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review_7
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 luni
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luni
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luni
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luni
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luni
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luni
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luni
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luni
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luni
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luni
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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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+219
@@ -0,0 +1,219 @@
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nohup python auto_encoder_static.py --name="lhunor_ae_static_r7_3" > nohup/review/ae_static_r7_3.out 2>&1 &
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nohup python auto_encoder_body.py --name="lhunor_ae_body_r7_3" > nohup/review/ae_body_r7_3.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_r7_3" > nohup/review/ae_curv_r7_3.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_resnet_r7" > nohup/review/abl_all_resnet_r7.out 2>&1 &
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nohup python calculate_error.py --name abl_all_resnet_r7_70k > nohup/error/abl_all_resnet_r7_70k.out 2>&1 &
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nohup python evaluate_ae.py --name abl_all_resnet_r7_70k > nohup/evaluate/abl_all_resnet_r7_70k.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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|
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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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|
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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 &
|
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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 &
|
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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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|
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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 &
|
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nohup python evaluate_ae_support.py > nohup/evaluate_ae_support.out 2>&1 &
|
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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 &
|
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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.review.config_ae_body import config
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
|
||||
import tensorflow as tf
|
||||
@@ -34,7 +34,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
|
||||
|
||||
|
||||
def init_wandb():
|
||||
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_body.py')
|
||||
artifact.add_file('losses.py')
|
||||
@@ -42,7 +42,7 @@ 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.')]
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
|
||||
for config_file_name in config_file_names:
|
||||
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
|
||||
run.log_artifact(artifact)
|
||||
@@ -137,7 +137,10 @@ def train():
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
wandb.log(to_log, step=step)
|
||||
if step % 1000 == 0:
|
||||
wandb.log(to_log, step=step, commit=True)
|
||||
else:
|
||||
wandb.log(to_log, step=step, commit=False)
|
||||
|
||||
if step % 10000 == 0:
|
||||
manager_enc.save(checkpoint_number=step)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_ae_curv_post import config
|
||||
from configs.review.config_ae_curv import config
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
|
||||
import tensorflow as tf
|
||||
@@ -37,7 +37,7 @@ from models.cond_encoder import AutoEncoderNew as 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')
|
||||
@@ -45,7 +45,7 @@ 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.')]
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
|
||||
for config_file_name in config_file_names:
|
||||
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
|
||||
run.log_artifact(artifact)
|
||||
@@ -177,8 +177,11 @@ def train():
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
wandb.log(to_log, step=step)
|
||||
print(step)
|
||||
if step % 1000 == 0:
|
||||
wandb.log(to_log, step=step, commit=True)
|
||||
else:
|
||||
wandb.log(to_log, step=step, commit=False)
|
||||
# print(step)
|
||||
|
||||
if step % 10000 == 0:
|
||||
manager_enc.save(checkpoint_number=step)
|
||||
@@ -206,7 +209,7 @@ 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())
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_ae_static import config
|
||||
from configs.review.config_ae_static import config
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
|
||||
import tensorflow as tf
|
||||
@@ -37,15 +37,16 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
|
||||
|
||||
|
||||
def init_wandb():
|
||||
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_static.py')
|
||||
artifact.add_file('losses.py')
|
||||
artifact.add_file('models/base.py')
|
||||
artifact.add_file('models/resnet.py')
|
||||
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.')]
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.review.')]
