convert test set to raw uv as well

README.md

@@ -1,49 +0,0 @@
-# stylegan2-tf-2.x
-* This is tensorflow 2.0 based keras subclassing reimplementation of official [StyleGAN2 Repo](https://github.com/NVlabs/stylegan2)
-
-## Inference from official weights
-* Please check [how_to_extract_official_weights.md](./how_to_extract_official_weights.md)
-
-| Official repo result | Official weight copied result from this repo |
-| :---: | :---: |
-| ![official_result] | ![restored_result] |
-
-## Training on 256x256
-* batch size 32
-* around 300k steps
-
-| d_loss | g_loss |
-| :---: | :---: |
-| ![256x256_d_loss]| ![256x256_g_loss]|
-
-* result samples
-
-| | | |
-| :---: | :---: | :---: |
-| ![256x256_result_0]| ![256x256_result_1]| ![256x256_result_2]|
-
-## Using custom data
-* Create dataset(tf.data.Dataset) loader which returns 
-  * shape: `[None, 3, res, res]` where `res` is desired image resolution
-  * image value range: `[-1, +1]`
- 
-## Etc
-### Using with pycharm
-* If .bashrc file's paths don't work in pycharm environment (e.g. `nvcc -h`), open pycharm with following
-```bash
-# move to pycharm installed location (location may vary)
-moono@moono-ubuntu:~$ cd .local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin/
-
-# launch pycharm with .bashrc 
-moono@moono-ubuntu:~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin$ bash pycharm.sh
-```
-
-[loss_tensorboard]: assets/tf-keras-stylegan2-loss.PNG
-[generation_tensorboard]: assets/tf-keras-stylegan2-fake-images.PNG
-[official_result]: assets/seed6600-official.png
-[restored_result]: assets/seed6600-restored.png
-[256x256_d_loss]: assets/d_loss_256x256.png
-[256x256_g_loss]: assets/g_loss_256x256.png
-[256x256_result_0]: assets/out_256x256_0.png
-[256x256_result_1]: assets/out_256x256_1.png
-[256x256_result_2]: assets/out_256x256_2.png

