Upload config_recog_autolapa_frame_linear.py with huggingface_hub

config_recog_autolapa_frame_linear.py

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+import torch
+import torchvision.transforms as transforms
+_base_ = ['../base.py']
+
+config = dict(
+    train_config=[
+        dict(
+        type='Recognition_frame',
+        csv_root='/gpfswork/rech/okw/ukw13bv/mmsl/csv/autolaparo/csvs',
+        vid='%02d.csv'%i,
+        video_root='/gpfsscratch/rech/okw/ukw13bv/autolaparo/frames_output',
+        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 i in range(1, 11)
+    ],
+    val_config=[
+        dict(
+        type='Recognition_frame',
+        csv_root='/gpfswork/rech/okw/ukw13bv/mmsl/csv/autolaparo/csvs',
+        vid='%02d.csv'%i,
+        video_root='/gpfsscratch/rech/okw/ukw13bv/autolaparo/frames_output',
+        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 i in range(11, 15)
+    ],
+    test_config=[
+        dict(
+        type='Recognition_frame',
+        csv_root='/gpfswork/rech/okw/ukw13bv/mmsl/csv/autolaparo/csvs',
+        vid='%02d.csv'%i,
+        video_root='/gpfsscratch/rech/okw/ukw13bv/autolaparo/frames_output',
+        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 i in range(15, 22)
+    ],
+    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
+        )
+    )
+)
+