|
||||
for config_file_name in config_file_names:
|
||||
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
|
||||
run.log_artifact(artifact)
|
||||
@@ -138,7 +139,10 @@ def train():
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
|
||||
# if step % 1000 == 0:
|
||||
# wandb.log(to_log, step=step, commit=True)
|
||||
# else:
|
||||
# wandb.log(to_log, step=step, commit=False)
|
||||
wandb.log(to_log, step=step)
|
||||
|
||||
if step % 10000 == 0:
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.review.config_abl_all import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
# import tensorflow as tf
|
||||
|
||||
@@ -31,7 +31,7 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
|
||||
config['cond_ae']['activation'] = 'linear'
|
||||
|
||||
|
||||
config['gpu'] = '5'
|
||||
config['gpu'] = '0'
|
||||
config['lr'] = 0.0005
|
||||
config['name'] = 'static_post_4'
|
||||
config['notes'] = 'convnext + convnext, | \
|
||||
|
||||
@@ -0,0 +1,126 @@
|
||||
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
|
||||
|
||||
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
|
||||
assert ablation_count == 1 or ablation_count == 0
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['name'] = 'abl_all_resnet_r7'
|
||||
config['notes'] = ''
|
||||
|
||||
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
|
||||
if config['use_skinning']:
|
||||
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
|
||||
|
||||
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
|
||||
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['frames_file'] = 'frames_dress_30k.txt'
|
||||
# config['frames_file_valid'] = 'frames_dress_30k.txt'
|
||||
# config['frames_file_test'] = 'frames_dress_30k.txt'
|
||||
|
||||
config['static_model'] = 'checkpoints/post/static_r7_3'
|
||||
config['body_model'] = 'checkpoints/post/body_r7_3'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_r7_3'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
###############
|
||||
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']['add_noise'] = True
|
||||
config['cvae']['noise_amount'] = 0.001
|
||||
|
||||
config['cvae']['kernel_size'] = 7
|
||||
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
|
||||
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
|
||||
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
|
||||
|
||||
|
||||
config['cond_ae_static'] = dict()
|
||||
config['cond_ae_static']['is_label'] = False
|
||||
config['cond_ae_static']['kernel_size'] = 7
|
||||
config['cond_ae_static']['bottleneck_size'] = 8
|
||||
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 9, 3]
|
||||
config['cond_ae_static']['encoder_dims'] = [16, 32, 64, 64, config['cond_ae_static']['bottleneck_size']]
|
||||
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
|
||||
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
|
||||
config['cond_ae_static']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_curv'] = dict()
|
||||
config['cond_ae_curv']['is_label'] = True
|
||||
config['cond_ae_curv']['kernel_size'] = 7
|
||||
config['cond_ae_curv']['bottleneck_size'] = 40
|
||||
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
|
||||
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
|
||||
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
|
||||
config['cond_ae_curv']['activation'] = 'tanh'
|
||||
|
||||
config['cond_ae_body'] = dict()
|
||||
config['cond_ae_body']['is_label'] = False
|
||||
config['cond_ae_body']['kernel_size'] = 7
|
||||
config['cond_ae_body']['bottleneck_size'] = 16
|
||||
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
|
||||
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
|
||||
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
|
||||
config['cond_ae_body']['activation'] = 'tanh'
|
||||
@@ -0,0 +1,51 @@
|
||||
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'] = False
|
||||
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'] = 16
|
||||
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, 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'] = 'tanh'
|
||||
|
||||
|
||||
config['gpu'] = '4'
|
||||
config['lr'] = 0.0001
|
||||
config['name'] = 'body_r7_3'
|
||||
config['notes'] = 'resnet18'
|
||||
config['tags'] = ['review_7']
|
||||
|
||||
config['cond_ae']['upscale_output'] = False
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'pose_shape': (72 + 11,),
|
||||
'uv_body_posed': (3, 512, 256,),
|
||||
'mesh_body_posed': (config['smpl_res'], 3,),
|
||||
}
|
||||
config['signature_static'] = {
|
||||
'mesh_mask': (config['smpl_res'],),
|
||||
}
|
||||
@@ -0,0 +1,50 @@
|
||||
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'] = False
|
||||
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'] = True
|
||||
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'] = 40
|
||||
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
|
||||
config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, 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'] = 'tanh'
|
||||
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_r7_3'
|
||||
config['notes'] = 'resnet18'
|
||||
config['tags'] = ['review_7']
|
||||
|
||||
config['cond_ae']['upscale_output'] = False
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
|
||||
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 +1,51 @@
|
||||
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'] = '6'
|
||||
config['lr'] = 0.0005
|
||||
config['name'] = 'static_r7_3'
|
||||
config['notes'] = 'resnet18 based, one more block'
|
||||
config['tags'] = ['review_7']
|
||||
|
||||
|
||||
|
||||
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,),
|
||||
}
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.review.config_abl_all import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
@@ -58,7 +58,9 @@ 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 = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
|
||||
@@ -73,7 +75,7 @@ def kl_loss(mean, logvar):