aaa_helpers.txt

@@ -0,0 +1,219 @@
+python merge_data.py -fi '/home/workspace/data/dataset/train' -fo '/home/workspace/local-data/train' -s 0 -e 1500
+nohup python auto_encoder_curv.py --name="lhunor_curv_raw_r2" > nohup/review/curv_raw_r2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_raw_r2" > nohup/review/abl_all_raw_r2.out 2>&1 &
+nohup python evaluate_ae.py --name abl_all_raw_r2_70k > nohup/evaluate/abl_all_raw_r2_70k.out 2>&1 &
+nohup python calculate_error.py --name error_abl_all_raw_r2 > nohup/error/abl_all_raw_r2_70k.out 2>&1 &
+
+
+one final convolutional layer for smoothing, on the final coordinates
+
+skirts!!!!
+pyramid next week
+
+basic final, basic final skirts,
+ basic pyramid
+ 
+ 
+removing curvatures test in ablation
+
+nohup python cvae_low.py --name="lhunor_cvae_low_4_3_skirt_d32" > nohup/nohup_cvae_low_4_3_skirt_d32.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_ae_body_post_2" > nohup/post/ae_body_2.out 2>&1 &
+nohup python auto_encoder_static.py --name="lhunor_ae_static_post_4" > nohup/post/ae_static_4.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_post_1" > nohup/post/ae_curv_1.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_pyr_64_128_3" > nohup/post/abl_pyr_64_128_3.out 2>&1 &
+
+nohup python cvae_pyramid.py --name="lhunor_abl_pyr_32_64" > nohup/post/abl_pyr_32_64.out 2>&1 &
+
+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 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32" > nohup/ablation/pyr_abl_32.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_2" > nohup/ablation/pyr_abl_32_2.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skin" > nohup/ablation/pyr_abl_32_skin.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skin_offset" > nohup/ablation/pyr_abl_32_skin_offset.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skirt" > nohup/ablation/pyr_abl_32_skirt.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_extra_loss_skirt" > nohup/ablation/pyr_abl_32_extra_loss_skirt.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_skirt_2" > nohup/ablation/pyr_abl_32_skirt_2.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64" > nohup/ablation/pyr_abl_32_64.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_2" > nohup/ablation/pyr_abl_32_64_2.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_skirt" > nohup/ablation/pyr_abl_32_64_skirt.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_skirt_2" > nohup/ablation/pyr_abl_32_64_skirt_2.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_m" > nohup/ablation/pyr_abl_64_cape_m.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_f" > nohup/ablation/pyr_abl_64_cape_f.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_cape_f_2" > nohup/ablation/pyr_abl_64_cape_f_2.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_cape_f" > nohup/ablation/pyr_abl_64_128_cape_f.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_64_128_cape_m" > nohup/ablation/pyr_abl_64_128_cape_m.out 2>&1 &
+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 &
+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 &
+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 &
+nohup python upscale_test.py --name="lhunor_256_512_skirt" > nohup/up_256_512_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_cvae_v2_7_4_3" > nohup/cvae_v2_7_4_3.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_extra_loss" > nohup/ablation/abl_extra_loss.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_extra_loss_skirt" > nohup/ablation/abl_extra_loss_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_extra_loss_skirt_2" > nohup/ablation/abl_extra_loss_skirt_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_curv" > nohup/ablation/abl_curv.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_curv_skirt" > nohup/ablation/abl_curv_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_abs" > nohup/ablation/abl_abs.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_abs_skirt" > nohup/ablation/abl_abs_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin" > nohup/ablation/abl_skin.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin_norm" > nohup/ablation/abl_skin_norm.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin_skirt" > nohup/ablation/abl_skin_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all" > nohup/ablation/abl_all.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_skirt" > nohup/ablation/abl_all_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_orient" > nohup/ablation/abl_orient.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_orient_skirt" > nohup/ablation/abl_orient_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_one_hot_skirt" > nohup/ablation/abl_one_hot_skirt.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_norm" > nohup/ablation/abl_norm.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_10p" > nohup/ablation/abl_10p.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_25p" > nohup/ablation/abl_25p.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_10p_skin" > nohup/ablation/abl_10p_skin.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_25p_skin" > nohup/ablation/abl_25p_skin.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_50p_skin" > nohup/ablation/abl_50p_skin.out 2>&1 &
+
+nohup python cvae.py --name="lhunor_abl_skin_var" > nohup/ablation/abl_skin_var.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin_var_2" > nohup/ablation/abl_skin_var_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin_var_3" > nohup/ablation/abl_skin_var_3.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_skin_var_4" > nohup/ablation/abl_skin_var_4.out 2>&1 &
+
+nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/ablation/cvae_curv.out 2>&1 &
+nohup python cvae_curv.py --name="lhunor_cvae_curv_2" > nohup/ablation/cvae_curv_2.out 2>&1 &
+
+
+abl_2_pyr_32_64_128
+abl_2_pyr_64_cape_f_03223_SS
+abl_2_pyr_32_64_128_256_512
+
+nohup python cvae.py --name="lhunor_cape_f_03223_SL_2" > nohup/ablation/cape_f_03223_SL_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_cape_f_03223_SS_2" > nohup/ablation/cape_f_03223_SS_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_post" > nohup/post/abl_all_post.out 2>&1 &
+	
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32" > nohup/ablation_2/abl_2_pyr_32.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skin" > nohup/ablation_2/abl_2_pyr_32_skin.out 2>&1 &
+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 &
+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 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_64" > nohup/ablation_2/abl_2_pyr_32_64.out 2>&1 &
+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 &
+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 &
+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 &
+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 &
+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 &
+
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_32_skirt" > nohup/ablation_2/abl_2_pyr_32_skirt.out 2>&1 &
+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 &
+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 &
+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 &
+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 &
+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 &
+
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_cape_f" > nohup/ablation_2/abl_2_pyr_64_cape_f.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_cape_f.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_256_cape_f.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_512_cape_f" > nohup/ablation_2/abl_2_pyr_64_128_256_512_cape_f.out 2>&1 &
+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 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_cape_m" > nohup/ablation_2/abl_2_pyr_64_128_cape_m.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_abl_2_pyr_64_128_256_cape_m" > nohup/ablation_2/abl_2_pyr_64_128_256_cape_m.out 2>&1 &
+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 &
+
+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 &
+
+abl_2_all_skirt
+abl_2_pyr_64_cape_m_00032_SL
+
+python convert_to_uv.py -f '/home/workspace/data/dataset/validation' -s 0 -e 150 -n "lhunor"
+python convert_to_uv.py -f '/home/workspace/data/dataset/skirt_test' -s 0 -e 1 -n "lhunor"
+
+cp configs/config_abl_all.py configs/config_abl_abs.py
+
+
+python merge_data.py -fi '/home/workspace/data/dataset/test' -fo '/home/workspace/data/dataset/test_ready' -s 0 -e 1500 
+python merge_data.py -fi '/home/workspace/data/dataset/valid_skirt' -fo '/home/workspace/local-data/valid_skirt' -s 0 -e 1500
+
+nohup python upscale_test.py  > nohup/u.out 2>&1 &
+nohup python unpose_one_hot.py  > nohup/unpose_one_hot_train_skirt.out 2>&1 &
+nohup python preprocessing.py  > nohup/unshape_one_hot_test_skirt.out 2>&1 &
+nohup python dataset.py  > nohup/avg_skirt.out 2>&1 &
+nohup python evaluate_ae_support.py  > nohup/evaluate_ae_support.out 2>&1 &
+nohup python evaluate_cape.py --name evaluate_abl_pyr_64_128_256_512_cape_f > nohup/evaluate_abl_pyr_64_128_256_512_cape_f.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_64_110k > nohup/evaluate_ae_abl_pyr_32_64_110k.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_64_2_50k > nohup/evaluate_ae_abl_pyr_32_64_2_50k.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_skirt > nohup/evaluate_ae_abl_pyr_32_skirt.out 2>&1 &
+nohup python evaluate_ae.py --name abl_norm_skirt_70k > nohup/evaluate/abl_norm_skirt_70k.out 2>&1 &
+nohup python calculate_error.py --name error_abl_2_pyr_32_64_128_skirt > nohup/error_ae_abl_skin_skirt_70k.out 2>&1 &
+
+nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_100k > nohup/evaluate/abl_2_pyr_32_64_100k.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_80k > nohup/evaluate/abl_2_pyr_32_64_128_80k.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_256_512_skirt_50k > nohup/evaluate/abl_2_pyr_32_64_128_256_512_skirt_50k.out 2>&1 &
+
+nohup python calculate_error.py --name abl_2_pyr_32_64_128_256_512_50k > nohup/error/abl_2_pyr_32_64_128_256_512_50k.out 2>&1 &
+
+nohup 7z x CAPE_dataset.zip.001 > nohup/unzip.out 2>&1 &
+
