def visualize2(pretrained_model,
prep_img,
target_class,
file_name_to_export="test"):
# Vanilla backprop
VBP = VanillaBackprop(pretrained_model)
# Generate gradients
vanilla_grads = VBP.generate_gradients(prep_img, target_class)
# Save colored gradients
save_gradient_images(vanilla_grads,
file_name_to_export + '_Vanilla_BP_color')
# Convert to grayscale
grayscale_vanilla_grads = convert_to_grayscale(vanilla_grads)
# Save grayscale gradients
save_gradient_images(grayscale_vanilla_grads,
file_name_to_export + '_Vanilla_BP_gray')
print('Vanilla backprop completed')
def visualize(pretrained_model,
prep_img,
target_class,
file_name_to_export="test.png"):
# 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')
# Average it out
smooth_grad = smooth_grad / param_n
return smooth_grad
if __name__ == '__main__':
# Get params
target_example = 0 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) = \
get_example_params(target_example)
VBP = VanillaBackprop(pretrained_model)
# GBP = GuidedBackprop(pretrained_model) # if you want to use GBP dont forget to
# change the parametre in generate_smooth_grad
param_n = 50
param_sigma_multiplier = 4
smooth_grad = generate_smooth_grad(VBP, # ^This parameter
prep_img,
target_class,
param_n,
param_sigma_multiplier)
# Save colored gradients
save_gradient_images(smooth_grad, file_name_to_export + '_SmoothGrad_color')
# Convert to grayscale
grayscale_smooth_grad = convert_to_grayscale(smooth_grad)
# Save grayscale gradients
save_gradient_images(grayscale_smooth_grad, file_name_to_export + '_SmoothGrad_gray')
print('Smooth grad completed')
model_output = self.model(input_image)
# Zero grads
self.model.zero_grad()
# Target for backprop
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__':
# Get params
target_example = 1 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) = \
get_example_params(target_example)
# Vanilla backprop
VBP = VanillaBackprop(pretrained_model)
# Generate gradients
vanilla_grads = VBP.generate_gradients(prep_img, target_class)
# Save colored gradients
save_gradient_images(vanilla_grads, file_name_to_export + '_Vanilla_BP_color')
# Convert to grayscale
grayscale_vanilla_grads = convert_to_grayscale(vanilla_grads)
# Save grayscale gradients
save_gradient_images(grayscale_vanilla_grads, file_name_to_export + '_Vanilla_BP_gray')
print('Vanilla backprop completed')
# [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
dataset = ds.ClipArt("assets")
prep_img, target_class = dataset.__getitem__(0)
file_name_to_export = "test.png"
pretrained_model = None
# 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')