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.')