+nohup python main.py --config configs/CAPE-affineconv_nz18_pose24_clotype8_male.yaml --name CAPE-affineconv_nz18_pose24_clotype8_male --gender male --mode demo  > cape_male.out 2>&1 &
+nohup python3 main.py --config configs/CAPE-affineconv_nz18_pose24_clotype8_female.yaml --name CAPE-affineconv_nz18_pose24_clotype8_female --gender female --mode demo  > cape_female.out 2>&1 &
+
+nohup python curvature_labels.py > nohup/dijkstra_skirt.out 2>&1 &
+nohup python downscale.py > nohup/downscale_2_test_skirt.out 2>&1 &
+nohup python downscale_cape.py > nohup/downscale_cape_train_m.out 2>&1 &
+nohup python merge_data.py -fi /home/workspace/data/dataset/train -fo /home/workspace/local-data/train -s 0 -e 1500 > nohup/merge_data_valid_skirt.out 2>&1 &
+nohup python merge_data.py -fi /home/workspace/data/dataset/test_skirt -fo /home/workspace/data/dataset/test_skirt_ready -s 0 -e 1500 > nohup/merge_data_test_skirt_ready.out 2>&1 &
+
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 0 -e 100 -n lhunor_unpose_skirt_0_100 > nohup/unpose_skirt_0_100.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 100 -e 200 -n lhunor_unpose_skirt_100_200 > nohup/unpose_skirt_100_200.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 200 -e 300 -n lhunor_unpose_skirt_200_300 > nohup/unpose_skirt_200_300.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 300 -e 400 -n lhunor_unpose_skirt_300_400 > nohup/unpose_skirt_300_400.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 400 -e 500 -n lhunor_unpose_skirt_400_500 > nohup/unpose_skirt_400_500.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 500 -e 600 -n lhunor_unpose_skirt_500_600 > nohup/unpose_skirt_500_600.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 600 -e 700 -n lhunor_unpose_skirt_600_700 > nohup/unpose_skirt_600_700.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/train_skirt -s 700 -e 800 -n lhunor_unpose_skirt_700_800 > nohup/unpose_skirt_700_800.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test_skirt -s 0 -e 100 -n lhunor_unpose_test_skirt_0_100 > nohup/unpose_test_skirt_0_100_2.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test_skirt -s 100 -e 200 -n lhunor_unpose_test_skirt_100_200 > nohup/unpose_test_skirt_100_200_2.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 0 -e 100 -n lhunor_unpose_extra_skirt_0_100 > nohup/unpose_extra_skirt_0_100.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 100 -e 200 -n lhunor_unpose_extra_skirt_100_200 > nohup/unpose_extra_skirt_100_200.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 200 -e 300 -n lhunor_unpose_extra_skirt_200_300 > nohup/unpose_extra_skirt_200_300_2.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/cloth3d_skirt -s 300 -e 400 -n lhunor_unpose_extra_skirt_300_400 > nohup/unpose_extra_skirt_300_400.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 0 -e 100 -n lhunor_unpose_test_0_100 > nohup/unpose_test_0_100.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 100 -e 200 -n lhunor_unpose_test_100_200 > nohup/unpose_test_100_200.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 200 -e 300 -n lhunor_unpose_test_200_300 > nohup/unpose_test_200_300.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 300 -e 400 -n lhunor_unpose_test_300_400 > nohup/unpose_test_300_400.out 2>&1 &
+nohup python unpose_skirt.py -f /home/workspace/data/dataset/test -s 00 -e 00 -n lhunor_unpose_test_00_00 > nohup/unpose_test_00_00.out 2>&1 &
+
+
+calculate avg garment for skirt
+need dijkstra/dijsktra skirt
+bodies have to be converted in non-skirt mode
+need to pretrain encoders again...
+not enough sapce to preprocess data
+
+add one more layer and train, repeat
+
+curvature binning
+do it according to trainingn set distribution for validation, or it's own for validation
+merge curvatures for validation from data-net to local
+avg garment for big datasets
+check ranges of curvatur ranges
+
+collision in pecentage
+bend loss?
+evaluation pipeline
+look tailernot dataset
+blender for visualization
+
+
+gpu needed estimation
+let's say if we want to finish in on eweek
+
+we use the same skinning weights for skirts. okay?
+
+
+redo curvatures for skirt train.valid
+train_skirt: array([1.83511008e-06, 1.84299448e-03, 3.38148023e-03, 5.67459036e-03, 1.02263279e-02, 2.21962575e-02, 3.24614763e-01])
+             array([1.83511008e-06, 1.82755420e-03, 3.35242413e-03, 5.61911613e-03, 1.01151643e-02, 2.20824350e-02, 3.25994939e-01])
+valid_skirt: array([4.51388405e-06, 1.90253550e-03, 3.44673195e-03, 5.73266158e-03, 1.02412868e-02, 2.22220197e-02, 3.12285781e-01])
+             array([4.51388405e-06, 1.88061886e-03, 3.40514188e-03, 5.64374123e-03, 1.00161384e-02, 2.18187869e-02, 3.17445695e-01])
+test_skirt:  array([7.21150764e-06, 1.77989923e-03, 3.26598203e-03, 5.47702867e-03, 9.79944225e-03, 2.07518619e-02, 3.23069602e-01])
+
+how to include curvature in evaluation?
+use gt, predict with vae?
+
+
+docker run -d --rm -p 61208-61209:61208-61209 -e GLANCES_OPT="-w" -v /var/run/docker.sock:/var/run/docker.sock:ro --pid host nicolargo/glances:alpine-dev
+
+

