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6
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 luni
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c2be0971a0 | ||
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luni
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33232fa945 | ||
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luni
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6e6b0a6604 | ||
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luni
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a3c5b526b8 | ||
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luni
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ffdc60062d | ||
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luni
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73d374401b |
@@ -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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python merge_data.py -fi '/home/workspace/data/dataset/train' -fo '/home/workspace/local-data/train' -s 0 -e 1500
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nohup python auto_encoder_curv.py --name="lhunor_curv_raw_r2" > nohup/review/curv_raw_r2.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_raw_r2" > nohup/review/abl_all_raw_r2.out 2>&1 &
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nohup python evaluate_ae.py --name abl_all_raw_r2_70k > nohup/evaluate/abl_all_raw_r2_70k.out 2>&1 &
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nohup python calculate_error.py --name error_abl_all_raw_r2 > nohup/error/abl_all_raw_r2_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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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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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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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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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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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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cp configs/config_abl_all.py configs/config_abl_abs.py
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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 &
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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 &
|
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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 &
|
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nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_skirt > nohup/evaluate_ae_abl_pyr_32_skirt.out 2>&1 &
|
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nohup python evaluate_ae.py --name abl_norm_skirt_70k > nohup/evaluate/abl_norm_skirt_70k.out 2>&1 &
|
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nohup python calculate_error.py --name error_abl_2_pyr_32_64_128_skirt > nohup/error_ae_abl_skin_skirt_70k.out 2>&1 &
|
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|
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nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_100k > nohup/evaluate/abl_2_pyr_32_64_100k.out 2>&1 &
|
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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 &
|
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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 &
|
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|
||||
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
|
||||
|
||||
|
||||
+22
-29
@@ -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')
|
||||
@@ -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'])
|
||||
@@ -100,22 +102,18 @@ 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))
|
||||
ce_uv_loss = tf.reduce_mean(tf.abs(batch['uv_curvature'] - pred))
|
||||
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(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)
|
||||
ce_mesh_loss = tf.reduce_mean(tf.abs(batch['mesh_curvature'] - mesh_pred[:,:,0]))
|
||||
|
||||
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 }
|
||||
return { 'train/loss': loss, 'train/uv_l1': ce_uv_loss, 'train/ssim': ms_ssim,
|
||||
'train/mesh_l1': ce_mesh_loss }
|
||||
|
||||
|
||||
|
||||
@@ -123,35 +121,30 @@ def train_step(batch):
|
||||
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))
|
||||
ce_uv_loss = tf.reduce_mean(tf.abs(batch['uv_curvature'] - pred))
|
||||
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(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)
|
||||
ce_mesh_loss = tf.reduce_mean(tf.abs(batch['mesh_curvature'] - mesh_pred[:,:,0]))
|
||||
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 }
|
||||
