Create GetMismatch.py

GetMismatch.py

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+incorrect_examples = []
+incorrect_labels = []
+incorrect_pred = []
+model.eval()
+for data,target in test_loader:
+
+  data , target = data.to(device), target.to(device)
+  output = model(data) # shape = torch.Size([batch_size, 10])
+  pred = output.argmax(dim=1, keepdim=True) #pred will be a 2d tensor of shape [batch_size,1]
+  idxs_mask = ((pred == target.view_as(pred))==False).view(-1)
+  if idxs_mask.numel(): #if index masks is non-empty append the correspoding data value in incorrect examples
+    incorrect_examples.append(data[idxs_mask].squeeze().cpu().numpy())
+    incorrect_labels.append(target[idxs_mask].cpu().numpy()) #the corresponding target to the misclassified image
+    incorrect_pred.append(pred[idxs_mask].squeeze().cpu().numpy()) #the corresponiding predicted class of the misclassified image