calculate errors

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 +1,228 @@
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2_2" > nohup/review/curv_1_orig_curv_100_10p_r2_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/review/abl_all_10p_curv_25_r2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/review/abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_25_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/error/abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/error/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/error/abl_all_10p_curv_25_r2.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

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_ae_curv_post import config
+from configs.config_ae_curv_1_orig_curv_10_10p_r2_2 import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -31,13 +31,13 @@ import plotly.express as ex
 from tf_utils import check_tf_version, allow_memory_growth
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict
-from models.cond_encoder import AutoEncoderNew as AutoEncoder
+from models.cond_encoder import AutoEncoder
 
 
 testing = False
 def init_wandb():
     if not testing:
-        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
+        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/', tags=config['tags'])
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('losses.py')
@@ -47,7 +47,9 @@ def init_wandb():
         artifact.add_file('convnext/models/convnext_tf.py')
         config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
         for config_file_name in config_file_names:
-            artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+            file_name = '/'.join(config_file_name.split('.')) + '.py'
+            if os.path.exists(file_name):
+                artifact.add_file(file_name)
         run.log_artifact(artifact)
 
 uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -96,46 +98,73 @@ def dice_dense(prediction, ground_truth, weight_map=None):
 
 
 @tf.function
-def train_step(batch):
+def train_step(batch):	
-    with tf.GradientTape() as tape:
+    with tf.GradientTape() as tape:	
-        pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
+        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))	
+        loss = ce_uv_loss	
+    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
+    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
+    dice = dice_dense(pred, batch['uv_curvature'])	
+    	
+    gradients = tape.gradient(loss, model.trainable_variables)	
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))	
+    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/mean_iou_mesh': mean_iou_mesh, 'train/dice': dice }	
+        	
+@tf.function	
+def valid_step(batch):	
+    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)	
+    ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)	
+    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))	
+    loss = ce_uv_loss	
+    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
+    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
+    dice = dice_dense(pred, batch['uv_curvature'])	
+    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/mean_iou_mesh': mean_iou_mesh, 'valid/dice': dice }
 
-        ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
+# def train_step(batch):
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+#     with tf.GradientTape() as tape:
+#         pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
 
-        mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+#         ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
-        mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
+#         ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
-        ce_mesh_loss = scce(mesh_orig, mesh_pred)
 
-        loss = ce_uv_loss + ce_mesh_loss
+#         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)
 
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+#         loss = ce_uv_loss + ce_mesh_loss
-    dice = dice_dense(pred, batch['uv_curvature'])
+
+#     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)
+#     gradients = tape.gradient(loss, model.trainable_variables)
-    optimizer.apply_gradients(zip(gradients, 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/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 }
+#              'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
         
 
 
-@tf.function
+# @tf.function
-def valid_step(batch):
+# def valid_step(batch):
-    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
+#     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 = 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))
+#     ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
 
-    mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+#     mesh_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']
+#     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 = scce(mesh_orig, mesh_pred)
-    loss = ce_uv_loss + ce_mesh_loss
+#     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))
+#     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'])
+#     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,
+#     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 }
+#              'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
         
 
 def plot_everything(batch: dict, type: str):
@@ -206,7 +235,7 @@ def test():
 
 if __name__ == '__main__':
     init_wandb()
-    allow_memory_growth()
+    # allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -226,7 +255,8 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -240,7 +270,8 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'], 
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_25_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf
@@ -59,7 +59,7 @@ if __name__ == '__main__':
     folder = config['test_dir']
 
     checkpoint_name = config['name']
-    iteration = ''
+    iteration = '70000'
 
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()

configs/config_abl_all_10p_curv_100_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 100
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '1'
+config['name'] = 'abl_all_10p_curv_100_r2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_100_10p_r2_2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_10_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 10
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '6'
+config['name'] = 'abl_all_10p_curv_10_r2_2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_10_10p_r2_2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_25_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 25
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '0'
+config['name'] = 'abl_all_10p_curv_25_r2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_25_10p_r2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_pyr_32.py

@@ -1,7 +1,7 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32'
+config['name'] = 'pyr_abl_32_r1'
 config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE

configs/config_abl_pyr_32_skirt.py

@@ -1,8 +1,8 @@
 config = dict()
 
 config['gpu'] = '3'
-config['name'] = 'pyr_abl_32_skirt_2'
+config['name'] = 'pyr_abl_32_skirt_r1'
-config['notes'] = 'continued from pyr_abl_32_skirt-180k'
+config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -24,7 +24,10 @@ config['pyramid']['resolutions'] = [32]
 config['pyramid']['pretrained_pyramid'] = ''
 config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
 
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
+config['pretrained_model'] = ''
+config['pretrained_model_iteration'] = ''
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
 config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
@@ -35,7 +38,7 @@ config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
 config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
 config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
 
-config['dataset_in_memory'] = False
+config['dataset_in_memory'] = True
 config['resolution'] = None
 config['latent_dim'] = 128
 
@@ -65,6 +68,7 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
 config['pyramid']['is_label'] = False
 config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
+config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 

configs/config_ae_curv.py

@@ -1,13 +1,23 @@
 from configs.config_base import config
 
-config['gpu'] = '5'
+config['gpu'] = '4'
 config['lr'] = 0.001
-config['name'] = 'curv_1' #'ae_curv_9'
+config['name'] = 'curv_1_orig_r2' #'ae_curv_9'
 config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
 | \
   '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
 
 config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
                           'uv_curvature': (512, 256, 7),

configs/config_ae_curv_1_orig_curv_100_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '1'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_100_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 100
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_100_10p_r2_2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '1'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_100_10p_r2_2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 100
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_10_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '4'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_10_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_10_10p_r2_2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '4'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_10_10p_r2_2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_25_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '6'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_25_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 25
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

convnext/models/convnext_tf.py

@@ -366,8 +366,8 @@ class ConvNeXtReverseLabels(tf.keras.Model):
             self.stages.append(stage)
             cur += depths[i]
 
