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Description
Model: Alexnet
Dataset: CIFAR-10
The code works well with layer_name='features'
but I get this error when I change it to layer_name='classifier'
Code:
# GRADCAM++
image_path = 'xyz'
image = load_image(image_path)
norm_image = apply_transforms(image, size=32)
model_dict = dict(arch=model, layer_name='classifier_0', input_size=(32, 32))
gradcampp = GradCAMpp(model_dict)
output = gradcampp(norm_image)
visualize(norm_image, output)
Error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-63-fed255ab9edc> in <module>()
7
8 gradcampp = GradCAMpp(model_dict)
----> 9 output = gradcampp(norm_image)
10 visualize(norm_image, output)
1 frames
<ipython-input-46-e7d860e12ca6> in __call__(self, input_, class_idx, retain_graph)
70
71 def __call__(self, input_, class_idx=None, retain_graph=False):
---> 72 return self.forward(input_, class_idx, retain_graph)
<ipython-input-49-d221b5d1444c> in forward(self, input_image, class_idx, retain_graph)
32 gradients = self.gradients['value']
33 activations = self.activations['value']
---> 34 b, k, u, v = gradients.size()
35
36 alpha_num = gradients.pow(2)
ValueError: not enough values to unpack (expected 4, got 2)
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