Create tester.py

tester.py

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+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+import timm
+from PIL import Image
+
+device = 'cuda'
+processor = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+class SwinBinaryClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.backbone = timm.create_model('swin_tiny_patch4_window7_224', pretrained=False, num_classes=0)
+        in_f = self.backbone.num_features
+        self.classifier = nn.Linear(in_f, 1)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        return self.classifier(x)
+
+model = SwinBinaryClassifier().to(device)
+model.load_state_dict(torch.load('./breastcancer_model.pth'))
+
+image = Image.open('./tests/Benign Masses/20586908 (12)_Benign.png').convert("RGB")
+input_tensor = processor(image)
+input_batch = input_tensor.unsqueeze(0)  # Add a batch dimension
+
+# Move the input and model to GPU if available
+if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+# Make a prediction
+with torch.no_grad():
+        output = model(input_batch)
+
+preds = (torch.sigmoid(output) > 0.5).int()
+
+classes = ['Benign', 'Malignant']
+# Print the predicted class
+print(f'Predicted class: {classes[preds]}')