guided_backprop_mask (np_arr):Guided backprop mask
"""
cam_gb = np.multiply(grad_cam_mask, guided_backprop_mask)
return cam_gb
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)
# Grad cam
gcv2 = GradCam(pretrained_model, target_layer=11)
# Generate cam mask
cam = gcv2.generate_cam(prep_img, target_class)
print('Grad cam completed')
# Guided backprop
GBP = GuidedBackprop(pretrained_model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, target_class)
print('Guided backpropagation completed')
# Guided Grad cam
cam_gb = guided_grad_cam(cam, guided_grads)
save_gradient_images(cam_gb, file_name_to_export + '_GGrad_Cam')
grayscale_cam_gb = convert_to_grayscale(cam_gb)
save_gradient_images(grayscale_cam_gb, file_name_to_export + '_GGrad_Cam_gray')
print('Guided grad cam completed')
Created on Wed Jun 19 17:12:04 2019
@author: Utku Ozbulak - github.com/utkuozbulak
"""
from visualization.misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images)
from visualization.vanilla_backprop import VanillaBackprop
# from guided_backprop import GuidedBackprop # To use with guided backprop
# from integrated_gradients import IntegratedGradients # To use with integrated grads
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)
# Vanilla backprop
VBP = VanillaBackprop(pretrained_model)
# Generate gradients
vanilla_grads = VBP.generate_gradients(prep_img, target_class)
# Make sure dimensions add up!
grad_times_image = vanilla_grads * prep_img.detach().numpy()[0]
# Convert to grayscale
grayscale_vanilla_grads = convert_to_grayscale(grad_times_image)
# Save grayscale gradients
save_gradient_images(
grayscale_vanilla_grads,
file_name_to_export + '_Vanilla_grad_times_image_gray')
print('Grad times image completed.')
# 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__':
# one for each class, you might adjust the paths in misc_functions#get_params
for target_example in range(5):
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) = \
get_params(target_example)
# Guided backprop
GBP = GuidedBackprop(pretrained_model, prep_img, target_class)
# Get gradients
guided_grads = GBP.generate_gradients()
# 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(f'Guided backprop completed for {target_example}')
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')
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')
# Generate xbar images
xbar_list = self.generate_images_on_linear_path(input_image, steps)
# Initialize an iamge composed of zeros
integrated_grads = np.zeros(input_image.size())
for xbar_image in xbar_list:
# Generate gradients from xbar images
single_integrated_grad = self.generate_gradients(
xbar_image, target_class)
# Add rescaled grads from xbar images
integrated_grads = integrated_grads + single_integrated_grad / steps
# [0] to get rid of the first channel (1,3,224,224)
return integrated_grads[0]
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)
# Vanilla backprop
IG = IntegratedGradients(pretrained_model)
# Generate gradients
integrated_grads = IG.generate_integrated_gradients(
prep_img, target_class, 100)
# Convert to grayscale
grayscale_integrated_grads = convert_to_grayscale(integrated_grads)
# Save grayscale gradients
save_gradient_images(grayscale_integrated_grads,
file_name_to_export + '_Integrated_G_gray')
print('Integrated gradients completed.')