"""
Created on Thu Oct 26 11:23:47 2017

@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.nn import ReLU

class GuidedBackprop():
    """
       Produces gradients generated with guided back propagation from the given image
    """
    def __init__(self, model, arch):
        self.model = model
        self.arch = arch
        self.gradients = None
        self.forward_relu_outputs = []
        # Put model in evaluation mode
        self.model.train()
        self.update_relus()
        self.hook_layers()

    def hook_layers(self):
        def hook_function(module, grad_in, grad_out):
            self.gradients = grad_in[0]
        # Register hook to the first layer
        if 'alexnet' in self.arch:
            first_layer = list(self.model.features._modules.items())[0][1]
        elif 'resnet' in self.arch:
            first_layer = list(self.model._modules.items())[0][1]
        first_layer.register_backward_hook(hook_function)

    def update_relus(self):
        """
            Updates relu activation functions so that
                1- stores output in forward pass
                2- imputes zero for gradient values that are less than zero
        """
        def relu_backward_hook_function(module, grad_in, grad_out):
            """
            If there is a negative gradient, change it to zero
            """
            # Get last forward output
            corresponding_forward_output = self.forward_relu_outputs[-1]
            corresponding_forward_output[corresponding_forward_output > 0] = 1
            modified_grad_out = corresponding_forward_output * torch.clamp(grad_in[0], min=0.0)
            del self.forward_relu_outputs[-1]  # Remove last forward output
            return (modified_grad_out,)

        def relu_forward_hook_function(module, ten_in, ten_out):
            """
            Store results of forward pass
            """
            self.forward_relu_outputs.append(ten_out)

        # Loop through layers, hook up ReLUs
        if 'alexnet' in self.arch:
            for pos, module in self.model.features._modules.items():
                if isinstance(module, ReLU):
                    module.register_backward_hook(relu_backward_hook_function)
                    module.register_forward_hook(relu_forward_hook_function)
        elif 'resnet' in self.arch:
            for module in self.model.modules():
                if isinstance(module, ReLU):
                    module.register_backward_hook(relu_backward_hook_function)
                    module.register_forward_hook(relu_forward_hook_function)

    def generate_gradients(self, input_image, one_hot_output_guided, text_for_pred):
        # Forward pass
        model_output = self.model(input_image, text_for_pred, is_train=False)
        # Zero gradients
        self.model.zero_grad()
        # Backward pass
        model_output.backward(gradient=one_hot_output_guided)
        # Convert Pytorch variable to numpy array
        # [0] to get rid of the first channel (1,3,224,224)
        gradients_as_arr = self.gradients.data.to('cpu').numpy()[0]
        print("gradients_as_arr shape: ", gradients_as_arr.shape)
        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')
    # 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')