|
||||
|
||||
# 3d loss on posed !!!!!!!!!!!!
|
||||
|
||||
# @tf.function
|
||||
@tf.function
|
||||
def train_step(batch):
|
||||
|
||||
with tf.GradientTape() as tape:
|
||||
@@ -425,8 +427,6 @@ def sample(folder):
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def test():
|
||||
str_split = config['name'].split('_')
|
||||
test_idx = str_split.index('test')
|
||||
@@ -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())
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
from configs.review.config_abl_all import config
|
||||
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
+31
-15
@@ -1,5 +1,7 @@
|
||||
import tensorflow as tf
|
||||
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
|
||||
from models.resnet import resnet_18, make_basic_block_layer
|
||||
|
||||
|
||||
class Bottleneck(tf.keras.Model):
|
||||
|
||||
@@ -111,20 +113,34 @@ class Decoder(tf.keras.Model):
|
||||
|
||||
class VariationalEncoderNew(tf.keras.Model):
|
||||
|
||||
def __init__(self, depths, dims, kernel_size=7):
|
||||
def __init__(self, features):
|
||||
super(VariationalEncoderNew, self).__init__()
|
||||
|
||||
self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
|
||||
self.encoder = resnet_18(features)
|
||||
|
||||
self.mean_branch = tf.keras.Sequential(
|
||||
[Block(dim=64, drop_path=0, kernel_size=kernel_size,
|
||||
layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
|
||||
)
|
||||
self.mean_branch = tf.keras.Sequential([
|
||||
tf.keras.layers.Conv2D(filters=features,
|
||||
kernel_size=(3, 3),
|
||||
strides=1,
|
||||
padding="same"),
|
||||
tf.keras.layers.BatchNormalization(),
|
||||
tf.keras.layers.Conv2D(filters=features,
|
||||
kernel_size=(3, 3),
|
||||
strides=1,
|
||||
padding="same")
|
||||
])
|
||||
|
||||
self.var_branch = tf.keras.Sequential(
|
||||
[Block(dim=64, drop_path=0, kernel_size=kernel_size,
|
||||
layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
|
||||
)
|
||||
self.var_branch = tf.keras.Sequential([
|
||||
tf.keras.layers.Conv2D(filters=features,
|
||||
kernel_size=(3, 3),
|
||||
strides=1,
|
||||
padding="same"),
|
||||
tf.keras.layers.BatchNormalization(),
|
||||
tf.keras.layers.Conv2D(filters=features,
|
||||
kernel_size=(3, 3),
|
||||
strides=1,
|
||||
padding="same")
|
||||
])
|
||||
|
||||
self.flatten_mean = tf.keras.layers.Flatten()
|
||||
self.flatten_var = tf.keras.layers.Flatten()
|
||||
@@ -147,10 +163,10 @@ class VariationalEncoderNew(tf.keras.Model):
|
||||
|
||||
class AEEncoderNew(tf.keras.Model):
|
||||
|
||||
def __init__(self, depths, dims, kernel_size=7, activation='tanh'):
|
||||
def __init__(self, features, activation='tanh'):
|
||||
super(AEEncoderNew, self).__init__()
|
||||
|
||||
self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
|
||||
self.encoder = resnet_18(features)
|
||||
self.act = tf.keras.layers.Activation(activation)
|
||||
|
||||
|
||||
@@ -171,15 +187,15 @@ class AEEncoderNew(tf.keras.Model):
|
||||
|
||||
class DecoderNew(tf.keras.Model):
|
||||
|
||||
def __init__(self, depths, dims, kernel_size=7, is_label=False):
|
||||
def __init__(self, features, is_label=False, is_cvae=False):
|
||||
super(DecoderNew, self).__init__()
|
||||
|
||||
self.is_label = is_label
|
||||
|
||||
if not is_label:
|
||||
self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
|
||||
self.decoder = resnet_18(features, reversed=True, is_cvae=is_cvae)
|
||||
else:
|
||||
self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
|
||||
self.decoder = resnet_18(features, reversed=True, labels=6, is_cvae=is_cvae)
|
||||
|
||||
|
||||
def call(self, inputs, training):
|
||||
|
||||
@@ -25,8 +25,8 @@ class AutoEncoderNew(tf.keras.Model):