return { 'valid/loss': loss, 'valid/uv_l1': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mesh_l1': ce_mesh_loss }
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
orig_img = tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.dtypes.int32)
|
||||
pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
|
||||
orig_img = batch['uv_curvature']
|
||||
pred_img = pred
|
||||
img = tf.concat((orig_img, pred_img), axis=2).numpy()
|
||||
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
||||
color_img = np.zeros((img.shape[0], img.shape[1], img.shape[2], 3), np.uint8)
|
||||
for i in range(7):
|
||||
color_img[img == i, :] = colors[i]
|
||||
color_img = [wandb.Image(color_img[i]) for i in range(4)]
|
||||
# colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
|
||||
# color_img = np.zeros((img.shape[0], img.shape[1], img.shape[2], 3), np.uint8)
|
||||
# for i in range(7):
|
||||
# color_img[img == i, :] = colors[i]
|
||||
# color_img = [wandb.Image(color_img[i]) for i in range(4)]
|
||||
|
||||
imgs = { type + '/images': [wandb.Image(img[i,:,:,np.newaxis] / 6.0) for i in range(img.shape[0])], type + '/color_images': color_img }
|
||||
imgs = { type + '/images': [wandb.Image((img[i,:,:,np.newaxis]-img[i,:,:,np.newaxis].min()) / (img[i,:,:,np.newaxis].max()-img[i,:,:,np.newaxis].min())) for i in range(img.shape[0])], } # type + '/color_images': color_img
|
||||
|
||||
return imgs
|
||||
|
||||
@@ -206,7 +199,7 @@ 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())
|
||||
|
||||
@@ -19,9 +19,10 @@ 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'] = '1'
|
||||
config['name'] = 'al_all_post'
|
||||
config['gpu'] = '3'
|
||||
config['name'] = 'abl_all_raw_r2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_2']
|
||||
|
||||
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']:
|
||||
@@ -45,7 +46,7 @@ config['test_dir'] = '/home/workspace/data/dataset/test_ready'
|
||||
|
||||
config['static_model'] = 'checkpoints/post/static_post_2'
|
||||
config['body_model'] = 'checkpoints/post/body_post_2'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_post_1'
|
||||
config['curvature_model'] = 'checkpoints/post/curv_raw_r2'
|
||||
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
|
||||
@@ -31,22 +31,18 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
|
||||
config['cond_ae']['activation'] = 'tanh'
|
||||
|
||||
|
||||
config['gpu'] = '3'
|
||||
config['gpu'] = '1'
|
||||
config['lr'] = 0.001
|
||||
config['name'] = 'curv_post_1'
|
||||
config['notes'] = 'convnext + convnext, | \
|
||||
\
|
||||
| \
|
||||
deeper model | \
|
||||
| \
|
||||
'
|
||||
config['name'] = 'curv_raw_r2'
|
||||
config['notes'] = ''
|
||||
config['tags'] = ['review_2']
|
||||
|
||||
config['cond_ae']['upscale_output'] = False
|
||||
config['pretrained_model'] = ''
|
||||
|
||||
|
||||
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
'uv_curvature': (512, 256, 1),
|
||||
'mesh_curvature': (config['smpl_res'])
|
||||
}
|
||||
config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
|
||||
|
||||
+18
-15
@@ -266,7 +266,9 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
|
||||
|
||||
def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
|
||||
batch = values.shape[0]
|
||||
values = (values + 1) * mask
|
||||
x = np.zeros((batch,14475))
|
||||
x[:,mask] = values
|
||||
values = x
|
||||
values_uv = np.zeros((batch, res * 2, res))
|
||||
missing = np.zeros((res * 2, res))
|
||||
uv_values = np.zeros((batch, uv_map.shape[0]))
|
||||
@@ -314,7 +316,8 @@ def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=Non
|
||||
final = []
|
||||
for i in range(batch):
|
||||
print('processing frame: ' + str(i))
|
||||
interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
|
||||
# interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
|
||||
interpolator = spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values[i,:], neighbors=neighbours, kernel='linear')
|
||||
final.append(interpolator(rbf_dest_current))
|
||||
final = np.asarray(final)
|
||||
|
||||
@@ -450,7 +453,7 @@ if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/train')
|
||||
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=10)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=1)
|
||||
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
|
||||
parser.add_argument("-sk", "--skirt", help = "end of range", default=0)
|
||||
args = parser.parse_args()
|
||||
@@ -526,17 +529,17 @@ if __name__ == '__main__':
|
||||
offsets_static = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
|
||||
body_static = sio.loadmat(os.path.join(garment_dir, 'info_body.mat'))['B']
|
||||
mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
|
||||
body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
|
||||
# body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
|
||||
# gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
|
||||
# curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
|
||||
curvature = np.load(os.path.join(folder, id, 'curvature_25_raw.npy'))
|
||||
# mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
|
||||
# mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# mesh_unposed_no_shape = np.stack([mesh_unposed_no_shape[i]['mesh_unposed_no_shape'] for i in range(gt.shape[0])])
|
||||
# body_no_orient = [np.load(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
|
||||
mesh_unposed_no_shape_one_hot = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(body.shape[0])]
|
||||
mesh_unposed_no_shape_one_hot = np.stack([mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(body.shape[0])])
|
||||
# mesh_unposed_no_shape_one_hot = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(body.shape[0])]
|
||||
# mesh_unposed_no_shape_one_hot = np.stack([mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(body.shape[0])])
|
||||
|
||||
|
||||
verts_static = (body_static + offsets_static) # * mask[:, np.newaxis]
|
||||
@@ -608,10 +611,10 @@ if __name__ == '__main__':
|
||||
# uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
uv_curvature_raw = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
|
||||
print(time.perf_counter() - start)
|
||||
|
||||
@@ -632,9 +635,9 @@ if __name__ == '__main__':
|
||||
# for i in range(uv_gt.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
|
||||
# for i in range(uv_curvature.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
|
||||
os.makedirs(os.path.join(folder, id, 'curvature_uv_raw'), exist_ok=True)
|
||||
for i in range(uv_curvature_raw.shape[0]):
|
||||
np.savez_compressed(os.path.join(folder, id, 'curvature_uv_raw', garment + '_' + str(i) + '.npz'), curvature_uv_raw=uv_curvature_raw[i, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
|
||||
# for i in range(uv_unposed.shape[0]):
|
||||
@@ -644,9 +647,9 @@ if __name__ == '__main__':
|
||||
# for i in range(uv_unposed_no_shape.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape[i, :, :, :])
|
||||
|
||||
os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot'), exist_ok=True)
|
||||
for i in range(uv_unposed_no_shape_one_hot.shape[0]):
|
||||
np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape_one_hot[i, :, :, :])
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot'), exist_ok=True)
|
||||
# for i in range(uv_unposed_no_shape_one_hot.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape_one_hot[i, :, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_shaped_body'), exist_ok=True)
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_shaped_body.npz'), uv_shaped_body=uv_shaped_body)
|
||||
|
||||
+12
-12
@@ -416,7 +416,7 @@ def test(verts, faces, values):
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/train')
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/local-data/train')
|
||||
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=10)
|
||||
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
|
||||
@@ -499,15 +499,15 @@ if __name__ == '__main__':
|
||||
mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
|
||||
# body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
|
||||
# gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
|
||||
# curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
|
||||
curvature = np.load(os.path.join(folder, id, 'curvature_raw.npy'))
|
||||
# mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
|
||||
# mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# mesh_unposed_no_shape = np.stack([mesh_unposed_no_shape[i]['mesh_unposed_no_shape'] for i in range(gt.shape[0])])
|
||||
# body_no_orient = [np.load(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
|
||||
mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(frames)]
|
||||
mesh_unposed_no_shape_one_hot = np.stack([mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)])
|
||||
# mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(frames)]
|
||||
# mesh_unposed_no_shape_one_hot = np.stack([mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)])
|
||||
|
||||
|
||||
# verts_dynamic = gt
|
||||
@@ -561,10 +561,10 @@ if __name__ == '__main__':
|
||||
# uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
uv_curvature_raw = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
|
||||
print(time.perf_counter() - start)
|
||||
|
||||
@@ -585,9 +585,9 @@ if __name__ == '__main__':
|
||||
# for i in range(uv_gt.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
|
||||
# for i in range(uv_curvature.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
|
||||
os.makedirs(os.path.join(folder, id, 'curvature_uv_raw'), exist_ok=True)
|
||||
for i in range(uv_curvature_raw.shape[0]):
|
||||
np.savez_compressed(os.path.join(folder, id, 'curvature_uv_raw', garment + '_' + str(i) + '.npz'), curvature_uv_raw=uv_curvature_raw[i, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
