Upload config_recog_bern_bypass_frame_linear.py with huggingface_hub

config_recog_bern_bypass_frame_linear.py

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+import torch
+import torchvision.transforms as transforms
+import os
+import logging
+import pickle
+
+def read_pkl_data(pkl_path, img_path):
+    logging.info('reading pickle file: '+ pkl_path)
+    with open(pkl_path, "rb") as fp:
+        data = pickle.load(fp)
+        fp.close()
+    
+    root_dir = img_path
+    if not os.path.exists(root_dir):
+        root_dir = root_dir.replace('train', '').replace('val', '').replace('test', '')
+    imgs, phases, steps = [], [], []
+    for vid_name in sorted(data.keys()):
+        paths = [
+                os.path.join(root_dir, vid_name, f"{item['Frame_id']}.jpg")
+            for item in data[vid_name]
+        ]
+        imgs.append(paths)
+        phases.append([item['Phase_gt'] for item in data[vid_name]])
+        steps.append([item['Step_gt'] for item in data[vid_name]])
+    
+    return imgs, phases, steps
+
+
+## Read test pickle files
+#### TRAIN ####
+labels = os.path.join('/gpfswork/rech/okw/ukw13bv/MultiBypass140/labels', 'bern', 'labels_by70_splits/labels', 'train', f'1fps_100_0.pickle')
+images = os.path.join('/gpfsscratch/rech/okw/ukw13bv/bypass/BernBypass70/frames')
+videos_train, phase_labels_train, step_labels_train = read_pkl_data(
+    labels, images
+)
+
+#### VAL ####
+labels = os.path.join('/gpfswork/rech/okw/ukw13bv/MultiBypass140/labels', 'bern', 'labels_by70_splits/labels', 'val', f'1fps_0.pickle')
+images = os.path.join('/gpfsscratch/rech/okw/ukw13bv/bypass/BernBypass70/frames')
+videos_val, phase_labels_val, step_labels_val = read_pkl_data(
+    labels, images
+)
+
+#### TEST ####
+labels = os.path.join('/gpfswork/rech/okw/ukw13bv/MultiBypass140/labels', 'bern', 'labels_by70_splits/labels', 'test', f'1fps_0.pickle')
+images = os.path.join('/gpfsscratch/rech/okw/ukw13bv/bypass/BernBypass70/frames')
+videos_test, phase_labels_test, step_labels_test = read_pkl_data(labels, images)
+
+_base_ = ['../base.py']
+config = dict(
+    train_config=[
+      dict(
+      type='Recognition_frame_bypass',
+      img_list=v,
+      label_list=l,
+      transforms=transforms.Compose(
+          [
+          transforms.Resize((360, 640)),
+          transforms.CenterCrop(224),
+          transforms.ToTensor(),
+          transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+          ]
+          ),
+      ) for v, l in zip(videos_train, phase_labels_train)
+    ],
+    val_config=[
+      dict(
+      type='Recognition_frame_bypass',
+      img_list=v,
+      label_list=l,
+      transforms=transforms.Compose(
+          [
+          transforms.Resize((360, 640)),
+          transforms.CenterCrop(224),
+          transforms.ToTensor(),
+          transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+          ]
+          ),
+      ) for v, l in zip(videos_val, phase_labels_val)
+    ],
+    test_config=[
+      dict(
+      type='Recognition_frame_bypass',
+      img_list=v,
+      label_list=l,
+      transforms=transforms.Compose(
+          [
+          transforms.Resize((360, 640)),
+          transforms.CenterCrop(224),
+          transforms.ToTensor(),
+          transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+          ]
+          ),
+      ) for v, l in zip(videos_test, phase_labels_test)
+    ],
+    model_config = dict(
+        type='MVNet_feature_extractor',
+        backbone_img = dict(
+            type='img_backbones/ImageEncoder_feature_extractor',
+            # type='img_backbones/ImageEncoder_CLIPVISUAL',
+            num_classes=768,
+            pretrained='imagenet', # imagenet/ssl/random
+            backbone_name='resnet_50', 
+            # backbone_name='resnet_50_clip' 
+            img_norm=False,
+        ),
+        backbone_text= dict(
+            type='text_backbones/BertEncoder',
+            text_bert_type='/gpfswork/rech/okw/ukw13bv/mmsl/biobert_pretrain_output_all_notes_150000',
+            text_last_n_layers=4,
+            text_aggregate_method='sum',
+            text_norm=False,
+            text_embedding_dim=768,
+            text_freeze_bert=False,
+            text_agg_tokens=True
+        )
+    )
+)
+