|
||||
def __init__(self, config):
|
||||
super(AutoEncoderNew, self).__init__()
|
||||
|
||||
self.encoder : AEEncoderNew = AEEncoderNew(config['encoder_depths'], config['encoder_dims'], config['kernel_size'], config['activation'])
|
||||
self.decoder : DecoderNew = DecoderNew(config['decoder_depths'], config['decoder_dims'], kernel_size=config['kernel_size'], is_label=config['is_label'])
|
||||
self.encoder : AEEncoderNew = AEEncoderNew(config['bottleneck_size'], config['activation'])
|
||||
self.decoder : DecoderNew = DecoderNew(config['bottleneck_size'], is_label=config['is_label'])
|
||||
|
||||
|
||||
def call(self, inputs, training):
|
||||
|
||||
@@ -159,16 +159,16 @@ class CVAENew(tf.keras.Model):
|
||||
self.size = config['resolution']
|
||||
|
||||
if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
|
||||
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
|
||||
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['bottleneck_size'], config['cond_ae_static']['activation'])
|
||||
if config['static_model'] != '':
|
||||
load_model(self.static_enc, config['static_model'] + '_enc')
|
||||
|
||||
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size'])
|
||||
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['bottleneck_size'])
|
||||
if config['body_model'] != '':
|
||||
load_model(self.body_enc, config['body_model'] + '_enc')
|
||||
|
||||
if config['use_curvature']:
|
||||
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
|
||||
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['bottleneck_size'], config['cond_ae_curv']['activation'])
|
||||
if config['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['curvature_model'] + '_enc')
|
||||
|
||||
@@ -179,10 +179,10 @@ class CVAENew(tf.keras.Model):
|
||||
if config['use_curvature']:
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
|
||||
self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
|
||||
self.encoder = VariationalEncoderNew(config['cvae']['encoder_dims'][-1])
|
||||
self.decoder = DecoderNew(config['cvae']['decoder_dims'][-1], is_cvae=True)
|
||||
|
||||
self.reshape = tf.keras.layers.Reshape((8, 4, 64))
|
||||
self.reshape = tf.keras.layers.Reshape((8, 4, config['cvae']['decoder_dims'][-1]))
|
||||
|
||||
if config['avg_garment_path'] != '':
|
||||
self.avg_garment = np.load(config['avg_garment_path'])
|
||||
|
||||
@@ -0,0 +1,254 @@
|
||||
# taken from https://github.com/calmisential/TensorFlow2.0_ResNet/blob/master/models/resnet.py
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
|
||||
class BasicBlock(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self, filter_num, stride=1, upsample=False):
|
||||
super(BasicBlock, self).__init__()
|
||||
if not upsample:
|
||||
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
|
||||
kernel_size=(3, 3),
|
||||
strides=stride,
|
||||
padding="same")
|
||||
else:
|
||||
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=filter_num,
|
||||
kernel_size=(3, 3),
|
||||
strides=stride,
|
||||
padding="same")
|
||||
self.bn1 = tf.keras.layers.BatchNormalization()
|
||||
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
|
||||
kernel_size=(3, 3),
|
||||
strides=1,
|
||||
padding="same")
|
||||
self.bn2 = tf.keras.layers.BatchNormalization()
|
||||
if stride != 1:
|
||||
if not upsample:
|
||||
self.updownsample = tf.keras.Sequential()
|
||||
self.updownsample.add(tf.keras.layers.Conv2D(filters=filter_num,
|
||||
kernel_size=(1, 1),
|
||||
strides=stride))
|
||||
self.updownsample.add(tf.keras.layers.BatchNormalization())
|
||||
else:
|
||||
self.updownsample = tf.keras.Sequential()
|
||||
self.updownsample.add(tf.keras.layers.Conv2DTranspose(filters=filter_num,