|
||||
# for i in range(uv_unposed.shape[0]):
|
||||
@@ -597,9 +597,9 @@ if __name__ == '__main__':
|
||||
# for i in range(uv_unposed_no_shape.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape[i, :, :, :])
|
||||
|
||||
os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot'), exist_ok=True)
|
||||
for i in range(uv_unposed_no_shape_one_hot.shape[0]):
|
||||
np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape_one_hot[i, :, :, :])
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot'), exist_ok=True)
|
||||
# for i in range(uv_unposed_no_shape_one_hot.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape_one_hot[i, :, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_shaped_body'), exist_ok=True)
|
||||
# np.savez_compressed(os.path.join(folder, id, 'uv_shaped_body.npz'), uv_shaped_body=uv_shaped_body)
|
||||
|
||||
@@ -202,7 +202,9 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
|
||||
|
||||
def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
|
||||
batch = values.shape[0]
|
||||
values = (values + 1) * mask
|
||||
x = np.zeros((batch,14475))
|
||||
x[:,mask] = values
|
||||
values = x
|
||||
values_uv = np.zeros((batch, res * 2, res))
|
||||
missing = np.zeros((res * 2, res))
|
||||
uv_values = np.zeros((batch, uv_map.shape[0]))
|
||||
@@ -250,7 +252,8 @@ def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=Non
|
||||
final = []
|
||||
for i in range(batch):
|
||||
print('processing frame: ' + str(i))
|
||||
interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
|
||||
interpolator = spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values[i,:], neighbors=neighbours, kernel='linear')
|
||||
# interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
|
||||
final.append(interpolator(rbf_dest_current))
|
||||
final = np.asarray(final)
|
||||
|
||||
@@ -384,16 +387,17 @@ def test(verts, faces, values):
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/local-data/valid')
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/local-data/train')
|
||||
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=1500)
|
||||
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
|
||||
parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
|
||||
parser.add_argument("-sk", "--skirt", help = "end of range", default=0)
|
||||
args = parser.parse_args()
|
||||
|
||||
# smpl = SMPL('FeatureLines/superSMPL')
|
||||
|
||||
local_folder = args.folder
|
||||
remote_folder = '/home/workspace/data/dataset/test'
|
||||
os.makedirs(os.path.join(local_folder, 'uv_posed_no_orient'), exist_ok=True)
|
||||
# os.makedirs(os.path.join(local_folder, 'uv_unposed_no_shape_one_hot_2'), exist_ok=True)
|
||||
|
||||
@@ -461,7 +465,7 @@ if __name__ == '__main__':
|
||||
mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
|
||||
# body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
|
||||
# gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
|
||||
# curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
|
||||
curvature = np.load(os.path.join(remote_folder, id, 'curvature_25_raw.npy'))
|
||||
# mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
# mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
|
||||
# mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
|
||||
@@ -470,8 +474,8 @@ if __name__ == '__main__':
|
||||
# body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
|
||||
# mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(frames)]
|
||||
# mesh_unposed_no_shape_one_hot = [mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)]
|
||||
mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient', id + '_' + str(i) + '.npz')) for i in range(frames)]
|
||||
mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
|
||||
# mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient', id + '_' + str(i) + '.npz')) for i in range(frames)]
|
||||
# mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
|
||||
|
||||
|
||||
# verts_dynamic = gt
|
||||
@@ -491,11 +495,11 @@ if __name__ == '__main__':
|
||||
# uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
uv_curvature_raw = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
||||
# uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
|
||||
uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
|
||||
# uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
|
||||
# uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
|
||||
print(time.perf_counter() - start)
|
||||
|
||||
@@ -516,9 +520,9 @@ if __name__ == '__main__':
|
||||
# for i in range(uv_gt.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
|
||||
# for i in range(uv_curvature.shape[0]):
|
||||
# np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
|
||||
os.makedirs(os.path.join(local_folder, 'curvature_uv_raw'), exist_ok=True)