-        # self.last = Dense(nr_of_classes, activation='softmax')
+        self.last = Dense(nr_of_classes, activation='softmax')
-        self.last = Dense(nr_of_classes - 1)
+        # self.last = Dense(nr_of_classes - 1)
 
     def call(self, x, training):
         for i in reversed(range(len(self.downsample_layers))):

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_100_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 
 
 def init_wandb():
@@ -73,7 +73,7 @@ def kl_loss(mean, logvar):
 
 # 3d loss on posed !!!!!!!!!!!!
 
-# @tf.function
+@tf.function
 def train_step(batch):
 
     with tf.GradientTape() as tape:
@@ -476,7 +476,7 @@ def test():
 
 
 if __name__ == '__main__':
-    # init_wandb()
+    init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -494,7 +494,8 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -509,7 +510,8 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.post.config_abl_pyr_64_128_5 import config
+from configs.config_abl_pyr_32_skirt import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.pyramid_model_optimized import PyramidNew as Pyramid
+from models.pyramid_model_optimized import Pyramid
 from postprocessing import fix_collision
 
 
@@ -59,9 +59,11 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.post.')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs')]
     for config_file_name in config_file_names:
-        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+        file_name = '/'.join(config_file_name.split('.')) + '.py'
+        if os.path.exists(file_name):
+            artifact.add_file(file_name)
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -208,7 +210,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
     return fig
 
 
-@tf.function
+# @tf.function
 def train_step(batch):
     levels_input, conds = group_inputs_for_levels(batch, config)
 
@@ -261,8 +263,8 @@ def train_step(batch):
         else:
             reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
 
-        if config['use_norm_loss'] and step > 2000:
+        # if config['use_norm_loss'] and step > 2000:
-            reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+        #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
         kl_total += kl
 
@@ -334,8 +336,8 @@ def valid_step(batch):
     else:
         reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
     
-    if config['use_norm_loss'] and step > 2000:
+    # if config['use_norm_loss'] and step > 2000:
-        reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+    #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
     kl_total += kl
 
@@ -731,7 +733,7 @@ def speed_step(levels_input, conds):
 
 
 if __name__ == '__main__':
-    init_wandb()
+    # init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str, default='lhunor')
     args = vars(parser.parse_args())
@@ -790,7 +792,7 @@ if __name__ == '__main__':
     # generate_results()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
 
     # load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
 
@@ -807,18 +809,18 @@ if __name__ == '__main__':
         model.static_enc.trainable = False
         model.body_enc.trainable = False
         model.curv_enc.trainable = False
-
+        model.cond_encoder.trainable = False
 
     # load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
     # model.levels[0].upscale_enhancer.trainable = False
 
 
-    for i in range(len(model.levels)):
+    for i in range(len(model.levels)-1):
         load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
         model.levels[i].upscale_enhancer.trainable = False
-    # if config['pyramid']['resolutions'][-1] != 512:
+    if config['pyramid']['resolutions'][-1] != 512:
-    #     load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
+        load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-    #     model.upscale_enhancer.trainable = False
+        model.upscale_enhancer.trainable = False
 
     for i in range(len(model.levels)-1):
         model.levels[i].trainable = False

dataset.py

@@ -194,7 +194,8 @@ class DatasetFactory():
                     use_one_hot=False,
                     use_no_orient=True,
                     testing=False,
-                    frames_file=None):
+                    frames_file=None,
+                    curv_neighbourhood=25):
         assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
 
         dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, frames_file=frames_file)
@@ -260,7 +261,7 @@ class DatasetFactory():
 
 
             if 'mesh_curvature' in signature.keys():
-                mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
+                mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
                 item['mesh_curvature'] = mesh_curvature.astype(np.int32)
 
 
@@ -270,7 +271,8 @@ class DatasetFactory():
                     res_string = '_' + str(res)
 
                 if 'uv_curvature' + res_string in signature.keys():
-                    uv_curvature = np.load(os.path.join(folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz')); 
+                    # print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
+                    uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz')); 
                     if res_string != '':
                         uv_curvature = uv_curvature['uv_curvature'] 
                     else:
@@ -381,15 +383,16 @@ if __name__ == '__main__':
                         #  'uv_shaped_body': (3, 512, 256,),
                           }
 
-    dataset = factory.get_dataset(folder='/home/workspace/local-data/train_skirt',
+    dataset = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
                                   batch_size=1,
                                   signature=signature_dynamic,
                                   resolutions=[512],
                                   do_repeat=False,
                                   use_one_hot=False,
                                   is_cape=False,
-                                  frames_file='frames_dress_30k.txt')
+                                  frames_file='frames_skirt_10p.txt',
-    static_dataset = StaticDataset(folder='/home/workspace/local-data/train_skirt',
+                                  curv_neighbourhood=25)
+    static_dataset = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
                                    signature=signature_static,
                                    resolutions=[512],
                                   is_cape=False)

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_25_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
@@ -102,7 +102,7 @@ if __name__ == '__main__':
     ids = [id.split('_')[0] for id in ids]
     ids = set(ids)
 
-    iteration = ''
+    iteration = '70000'
 
     load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')

models/cvae_model.py

@@ -43,7 +43,7 @@ class CVAE(tf.keras.Model):
         self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
                                                  tf.keras.layers.Dense(256, activation='relu'),
                                                  tf.keras.layers.Dense(128, activation='relu'),
-                                                 tf.keras.layers.Lambda(lambda x: x / 1000),
+                                                #  tf.keras.layers.Lambda(lambda x: x / 1000),
                                                  tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
 
         self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])