|
||||
kernel_size=(1, 1),
|
||||
strides=stride))
|
||||
self.updownsample.add(tf.keras.layers.BatchNormalization())
|
||||
else:
|
||||
self.updownsample = lambda x: x
|
||||
|
||||
def call(self, inputs, training=None, **kwargs):
|
||||
residual = self.updownsample(inputs)
|
||||
|
||||
x = self.conv1(inputs)
|
||||
x = self.bn1(x, training=training)
|
||||
x = tf.nn.relu(x)
|
||||
x = self.conv2(x)
|
||||
x = self.bn2(x, training=training)
|
||||
|
||||
output = tf.nn.relu(tf.keras.layers.add([residual, x]))
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class BottleNeck(tf.keras.layers.Layer):
|
||||
def __init__(self, filter_num, stride=1):
|
||||
super(BottleNeck, self).__init__()
|
||||
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
|
||||
kernel_size=(1, 1),
|
||||
strides=1,
|
||||
padding='same')
|
||||
self.bn1 = tf.keras.layers.BatchNormalization()
|
||||
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
|
||||
kernel_size=(3, 3),
|
||||
strides=stride,
|
||||
padding='same')
|
||||
self.bn2 = tf.keras.layers.BatchNormalization()
|
||||
self.conv3 = tf.keras.layers.Conv2D(filters=filter_num * 4,
|
||||
kernel_size=(1, 1),
|
||||
strides=1,
|
||||
padding='same')
|
||||
self.bn3 = tf.keras.layers.BatchNormalization()
|
||||
|
||||
self.downsample = tf.keras.Sequential()
|
||||
self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4,
|
||||
kernel_size=(1, 1),
|
||||
strides=stride))
|
||||
self.downsample.add(tf.keras.layers.BatchNormalization())
|
||||
|
||||
def call(self, inputs, training=None, **kwargs):
|
||||
residual = self.downsample(inputs)
|
||||
|
||||
x = self.conv1(inputs)
|
||||
x = self.bn1(x, training=training)
|
||||
x = tf.nn.relu(x)
|
||||
x = self.conv2(x)
|
||||
x = self.bn2(x, training=training)
|
||||
x = tf.nn.relu(x)
|
||||
x = self.conv3(x)
|
||||
x = self.bn3(x, training=training)
|
||||
|
||||
output = tf.nn.relu(tf.keras.layers.add([residual, x]))
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def make_basic_block_layer(filter_num, blocks, stride=1, upsample=False):
|
||||
res_block = tf.keras.Sequential()
|
||||
res_block.add(BasicBlock(filter_num, stride=stride, upsample=upsample))
|
||||
|
||||
for _ in range(1, blocks):
|
||||
res_block.add(BasicBlock(filter_num, stride=1))
|
||||
|
||||
return res_block
|
||||
|
||||
|
||||
def make_bottleneck_layer(filter_num, blocks, stride=1):
|
||||
res_block = tf.keras.Sequential()
|
||||
res_block.add(BottleNeck(filter_num, stride=stride))
|
||||
|
||||
for _ in range(1, blocks):
|
||||
res_block.add(BottleNeck(filter_num, stride=1))
|
||||
|
||||
return res_block
|
||||
|
||||
|
||||
class ResNetTypeI(tf.keras.Model):
|
||||
def __init__(self, layer_params, features, reversed=False, labels=None, is_cvae=False):
|
||||
super(ResNetTypeI, self).__init__()
|
||||
self.reversed = reversed
|
||||
self.labels = labels
|
||||
|
||||
if not reversed:
|
||||
self.conv1 = tf.keras.layers.Conv2D(filters=64,
|
||||
kernel_size=(7, 7),
|
||||
strides=2,
|
||||
padding="same")
|
||||
self.bn1 = tf.keras.layers.BatchNormalization()
|
||||
self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
|
||||
strides=2,
|
||||
padding="same")
|
||||
else:
|
||||
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3 if labels is None else labels,
|
||||
kernel_size=(7, 7),
|
||||
strides=2,
|
||||
padding="same")
|
||||
self.bn1 = tf.keras.layers.BatchNormalization()
|
||||
# self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
|
||||
# strides=2,
|
||||
# padding="same")
|
||||
self.pool1 = tf.keras.layers.UpSampling2D()
|
||||
|
||||
self.layer1 = make_basic_block_layer(filter_num=64,
|
||||
blocks=layer_params[0],
