acc_hist = BinaryClassificationMeter()
# get the image from the dataloader
for i, data in tqdm(enumerate(dataloader), desc='Dataiteration_Test'):
img = data['fdata'].to(device)
label = data['label'].to(device)
label_ID = data['label_ID']
# get the most likely prediction of the model
pred = net(img)
pred_label = net(img).argmax(dim=1)
acc_hist.update(pred_label.cpu().numpy(), label.cpu().numpy())
pred = pred[:, 1]
# back-propagation with the logit of the 2nd class which represents a success
# get the gradient of the output with respect to the parameters of the model
pred.backward()
# pull the gradients out of the model
gradients = net.get_activations_gradient()
# pool the gradients across the channels
pooled_gradients = torch.mean(gradients, dim=(0, 2, 3, 4))
# get the activations of the last convolutional layer
activations = net.get_activations(img).detach()
# weight the channels by corresponding gradients
for i in range(128):
activations[:, i, :, :] *= pooled_gradients[i]
# average the channels of the activations
heatmap = torch.mean(activations, dim=1).squeeze()
# relu on top of the heatmap
heatmap = np.maximum(heatmap.cpu().numpy(), 0)
# save the heatmap
if label_ID[0].find('B_b') != -1:
sumed_heatmap_B_b.append(heatmap)
elif label_ID[0].find('Z_b') != -1:
