fine tune

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 @@
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_e2e_r3" > nohup/review/pyr_abl_32_64_e2e_r3.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_e2e_r3_pre" > nohup/review/pyr_abl_32_64_e2e_r3_pre.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_e2e_r3_fine" > nohup/review/pyr_abl_32_64_e2e_r3_fine.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_pyr_abl_32_64_e2e_r3 > nohup/evaluate/pyr_abl_32_64_e2e_r3.out 2>&1 &
+nohup python calculate_error.py --name error_pyr_abl_32_64_e2e_r3 > nohup/error/pyr_abl_32_64_e2e_r3.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
+
+

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_pyr_32 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf
@@ -59,13 +59,13 @@ if __name__ == '__main__':
     folder = config['test_dir']
 
     checkpoint_name = config['name']
-    iteration = ''
+    iteration = '200000'
 
     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()
 
 
-    output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration
+    output_folder = '/home/workspace/data/results/' + checkpoint_name + '-' + iteration
 
 
     faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')

configs/config_abl_pyr_32.py

@@ -1,8 +1,9 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32'
+config['name'] = 'pyr_abl_32_64_e2e_r3_fine_2'
 config['notes'] = ''
+config['tags'] = ['review_3']
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -21,9 +22,12 @@ config['use_offsets'] =               False
 
 
 config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32]
+config['pyramid']['resolutions'] = [32, 64]
 config['pyramid']['pretrained_pyramid'] = ''
-config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4'
+config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_2'
+
+config['pretrained_model'] = 'checkpoints/pyr_abl_32_64_e2e_r3_fine'
+config['pretrained_model_iteration'] = ''
 
 config['train_dir'] = '/home/workspace/local-data/train'
 config['valid_dir'] = '/home/workspace/local-data/valid'
@@ -40,7 +44,7 @@ config['smpl_res'] = 14475
 if config['is_cape']:
   config['smpl_res'] = 3638
 
-config['lr'] = 0.0001
+config['lr'] = 0.00001
 config['starting_balancing_coeff'] = 0.0001
 config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
 config['avg_garment_path'] = ''
@@ -64,7 +68,7 @@ config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
 config['pyramid']['noise_amount'] = 0.025
 
-config['pyramid']['loss_weights'] = [1,1,1,1,1]
+config['pyramid']['loss_weights'] = [2,1,1,1,1]
 
 for res in config['pyramid']['resolutions']:
   if res != 512:
@@ -149,7 +153,7 @@ for res in config['pyramid']['resolutions']:
     level['decoder_depths'] = [9, 9]
     level['decoder_dims'] = [256, 256]
     level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
+    level['upscale_enhancer'] = 'checkpoints/32_512_4'#'checkpoints/up_3_d32_64_2'
     level['upscale_enhancer_freeze'] = True
     config['pyramid']['levels'].append(level)
   else:

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]
 

custom_utils/custom_utils.py

@@ -45,7 +45,7 @@ class UV2Mesh(tf.keras.Model):
         if uv.shape[1] == 3:
             uv = tf.transpose(uv, [0,2,3,1])
         if uv.shape[1] != 512:
-            uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+            uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.BILINEAR)
             
         batch_size = uv.shape[0]
 

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 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'])
@@ -215,66 +217,70 @@ def train_step(batch):
     with tf.GradientTape() as tape:
         out_levels = model([levels_input, conds], training=True)
 
-        if config['use_skinning']:
+        mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
-            mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
-        else:
-            mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
 
         output = dict()
         reconstr_loss_total = 0
         kl_total = 0
 
-        #for i, res in enumerate(config['pyramid']['resolutions']):
+        for i, res in enumerate(config['pyramid']['resolutions']):
-        i = -1
+            # i = -1
-        res = config['pyramid']['resolutions'][-1]
+            # res = config['pyramid']['resolutions'][-1]
-        res_modifier = '_' + str(res) if res != 512 else ''
+            if res != 512:
-        pred, mean, logvar = out_levels[i]
+                if i == len(config['pyramid']['resolutions']) - 1:
+                    res_modifier = ''
+                else:
+                    res_modifier = '_' + str(res)
+            else:
+                res_modifier = ''
 
-        mesh_pred = uv2mesh(pred)
+            pred, mean, logvar = out_levels[i]
 
