a

.ipynb_checkpoints/app-checkpoint.py

@@ -84,23 +84,25 @@ def video_identity(video,user_name,class_name,trainortest,ready):
         train_loader = DataLoader(train_ds, batch_size=2, collate_fn=collator, num_workers=8, shuffle=True)
         test_loader = DataLoader(test_ds, batch_size=2, collate_fn=collator, num_workers=7)
         
-        
-        for name, param in model.named_parameters():
-            param.requires_grad = False
-            if name.startswith("classifier"): # choose whatever you like here
-                param.requires_grad = True
+        val_batch = next(iter(test_loader))
+        outputs = model(**val_batch)
+        preds=outputs.logits.softmax(1).argmax(1)
+#         for name, param in model.named_parameters():
+#             param.requires_grad = False
+#             if name.startswith("classifier"): # choose whatever you like here
+#                 param.requires_grad = True
                 
-        pl.seed_everything(42)
-        classifier = Classifier(model, lr=2e-5)
-        trainer = pl.Trainer(accelerator='gpu', devices=1, precision=16, max_epochs=3)
+#         pl.seed_everything(42)
+#         classifier = Classifier(model, lr=2e-5)
+#         trainer = pl.Trainer(accelerator='gpu', devices=1, precision=16, max_epochs=3)
         
-        trainer.fit(classifier, train_loader, test_loader)
+#         trainer.fit(classifier, train_loader, test_loader)
         
-        for batch_idx, data in enumerate(test_loader):
-            outputs = model(**data)
-        img=data['pixel_values'][0][0]
-        preds=str(outputs.logits.softmax(1).argmax(1))
-        labels=str(data['labels'])
+#         for batch_idx, data in enumerate(test_loader):
+#             outputs = model(**data)
+#         img=data['pixel_values'][0][0]
+#         preds=str(outputs.logits.softmax(1).argmax(1))
+#         labels=str(data['labels'])
         
         return img, preds, labels
     

app.py

@@ -84,23 +84,25 @@ def video_identity(video,user_name,class_name,trainortest,ready):
         train_loader = DataLoader(train_ds, batch_size=2, collate_fn=collator, num_workers=8, shuffle=True)
         test_loader = DataLoader(test_ds, batch_size=2, collate_fn=collator, num_workers=7)
         
-        
-        for name, param in model.named_parameters():
-            param.requires_grad = False
-            if name.startswith("classifier"): # choose whatever you like here
-                param.requires_grad = True
+        val_batch = next(iter(test_loader))
+        outputs = model(**val_batch)
+        preds=outputs.logits.softmax(1).argmax(1)
+#         for name, param in model.named_parameters():
+#             param.requires_grad = False
+#             if name.startswith("classifier"): # choose whatever you like here
+#                 param.requires_grad = True
                 
-        pl.seed_everything(42)
-        classifier = Classifier(model, lr=2e-5)
-        trainer = pl.Trainer(accelerator='gpu', devices=1, precision=16, max_epochs=3)
+#         pl.seed_everything(42)
+#         classifier = Classifier(model, lr=2e-5)
+#         trainer = pl.Trainer(accelerator='gpu', devices=1, precision=16, max_epochs=3)
         
-        trainer.fit(classifier, train_loader, test_loader)
+#         trainer.fit(classifier, train_loader, test_loader)
         
-        for batch_idx, data in enumerate(test_loader):
-            outputs = model(**data)
-        img=data['pixel_values'][0][0]
-        preds=str(outputs.logits.softmax(1).argmax(1))
-        labels=str(data['labels'])
+#         for batch_idx, data in enumerate(test_loader):
+#             outputs = model(**data)
+#         img=data['pixel_values'][0][0]
+#         preds=str(outputs.logits.softmax(1).argmax(1))
+#         labels=str(data['labels'])
         
         return img, preds, labels