def generate_one_image_smooth_gradient(model_name,
image_name,
target_class,
full_image_path,
pretrain=True):
if model_name == 'denseNet':
pretrained_model = models.densenet121(pretrained=pretrain)
elif model_name == "resNet": # res net
pretrained_model = models.resnet18(pretrained=pretrain)
original_image = Image.open(full_image_path).convert('RGB')
prep_img = customPreProcessing(original_image)
save_image_name = image_name + model_name + '_SM_pretrained'
# save_image_name = image_name + model_name + '_SM_randomWeights'
# Vanilla backprop
VBP = VanillaBackprop(pretrained_model, _type=model_name)
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, save_image_name + '_colored')
# Convert to grayscale
grayscale_smooth_grad = convert_to_grayscale(smooth_grad)
# Save grayscale gradients
save_gradient_images(grayscale_smooth_grad, save_image_name)
print('Smooth grad completed')
def generate_attribution_map(img_batch, model, numbered_layer, device):
VBP = VanillaBackprop(model)
myExtractor = FeatureExtractor(model, extracted_layers=numbered_layer)
output = myExtractor(img_batch)
grad = []
if 'feature' in numbered_layer.keys():
for i in range(len(numbered_layer['feature'])):
grad.append(
VBP.generate_gradients(img_batch, numbered_layer['feature'][i],
'feature', device) *
img_batch.data.cpu().numpy())
if 'classifier' in numbered_layer.keys():
for i in range(len(numbered_layer['classifier'])):
grad.append(
VBP.generate_gradients(
img_batch, numbered_layer['classifier'][i], 'classifier',
device) * img_batch.data.cpu().numpy())
return (output, grad)
# Calculate gradients
vanilla_grads = Backprop.generate_gradients(noisy_img, target_class)
# Add gradients to smooth_grad
smooth_grad = smooth_grad + vanilla_grads
# 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')