auto_encoder_curv.py

@@ -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)
+        ce_uv_loss = tf.reduce_mean(tf.abs(batch['uv_curvature'] - pred))
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+        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 = tf.reduce_mean(tf.abs(batch['mesh_curvature'] - mesh_pred[:,:,0]))
-        ce_mesh_loss = scce(mesh_orig, mesh_pred)
 
         loss = ce_uv_loss + ce_mesh_loss
-
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
-    dice = dice_dense(pred, batch['uv_curvature'])
     
     gradients = tape.gradient(loss, model.trainable_variables)
     optimizer.apply_gradients(zip(gradients, model.trainable_variables))
-    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
+    return { 'train/loss': loss, 'train/uv_l1': ce_uv_loss, 'train/ssim': ms_ssim, 
-             'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
+             '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)
+    ce_uv_loss = tf.reduce_mean(tf.abs(batch['uv_curvature'] - pred))
-    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+    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 = tf.reduce_mean(tf.abs(batch['mesh_curvature'] - mesh_pred[:,:,0]))
-    ce_mesh_loss = scce(mesh_orig, mesh_pred)
     loss = ce_uv_loss + ce_mesh_loss
 
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+    return { 'valid/loss': loss, 'valid/uv_l1': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mesh_l1': ce_mesh_loss }
-    dice = dice_dense(pred, batch['uv_curvature'])
-
-    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
-             'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
         
 
 def plot_everything(batch: dict, type: str):
     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)
+    orig_img = batch['uv_curvature']
-    pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
+    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)])
+    # 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)
+    # color_img = np.zeros((img.shape[0], img.shape[1], img.shape[2], 3), np.uint8)
-    for i in range(7):
+    # for i in range(7):
-        color_img[img == i, :] = colors[i]
+    #     color_img[img == i, :] = colors[i]
-    color_img = [wandb.Image(color_img[i]) for i in range(4)]
+    # 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())

configs/config_abl_all_post.py

@@ -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['gpu'] = '3'
-config['name'] = 'al_all_post'
+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

configs/config_ae_curv_post.py

@@ -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['name'] = 'curv_raw_r2'
-config['notes'] = 'convnext + convnext, | \
+config['notes'] = '' 
- \
+config['tags'] = ['review_2']
-| \
-    deeper model | \
-| \
-    ' 
 
 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'],),

convert_to_uv.py

@@ -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.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.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)
+        os.makedirs(os.path.join(folder, id, 'curvature_uv_raw'), exist_ok=True)
-        # for i in range(uv_curvature.shape[0]):
+        for i in range(uv_curvature_raw.shape[0]):
-        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+            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)
+        # 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]):
+        # 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, :, :, :])
+        #     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)

convert_to_uv_local.py

@@ -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.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.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)
+        os.makedirs(os.path.join(folder, id, 'curvature_uv_raw'), exist_ok=True)
-        # for i in range(uv_curvature.shape[0]):
+        for i in range(uv_curvature_raw.shape[0]):
-        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+            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)
+        # 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]):
+        # 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, :, :, :])
+        #     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)

convert_to_uv_local_single.py

@@ -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 = [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 = [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)
+        os.makedirs(os.path.join(local_folder, 'curvature_uv_raw'), exist_ok=True)
-        # for i in range(uv_curvature.shape[0]):
+        for i in range(uv_curvature_raw.shape[0]):
-        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+            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]):

convnext/models/convnext_tf.py

@@ -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))):

curvature_labels.py

@@ -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)
+    # generate_curvatures(folder, neighbourhood)
-    # curvatures = read_raw_curvatures(folder)
+    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')

cvae.py

@@ -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())

dataset.py

@@ -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']
+                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.astype(np.int32)
+                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 = uv_curvature['curvature_uv_raw']
-                    uv_curvature = tf.one_hot(uv_curvature, 7)
+                    # uv_curvature = tf.one_hot(uv_curvature, 7)
-                    item['uv_curvature' + res_string] = uv_curvature
+                    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),
+                          'uv_curvature': (512, 256, 1),
-                        #   'mesh_curvature':  (14475)
+                          '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,
+                                  is_cape=False,)
-                                  frames_file='frames_dress_30k.txt')
+    static_dataset = StaticDataset(folder='/home/workspace/local-data/train',
-    static_dataset = StaticDataset(folder='/home/workspace/local-data/train_skirt',
                                    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)
 

merge_data.py

@@ -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)
+        os.makedirs(os.path.join(output_folder, 'mesh_curvature_raw'), exist_ok=True)
-        mesh_curvature = np.load(os.path.join(garment_dir, 'curvature_label_6_25.npy'))
+        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)

models/base.py

@@ -190,13 +190,7 @@ class DecoderNew(tf.keras.Model):
 
         x = self.decoder(x, training=training)
 
-        if self.is_label:
+        # 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 len(inputs) == 2:
             if len(mask.shape) == 2:

run_parallel.py

@@ -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'
+    folder = '/home/workspace/data/dataset/test_ready'
-    # ids = os.listdir(folder)
+    ids = os.listdir(folder)
-    ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
+    # 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 &')