-        if config['use_skinning']:
+            mesh_pred = uv2mesh(pred)
-            shape = batch['pose_shape'][:, 72:]
-            gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
-            shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
 
-            mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
+            if config['use_skinning']:
-            mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
+                shape = batch['pose_shape'][:, 72:]
-        else:
+                gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
-            mesh_pred_posed = mesh_pred
+                shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
 
-        # is the output already masked?
+                mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
+                mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
+            else:
+                mesh_pred_posed = mesh_pred
 
-        e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
+            # is the output already masked?
-        c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
-        c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-        l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
-        # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-        # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
-        l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
-        n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
-        kl = kl_loss(mean, logvar)
 
-        if config['use_extra_losses']:
+            e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
-            reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
+            c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
-            if not config['is_cape']:
+            c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-                reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] 
+            l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2]))
-        else:
+            # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-            reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
+            # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
+            l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
+            n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
+            kl = kl_loss(mean, logvar)
 
-        if config['use_norm_loss'] and step > 2000:
+            if config['use_extra_losses']:
-            reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+                reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
-        
+                if not config['is_cape']:
-        kl_total += kl
+                    reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] 
+            else:
+                reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
 
-        mean = tf.reduce_mean(mean)
+            # if config['use_norm_loss'] and step > 2000:
-        logvar = tf.reduce_mean(logvar)
+            #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+            
+            kl_total += kl
 
-        metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
+            mean = tf.reduce_mean(mean)
+            logvar = tf.reduce_mean(logvar)
 
-        output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric, 
+            metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
-                                 'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
+
-                                 'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
+            output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric, 
-                                 'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
+                                    'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
+                                    'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
+                                    'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
 
         loss = reconstr_loss_total + balancing_coeff * kl_total
 
@@ -288,66 +294,69 @@ def valid_step(batch):
     levels_input, conds = group_inputs_for_levels(batch, config)
     out_levels = model([levels_input, conds], training=False)
 
-
+    mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
-    if config['use_skinning']:
-        mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
-    else:
-        mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
 
     output = dict()
     reconstr_loss_total = 0
     kl_total = 0
 
-    #for i, res in enumerate(config['pyramid']['resolutions']):
+    for i, res in enumerate(config['pyramid']['resolutions']):
-    i = -1
+        # i = -1
-    res = config['pyramid']['resolutions'][-1]
+        # res = config['pyramid']['resolutions'][-1]
-    res_modifier = '_' + str(res) if res != 512 else ''
+        if res != 512:
-    pred, mean, logvar = out_levels[i]
+            if i == len(config['pyramid']['resolutions']) - 1:
+                res_modifier = ''
+            else:
+                res_modifier = '_' + str(res)
+        else:
+            res_modifier = ''
 
-    mesh_pred = uv2mesh(pred)
+        pred, mean, logvar = out_levels[i]
 
-    if config['use_skinning']:
+        mesh_pred = uv2mesh(pred)
-        shape = batch['pose_shape'][:, 72:]
-        gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
-        shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
 
-        mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
+        if config['use_skinning']:
-        mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
+            shape = batch['pose_shape'][:, 72:]
-    else:
+            gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
-        mesh_pred_posed = mesh_pred
+            shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
-    # is the output already masked?
 
-    e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
+            mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
-    c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
+            mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
-    c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
+        else:
-    l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+            mesh_pred_posed = mesh_pred
-    # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
+        # is the output already masked?
-    # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
-    l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
-    n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
-    kl = kl_loss(mean, logvar)
 
-    if config['use_extra_losses']:
+        e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
-        reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
+        c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
-        if not config['is_cape']:
+        c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-            reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] 
+        l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2]))
-    else:
+        # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-        reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
+        # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
-    
+        l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
-    if config['use_norm_loss'] and step > 2000:
+        n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
-        reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+        kl = kl_loss(mean, logvar)
+
+        if config['use_extra_losses']:
+            reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
+            if not config['is_cape']:
+                reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] 
+        else:
+            reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
         
-    kl_total += kl
+        # if config['use_norm_loss'] and step > 2000:
+        #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+            
+        kl_total += kl
 
-    mean = tf.reduce_mean(mean)
+        mean = tf.reduce_mean(mean)
-    logvar = tf.reduce_mean(logvar)
+        logvar = tf.reduce_mean(logvar)
 
