balancing_coeff'] = 0.001
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (512, 256, 3),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
'mesh_posed': (config['smpl_res'], 3,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
}
|
||||
config['signature_static'] = { 'uv_static': (512, 256, 3),
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
|
||||
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
|
||||
else:
|
||||
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
|
||||
|
||||
|
||||
config['upscale_enhancer'] = ''
|
||||
config['freeze_encoders'] = True
|
||||
config['use_one_hot'] = False
|
||||
|
||||
config['cvae'] = dict()
|
||||
config['cvae']['is_label'] = False
|
||||
config['cvae']['latent_dim'] = config['latent_dim']
|
||||
config['cvae']['add_noise'] = True
|
||||
config['cvae']['noise_amount'] = 0.025
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
|
||||
config['cvae']['decoder_input'] = (16, 8, 80)
|
||||
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
|
||||
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
|
||||
|
||||
|
||||
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]
|
||||
@@ -1,7 +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
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'pyr_abl_32_skirt_2'
|
||||
config['notes'] = 'continued from pyr_abl_32_skirt-180k'
|
||||
config['name'] = 'pyr_abl_32_skirt_r1'
|
||||
config['notes'] = ''
|
||||
|
||||
# DON'T FORGET TO RESET SSIM LOSS USAGE
|
||||
# DON'T FORGET TO CHECK MODEL LOADING
|
||||
@@ -24,7 +24,10 @@ 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
|
||||
@@ -35,7 +38,7 @@ 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 +68,7 @@ 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]
|
||||
|
||||
|
||||
@@ -1,13 +1,23 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['gpu'] = '4'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1' #'ae_curv_9'
|
||||
config['name'] = 'curv_1_orig_r2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 10
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1_orig_curv_100_10p_r2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 100
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '1'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1_orig_curv_100_10p_r2_2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 100
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1_orig_curv_10_10p_r2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 10
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1_orig_curv_10_10p_r2_2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 10
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
from configs.config_base import config
|
||||
|
||||
config['gpu'] = '6'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_1_orig_curv_25_10p_r2' #'ae_curv_9'
|
||||
config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
|
||||
| \
|
||||
'
|
||||
config['tags'] = ['review_2']
|
||||
config['remove_global_orientation'] = True
|
||||
config['dataset_in_memory'] = True
|
||||
|
||||
config['cond_ae']['is_label'] = True
|
||||
config['is_skirt'] = True
|
||||
|
||||
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['frames_file'] = 'frames_skirt_10p.txt'
|
||||
config['frames_file_valid'] = 'frames_skirt_10p.txt'
|
||||
config['curv_neighbourhood'] = 25
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
'uv_mask': (512, 256,),
|
||||
}
|
||||
|
||||
@@ -366,8 +366,8 @@ class ConvNeXtReverseLabels(tf.keras.Model):
|
||||
self.stages.append(stage)
|
||||
cur += depths[i]
|
||||
|
||||
# self.last = Dense(nr_of_classes, activation='softmax')
|
||||
self.last = Dense(nr_of_classes - 1)
|
||||
self.last = Dense(nr_of_classes, activation='softmax')
|
||||
# self.last = Dense(nr_of_classes - 1)
|
||||
|
||||
def call(self, x, training):
|
||||
for i in reversed(range(len(self.downsample_layers))):
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.config_abl_all_10p_curv_100_r2 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, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.cvae_model import CVAENew as CVAE
|
||||
from models.cvae_model import CVAE
|
||||
|
||||
|
||||
def init_wandb():
|
||||
@@ -73,7 +73,7 @@ def kl_loss(mean, logvar):