|
||||
for i in range(uv_curvature_raw.shape[0]):
|
||||
np.savez_compressed(os.path.join(local_folder, 'curvature_uv_raw', id + '_' + str(i) + '.npz'), curvature_uv_raw=uv_curvature_raw[i, :, :])
|
||||
|
||||
# os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
|
||||
# for i in range(uv_unposed.shape[0]):
|
||||
|
||||
@@ -338,7 +338,7 @@ class ConvNeXtReverseLabels(tf.keras.Model):
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
|
||||
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], kernel_size=7,
|
||||
drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
|
||||
nr_of_classes=7):
|
||||
super().__init__()
|
||||
@@ -360,14 +360,14 @@ class ConvNeXtReverseLabels(tf.keras.Model):
|
||||
cur = 0
|
||||
for i in range(len(depths)):
|
||||
stage = tf.keras.Sequential(
|
||||
[Block(dim=dims[i], drop_path=dp_rates[cur + j],
|
||||
[Block(dim=dims[i], drop_path=dp_rates[cur + j], kernel_size=kernel_size,
|
||||
layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
|
||||
)
|
||||
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(1, activation='sigmoid')
|
||||
|
||||
def call(self, x, training):
|
||||
for i in reversed(range(len(self.downsample_layers))):
|
||||
|
||||
+6
-5
@@ -275,6 +275,7 @@ def read_raw_curvatures(folder):
|
||||
continue
|
||||
|
||||
curv = np.load(garment_dir + '/curvature_25_raw.npy')
|
||||
print(curv.max())
|
||||
curvatures_all.append(curv)
|
||||
|
||||
return curvatures_all
|
||||
@@ -282,24 +283,24 @@ def read_raw_curvatures(folder):
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test')
|
||||
parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/train')
|
||||
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=1500)
|
||||
parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
|
||||
args = parser.parse_args()
|
||||
|
||||
F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
|
||||
# F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
|
||||
# k_neighbours(k=25)
|
||||
|
||||
folder = args.folder
|
||||
nr_of_labels = 6
|
||||
neighbourhood = 25
|
||||
temp_file = 'temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
|
||||
dist = np.load('MeshUtils/dijsktra.npy')
|
||||
# dist = np.load('MeshUtils/dijsktra.npy')
|
||||
# dist = np.load('MeshUtils/dijkstra_skirt.npy')
|
||||
|
||||
generate_curvatures(folder, neighbourhood)
|
||||
# curvatures = read_raw_curvatures(folder)
|
||||
# generate_curvatures(folder, neighbourhood)
|
||||
curvatures = read_raw_curvatures(folder)
|
||||
# bins = get_bins(curvatures, nr_of_labels)
|
||||
# np.save('train_bins_skirt.npy', bins)
|
||||
# bins = np.load('train_bins.npy')
|
||||
|
||||
@@ -45,7 +45,7 @@ from models.cvae_model import CVAENew as CVAE
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb')
|
||||
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('cvae.py')
|
||||
artifact.add_file('models/cvae_model.py')
|
||||
@@ -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:
|
||||
@@ -476,7 +478,7 @@ def test():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init_wandb()
|
||||
init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
+13
-14
@@ -176,7 +176,7 @@ class DatasetFactory():
|
||||
elif key == 'frame_nr':
|
||||
tf_signature.append(tf.TensorSpec(shape = (), dtype = tf.string))
|
||||
elif key == 'mesh_curvature':
|
||||
tf_signature.append(tf.TensorSpec(shape = signature[key], dtype = tf.int32))
|
||||
tf_signature.append(tf.TensorSpec(shape = signature[key], dtype = tf.float32))
|
||||
else:
|
||||
tf_signature.append(tf.TensorSpec(shape = signature[key], dtype = tf.float32))
|
||||
return tf_signature
|
||||
@@ -260,8 +260,8 @@ 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']
|
||||
item['mesh_curvature'] = mesh_curvature.astype(np.int32)
|
||||
mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature_raw', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature_raw']
|
||||
item['mesh_curvature'] = mesh_curvature
|
||||
|
||||
|
||||
for res in resolutions:
|
||||
@@ -270,13 +270,13 @@ 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'));
|
||||
uv_curvature = np.load(os.path.join(folder, 'curvature_uv_raw' + res_string, id + separator + frame_nr + '.npz'));
|
||||
if res_string != '':
|
||||
uv_curvature = uv_curvature['uv_curvature']
|
||||
else:
|
||||
uv_curvature = uv_curvature['curvature_uv']
|
||||
uv_curvature = tf.one_hot(uv_curvature, 7)
|
||||
item['uv_curvature' + res_string] = uv_curvature
|
||||
uv_curvature = uv_curvature['curvature_uv_raw']
|
||||
# uv_curvature = tf.one_hot(uv_curvature, 7)
|
||||
item['uv_curvature' + res_string] = uv_curvature[:,:,tf.newaxis]
|
||||
|
||||
if 'uv_unposed' + res_string in signature.keys():
|
||||
uv_unposed_no_shape = np.load(os.path.join(folder, 'uv_unposed_no_shape' + one_hot_2 + res_string, id + separator + frame_nr + '.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed_no_shape']
