prepare frames files

aaa_helpers.txt

@@ -1,5 +1,4 @@
 one final convolutional layer for smoothing, on the final coordinates
-nohup python calculate_error.py --name error_al_all_150k > nohup/error/post_al_all_150k.out 2>&1 &
 
 skirts!!!!
 pyramid next week

configs/config_abl_all_post.py

@@ -19,7 +19,7 @@ config['only_curvatures'] =           False
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
 
-config['gpu'] = '4'
+config['gpu'] = '1'
 config['name'] = 'al_all_post'
 config['notes'] = ''
 

configs/post/config_abl_pyr_32.py

@@ -1,9 +1,9 @@
 config = dict()
 
-config['gpu'] = '2'
+config['gpu'] = '6'
 config['name'] = 'abl_pyr_32'
-config['notes'] = 'fixed noise handling'
+config['notes'] = ''
-config['tags'] = ['pyr_post']
+config['tags'] = ['review_1']
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING

docker/Dockerfile

@@ -2,41 +2,9 @@ FROM tensorflow/tensorflow:2.9.0-gpu
 
 RUN apt-get update
 RUN apt-get upgrade -y
-
-# Add the current user to the image
-ARG USER_NAME=lhunor
-ARG USER_ID=11017
-ARG GROUP_NAME=guest
-ARG GROUP_ID=11000
-
-# Update the system and install required packages
-RUN apt-get update && apt-get upgrade -y && apt-get install -y sudo git
-
-# Create a new group with the same GID as the host
-RUN groupadd --gid $GROUP_ID $GROUP_NAME
-
-# Create a new user with the same UID and GID as the host
-RUN useradd --uid $USER_ID --gid $GROUP_ID --shell /bin/bash --create-home $USER_NAME
-
-# don't require password with sudo, for convenience
-# not the safest thing to do, but hopefully okay inside the container
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-
-# Add the new user to the sudo group
-RUN usermod -aG sudo $USER_NAME
-
-# Set the default user to the new user
-USER $USER_NAME
-
-WORKDIR //home-net
-
 RUN apt-get install git ffmpeg libsm6 libxext6 libopenexr-dev zip unzip htop nano -y
 RUN pip install numpy matplotlib opencv-python wandb OpenEXR tensorflow-graphics scipy plotly pandas tensorboardX opencv-python cmapy
 
-# defining the volumes that will be mounted
-VOLUME [ "/data-net" ]
-VOLUME [ "/data-local" ]
-VOLUME [ "/home-net" ]
 
-# Start the container with a bash shell
+WORKDIR /home/workspace/data
-CMD ["/bin/bash"]
+# RUN wandb login

preprocessing.py

@@ -232,17 +232,17 @@ def generate_frames_file(folder, ratio=0.1):
     selected = []
 
     for id in tqdm.tqdm(sub_ids):
-        if garment_type_table[id] != 'Dress':
+        # if garment_type_table[id] != 'Dress':
-            continue
+        #     continue
-        if len(selected) > 30000:
+        # if len(selected) > 30000:
-            break
+        #     break
 
         # frames = glob.glob(os.path.join(folder, 'pose_shape', id + '_*'))
         frames = [frame for frame in all_frames if frame.startswith(id + '_')]
         frames = [frame.split('/')[-1].split('.')[0] for frame in frames]
         selected.extend(frames)
 
-    with open(os.path.join(folder, 'frames_dress_30k.txt'), 'w') as f:
+    with open(os.path.join(folder, 'frames_skirt_10p.txt'), 'w') as f:
         for i, frame in enumerate(selected):
             f.write(frame + '\n')
 
@@ -293,9 +293,9 @@ def generate_frames_file_filtered_cape():
 
 if __name__ == '__main__':
 
-    src_folder = '/home/workspace/local-data/train_skirt' #cloth3d_skirt
+    src_folder = '/home/workspace/local-data/valid_skirt' #cloth3d_skirt
     # src_folder = r'D:\Work\Dataset\validation_2'
-    dest_folder = '/home/workspace/data/dataset/validation' 
+    dest_folder = '/home/workspace/data/dataset/local-cache/train_skirt' 
 
 
     # create_no_orient_body(src_folder)
@@ -306,5 +306,5 @@ if __name__ == '__main__':
     # verify_poses(src_folder)
     # create_garment_type_table()
     # generate_faces()
-    generate_frames_file(src_folder, ratio=1)
+    generate_frames_file(src_folder, ratio=0.1)
     # generate_frames_file_filtered_cape()