-    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
+        metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
 
-    output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric, 
+        output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric, 
-                                 'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
+                                    'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
-                                'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
+                                    'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
-                                'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
+                                    'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
 
     loss = reconstr_loss_total + balancing_coeff * kl_total
 
@@ -790,7 +799,7 @@ 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')
 
@@ -798,30 +807,33 @@ if __name__ == '__main__':
         # assert len(config['pyramid']['resolutions']) == 1
         load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
 
-    if len(config['pyramid']['resolutions']) == 1:
+    model.static_enc.trainable = False
-        model.static_enc.trainable = False
+    model.body_enc.trainable = False
-        model.body_enc.trainable = False
+    model.curv_enc.trainable = False
-        model.curv_enc.trainable = False
-    else:
-        load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
-        model.static_enc.trainable = False
-        model.body_enc.trainable = False
-        model.curv_enc.trainable = False
 
+    # model.trainable = False
+    # model.cond_encoder_2.trainable = True
+    # if len(config['pyramid']['resolutions']) == 1:
+    # else:
+    #     load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
+    #     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):
+    # for i in range(len(model.levels)-1):
-        model.levels[i].trainable = False
+    #     model.levels[i].trainable = False
 
     # load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
     # load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])

evaluate_ae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_2_pyr_32_64_128_256_512 import config
+from configs.config_abl_pyr_32 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -97,7 +97,7 @@ if __name__ == '__main__':
     ids = [id.split('_')[0] for id in ids]
     ids = set(ids)
 
-    iteration = '50000'
+    iteration = '200000'
 
     load_model(model, 'checkpoints/' + 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'])

models/pyramid_level.py

@@ -92,7 +92,7 @@ class PyramidLevel(tf.keras.Model):
         
         # x = x * uv_mask_high[:,:,:,tf.newaxis]
 
-        return x, mean, logvar
+        return x, z, mean, logvar
 
 
 

models/pyramid_model_optimized.py

@@ -78,6 +78,11 @@ class Pyramid(tf.keras.Model):
             load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
             self.upscale_enhancer.trainable = False
 
+        self.cond_encoder_2 = 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.Dense(config['pyramid']['latent_dim'], activation='tanh'),])
+
     def enhance(self, low, mask, training):
         x = self.upscale_enhancer([low, mask], training=training)
         return x
@@ -90,44 +95,54 @@ class Pyramid(tf.keras.Model):
         z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
         z_cond = self.cond_encoder(z_cond, training=training)
 
+        return z_cond, z_static, z_body, z_curv
+
+    def encode_conds_2(self, z, z_static, z_body, z_curv, training):
+        z_cond = tf.concat([z, z_static, z_body, z_curv], axis=-1)
+        z_cond = self.cond_encoder_2(z_cond, training=training)
+
         return z_cond
 
     def call(self, inputs, training):
         levels_input, conds = inputs
         uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
 
-        z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training)
+        z_cond, z_static, z_body, z_curv = self.encode_conds(uv_static, uv_body, uv_curv, training)
 
         if self.add_noise == True and training == True:
             z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
 
         out_levels = []
-        pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
+        pred, z, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
         if self.use_offsets:
             pred = pred + levels_input[0][2] # uv_body_posed_32
         if 1 != len(self.levels):
             pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training)  # levels_input[i][-2]=uv_mask_res
-            pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
             out_levels.append([pred, mean, logvar])
+            pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
             pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
         else:
             pred = self.enhance(pred, levels_input[0][-2], training=training)
-            pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+            pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
             pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
             out_levels.append([pred, mean, logvar])
 
         for i in range(1, len(self.levels)):
-            pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
+            input = levels_input[i]
+            input[0] = pred # use previous level output as input for next level
+            z_cond_2 = self.encode_conds_2(z, z_static, z_body, z_curv, training=training)
+
+            pred_level, z, mean, logvar = self.levels[i](input, z_cond_2, training=training)
             pred = pred + pred_level
             if i != len(self.levels) - 1:
                 pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
                 out_levels.append([pred, mean, logvar])
-                pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
+                pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.BILINEAR)
                 pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
             else:
                 if self.upscale_enhancer is not None:
                     pred = self.enhance(pred, levels_input[-1][-2], training=training)
-                    pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
+                    pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
                 pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
                 out_levels.append([pred, mean, logvar])