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Author: NaeRong

Tutorials/Model Script/Confusion_Matrix.py

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+"""## Confusion Matrix"""
+
+model_name = args.model_name
+PATH = '/content/drive/My Drive/resnet101_2_75.pth'
+checkpoint = torch.load(PATH)
+# classes = ('0','1') 
+model = make_model(
+        model_name,
+        pretrained=True,
+        num_classes=2,
+        input_size= None,
+    )
+
+model.load_state_dict(checkpoint['state_dict'])
+
+optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum= args.momentum)
+optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+
+class_correct = list(0. for i in range(2))
+class_total = list(0. for i in range(2))
+with torch.no_grad():
+    for data in test_loader:
+        images = data['image']
+        labels = data['label']
+        outputs = model(images)
+        _, predicted = torch.max(outputs, 1)
+        c = (predicted == labels).squeeze()
+        for i in range(16):
+            label = labels[i]
+            class_correct[label] += c[i].item()
+            class_total[label] += 1
+
+
+for i in range(2):
+    print('Accuracy of %5s : %2d %%' % (
+        i, 100 * class_correct[i] / class_total[i]))
+
+
+for i in range(2):
+    print('Correct of %5s : %2d, Incorrect of %5s : %2d' % (
+        i, class_correct[i], i, 200-class_correct[i]))