|
||||
@@ -370,8 +370,8 @@ if __name__ == '__main__':
|
||||
# 'mesh_unposed': (14475, 3,),
|
||||
'uv_unposed': (512, 256, 3),
|
||||
# 'uv_unposed_32': (3, 32, 16,),
|
||||
'uv_curvature': (512, 256, 7),
|
||||
# 'mesh_curvature': (14475)
|
||||
'uv_curvature': (512, 256, 1),
|
||||
'mesh_curvature': (14475)
|
||||
}
|
||||
signature_static = { #'uv_static': (3, 512, 256,),
|
||||
# 'uv_static_32': (3, 32, 16,),
|
||||
@@ -381,15 +381,14 @@ 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/local-data/train',
|
||||
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',
|
||||
is_cape=False,)
|
||||
static_dataset = StaticDataset(folder='/home/workspace/local-data/train',
|
||||
signature=signature_static,
|
||||
resolutions=[512],
|
||||
is_cape=False)
|
||||
@@ -404,7 +403,7 @@ if __name__ == '__main__':
|
||||
for i, batch in enumerate(dataset):
|
||||
print(i)
|
||||
batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic))
|
||||
ex.imshow(batch['uv_curvature'][0].numpy().argmax(-1) / 6.0).show()
|
||||
ex.imshow(batch['uv_curvature'][0,:,:,0].numpy()).show()
|
||||
sum_uv += batch['uv_unposed'].numpy().sum(axis=0)
|
||||
sum_mask += batch['uv_mask'].numpy().sum(axis=0)
|
||||
|
||||
|
||||
+6
-4
@@ -179,7 +179,7 @@ if __name__ == '__main__':
|
||||
start = time.perf_counter()
|
||||
# offsets_static = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
|
||||
# body_static = sio.loadmat(os.path.join(garment_dir, 'info_body.mat'))['B']
|
||||
# mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
|
||||
mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
|
||||
# verts_static = (body_static + offsets_static) * mask[:, np.newaxis]
|
||||
# # dfassert verts_static.min() > -1 and verts_static.max() < 1
|
||||
|
||||
@@ -257,10 +257,12 @@ if __name__ == '__main__':
|
||||
# for i in range(body_posed.shape[0]):
|
||||
# shutil.copyfile(os.path.join(garment_dir, 'curvature_uv', garment + '_' + str(i) + '.npz'), os.path.join(output_folder, 'uv_curvature', id + '_' + str(i) + '.npz'))
|
||||
|
||||
os.makedirs(os.path.join(output_folder, 'mesh_curvature'), exist_ok=True)
|
||||
mesh_curvature = np.load(os.path.join(garment_dir, 'curvature_label_6_25.npy'))
|
||||
os.makedirs(os.path.join(output_folder, 'mesh_curvature_raw'), exist_ok=True)
|
||||
data = np.load(os.path.join(garment_dir, 'curvature_25_raw.npy'))
|
||||
mesh_curvature_raw = np.zeros((data.shape[0], 14475), dtype=np.float32)
|
||||
mesh_curvature_raw[:, mask] = data
|
||||
for i in range(pose.shape[1]):
|
||||
np.savez_compressed(os.path.join(output_folder, 'mesh_curvature', id + '_' + str(i) + '.npz'), mesh_curvature=mesh_curvature[i])
|
||||
np.savez_compressed(os.path.join(output_folder, 'mesh_curvature_raw', id + '_' + str(i) + '.npz'), mesh_curvature_raw=mesh_curvature_raw[i])
|
||||
|
||||
|
||||
# os.makedirs(os.path.join(output_folder, 'uv_posed_body_no_orient'), exist_ok=True)
|
||||
|
||||
+1
-7
@@ -190,13 +190,7 @@ class DecoderNew(tf.keras.Model):
|
||||
|
||||
x = self.decoder(x, training=training)
|
||||
|
||||
if self.is_label:
|
||||
x = x * mask[:,:,:,tf.newaxis]
|
||||
|
||||
background = tf.ones((mask.shape[0],512,256,1)) * (1 - mask[:,:,:,tf.newaxis])
|
||||
x = tf.concat((background, x), axis=-1)
|
||||
|
||||
x = tf.math.softmax(x)
|
||||
# if self.is_label:
|
||||
|
||||
if len(inputs) == 2:
|
||||
if len(mask.shape) == 2:
|
||||
|
||||
+7
-7
@@ -4,24 +4,24 @@ import os
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
folder = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
# folder = '/home/workspace/data/dataset/test_skirt_ready_2'
|
||||
# folder = '/home/workspace/local-data/cape_processed/train/male'
|
||||
# folder = '/home/workspace/local-data/valid_skirt'
|
||||
# folder = '/home/workspace/local-data/valid'
|
||||
# ids = os.listdir(folder)
|
||||
ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
|
||||
folder = '/home/workspace/data/dataset/test_ready'
|
||||
ids = os.listdir(folder)
|
||||
# ids = os.listdir('/home/workspace/data/dataset/validation')#[280000:]
|
||||
skirt = 0
|
||||
|
||||
count = 0
|
||||
step = 2000
|
||||
step = 25
|
||||
|
||||
# !!!!!!! change skirt parameter in script
|
||||
|
||||
while count < len(ids):
|
||||
while count < 400:
|
||||
# os.system('nohup python convert_to_uv_cape.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_curv_raw_test_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_curv_raw_test_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_valid_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_valid_skirt_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python downscale.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + ' > nohup/downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
|
||||
Reference in New Issue
Block a user