def interpret_image(self, image, target_class, result_dir):
# Get gradients
guided_grads = self.algo.attribute(
image, target_class).detach().cpu().numpy()[0]
print(guided_grads.shape)
# Save colored gradients
save_gradient_images(guided_grads, result_dir + '_Guided_BP_color')
# Convert to grayscale
grayscale_guided_grads = convert_to_grayscale(
guided_grads * image.detach().numpy()[0])
# Save grayscale gradients
save_gradient_images(grayscale_guided_grads,
result_dir + '_Guided_BP_gray')
# Positive and negative saliency maps
pos_sal, neg_sal = get_positive_negative_saliency(guided_grads)
save_gradient_images(pos_sal, result_dir + '_pos_sal')
save_gradient_images(neg_sal, result_dir + '_neg_sal')
print('Guided backprop completed')
one_hot_output[0][target_class] = 1
# Backward pass
model_output.backward(gradient=one_hot_output)
# Convert Pytorch variable to numpy array
# [0] to get rid of the first channel (1,3,224,224)
gradients_as_arr = self.gradients.data.numpy()[0]
return gradients_as_arr
if __name__ == '__main__':
target_example = 0 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) =\
get_example_params(target_example)
# Guided backprop
GBP = GuidedBackprop(pretrained_model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, target_class)
# Save colored gradients
save_gradient_images(guided_grads, file_name_to_export + '_Guided_BP_color')
print("exported to: " + str(os.getcwd()))
# Convert to grayscale
grayscale_guided_grads = convert_to_grayscale(guided_grads)
# Save grayscale gradients
save_gradient_images(grayscale_guided_grads, file_name_to_export + '_Guided_BP_gray')
# Positive and negative saliency maps
pos_sal, neg_sal = get_positive_negative_saliency(guided_grads)
save_gradient_images(pos_sal, file_name_to_export + '_pos_sal')
save_gradient_images(neg_sal, file_name_to_export + '_neg_sal')
print('Guided backprop completed')
one_hot_output = torch.FloatTensor(1, model_output.size()[-1]).zero_()
one_hot_output[0][target_class] = 1
# Backward pass
model_output.backward(gradient=one_hot_output)
# Convert Pytorch variable to numpy array
# [0] to get rid of the first channel (1,3,224,224)
gradients_as_arr = self.gradients.data.numpy()[0]
return gradients_as_arr
if __name__ == '__main__':
target_example = 0 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) =\
get_params(target_example)
# Guided backprop
GBP = GuidedBackprop(pretrained_model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, target_class)
# Save colored gradients
save_gradient_images(guided_grads, file_name_to_export + '_Guided_BP_color')
# Convert to grayscale
grayscale_guided_grads = convert_to_grayscale(guided_grads)
# Save grayscale gradients
save_gradient_images(grayscale_guided_grads, file_name_to_export + '_Guided_BP_gray')
# Positive and negative saliency maps
pos_sal, neg_sal = get_positive_negative_saliency(guided_grads)
save_gradient_images(pos_sal, file_name_to_export + '_pos_sal')
save_gradient_images(neg_sal, file_name_to_export + '_neg_sal')
print('Guided backprop completed')