|
||||
|
||||
# 3d loss on posed !!!!!!!!!!!!
|
||||
|
||||
# @tf.function
|
||||
@tf.function
|
||||
def train_step(batch):
|
||||
|
||||
with tf.GradientTape() as tape:
|
||||
@@ -476,7 +476,7 @@ def test():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init_wandb()
|
||||
init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
@@ -494,7 +494,8 @@ if __name__ == '__main__':
|
||||
use_one_hot=config['use_one_hot'],
|
||||
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'])
|
||||
is_cape=config['is_cape'],
|
||||
curv_neighbourhood=config['curv_neighbourhood'])
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
@@ -509,7 +510,8 @@ if __name__ == '__main__':
|
||||
use_one_hot=config['use_one_hot'],
|
||||
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'])
|
||||
is_cape=config['is_cape'],
|
||||
curv_neighbourhood=config['curv_neighbourhood'])
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
|
||||
+18
-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_skirt 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'])
|
||||
@@ -208,7 +210,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
|
||||
return fig
|
||||
|
||||
|
||||
@tf.function
|
||||
# @tf.function
|
||||
def train_step(batch):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -731,7 +733,7 @@ def speed_step(levels_input, conds):
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str, default='lhunor')
|
||||
args = vars(parser.parse_args())
|
||||
@@ -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')
|
||||
|
||||
@@ -807,18 +809,18 @@ if __name__ == '__main__':
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
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
|
||||
|
||||
+9
-6
@@ -194,7 +194,8 @@ class DatasetFactory():
|
||||
use_one_hot=False,
|
||||
use_no_orient=True,
|
||||
testing=False,
|
||||
frames_file=None):
|
||||
frames_file=None,
|
||||
curv_neighbourhood=25):
|
||||
assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
|
||||
|
||||
dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, frames_file=frames_file)
|
||||
@@ -260,7 +261,7 @@ class DatasetFactory():
|
||||
|
||||
|
||||
if 'mesh_curvature' in signature.keys():
|
||||
mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
|
||||
mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
|
||||
item['mesh_curvature'] = mesh_curvature.astype(np.int32)
|
||||
|
||||
|
||||
@@ -270,7 +271,8 @@ class DatasetFactory():
|
||||
res_string = '_' + str(res)
|
||||
|
||||
if 'uv_curvature' + res_string in signature.keys():
|
||||
uv_curvature = np.load(os.path.join(folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz'));
|
||||
# print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
|
||||
uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'));
|
||||
if res_string != '':
|
||||
uv_curvature = uv_curvature['uv_curvature']
|
||||
else:
|
||||
@@ -381,15 +383,16 @@ if __name__ == '__main__':
|
||||
# 'uv_shaped_body': (3, 512, 256,),
|
||||
}
|
||||
|
||||
dataset = factory.get_dataset(folder='/home/workspace/local-data/train_skirt',
|
||||
dataset = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
|
||||
batch_size=1,
|
||||
signature=signature_dynamic,
|
||||
resolutions=[512],
|
||||
do_repeat=False,
|
||||
use_one_hot=False,
|
||||
is_cape=False,
|
||||
frames_file='frames_dress_30k.txt')
|
||||
static_dataset = StaticDataset(folder='/home/workspace/local-data/train_skirt',
|
||||
frames_file='frames_skirt_10p.txt',
|
||||
curv_neighbourhood=25)
|
||||
static_dataset = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
|
||||
signature=signature_static,
|
||||
resolutions=[512],
|
||||
is_cape=False)
|
||||
|
||||
+3
-3
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.config_abl_all_10p_curv_25_r2 import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.cvae_model import CVAENew as CVAE
|
||||
from models.cvae_model import CVAE
|
||||
from custom_utils.supersmpl_io import writeOBJ
|
||||
# from unif_metric import chamfer_and_score
|
||||
|
||||
@@ -102,7 +102,7 @@ if __name__ == '__main__':
|
||||
ids = [id.split('_')[0] for id in ids]
|
||||
ids = set(ids)
|
||||
|
||||
iteration = ''
|
||||
iteration = '70000'
|
||||
|
||||
load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
|
||||
# load_model(model, 'checkpoints/' + 'abl_all_120k')
|
||||
|
||||
@@ -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'])
|
||||
|
||||
Reference in New Issue
Block a user