|
||||
stride=1 if not reversed else 2,
|
||||
upsample=reversed)
|
||||
self.layer2 = make_basic_block_layer(filter_num=64,
|
||||
blocks=layer_params[1],
|
||||
stride=2,
|
||||
upsample=reversed)
|
||||
self.layer3 = make_basic_block_layer(filter_num=64,
|
||||
blocks=layer_params[2],
|
||||
stride=2,
|
||||
upsample=reversed)
|
||||
self.layer4 = make_basic_block_layer(filter_num=128,
|
||||
blocks=layer_params[3],
|
||||
stride=2,
|
||||
upsample=reversed)
|
||||
self.layer5 = make_basic_block_layer(filter_num=features if not is_cvae else features*2,
|
||||
blocks=layer_params[4],
|
||||
stride=2 if not reversed else 1,
|
||||
upsample=reversed)
|
||||
|
||||
def call(self, inputs, training=None, mask=None):
|
||||
if not self.reversed:
|
||||
x = self.conv1(inputs)
|
||||
x = self.bn1(x, training=training)
|
||||
x = tf.nn.relu(x)
|
||||
x = self.pool1(x)
|
||||
x = self.layer1(x, training=training)
|
||||
x = self.layer2(x, training=training)
|
||||
x = self.layer3(x, training=training)
|
||||
x = self.layer4(x, training=training)
|
||||
x = self.layer5(x, training=training)
|
||||
else:
|
||||
x = self.layer5(inputs, training=training)
|
||||
x = self.layer4(x, training=training)
|
||||
x = self.layer3(x, training=training)
|
||||
x = self.layer2(x, training=training)
|
||||
x = self.layer1(x, training=training)
|
||||
|
||||
x = self.pool1(x)
|
||||
x = self.bn1(x, training=training)
|
||||
# x = tf.nn.relu(x)
|
||||
x = self.conv1(x)
|
||||
if self.labels is None:
|
||||
x = tf.nn.tanh(x)
|
||||
# else:
|
||||
# x = tf.nn.softmax(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResNetTypeII(tf.keras.Model):
|
||||
def __init__(self, layer_params):
|
||||
super(ResNetTypeII, self).__init__()
|
||||
self.conv1 = tf.keras.layers.Conv2D(filters=64,
|
||||
kernel_size=(7, 7),
|
||||
strides=2,
|
||||
padding="same")
|
||||
self.bn1 = tf.keras.layers.BatchNormalization()
|
||||
self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
|
||||
strides=2,
|
||||
padding="same")
|
||||
|
||||
self.layer1 = make_bottleneck_layer(filter_num=64,
|
||||
blocks=layer_params[0])
|
||||
self.layer2 = make_bottleneck_layer(filter_num=128,
|
||||
blocks=layer_params[1],
|
||||
stride=2)
|
||||
self.layer3 = make_bottleneck_layer(filter_num=256,
|
||||
blocks=layer_params[2],
|
||||
stride=2)
|
||||
self.layer4 = make_bottleneck_layer(filter_num=512,
|
||||
blocks=layer_params[3],
|
||||
stride=2)
|
||||
|
||||
self.avgpool = tf.keras.layers.GlobalAveragePooling2D()
|
||||
self.fc = tf.keras.layers.Dense(units=NUM_CLASSES, activation=tf.keras.activations.softmax)
|
||||
|
||||
def call(self, inputs, training=None, mask=None):
|
||||
x = self.conv1(inputs)
|
||||
x = self.bn1(x, training=training)
|
||||
x = tf.nn.relu(x)
|
||||
x = self.pool1(x)
|
||||
x = self.layer1(x, training=training)
|
||||
x = self.layer2(x, training=training)
|
||||
x = self.layer3(x, training=training)
|
||||
x = self.layer4(x, training=training)
|
||||
x = self.avgpool(x)
|
||||
output = self.fc(x)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def resnet_18(features, reversed=False, labels=None, is_cvae=False):
|
||||
return ResNetTypeI(layer_params=[2, 2, 2, 2, 2], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
|
||||
|
||||
|
||||
def resnet_34():
|
||||
return ResNetTypeI(layer_params=[3, 4, 6, 3])
|
||||
|
||||
|
||||
def resnet_50():
|
||||
return ResNetTypeII(layer_params=[3, 4, 6, 3])
|
||||
|
||||
|
||||
def resnet_101():
|
||||
return ResNetTypeII(layer_params=[3, 4, 23, 3])
|
||||
|
||||
|
||||
def resnet_152():
|
||||
return ResNetTypeII(layer_params=[3, 8, 36, 3])
|
||||
Reference in New Issue
Block a user