model.py

import torch
import torchvision
import torch.nn as nn
import torchvision.models as models
from PIL import Image
from torchvision import transforms
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import copy
import torchvision.models as models
from PIL import Image
import numpy as np

# from vgg_weights import VGG19_Weights


#Content Loss
class ContentLoss(nn.Module):

    def __init__(self, target,):
        super(ContentLoss, self).__init__()
        '''
        we 'detach' the target content from the tree used 
        to dynamically compute the gradient: this is a stated value,
        not a variable. Otherwise the forward method of the criterion
        will throw an error.
        '''
        self.target = target.detach()
      
    def forward(self, input):
        self.loss = F.mse_loss(input, self.target)
        return input

#Style Loss
def gram_matrix(input):
    a, b, c, d = input.size()  # a=batch size(=1)
    # b=number of feature maps
    # (c,d)=dimensions of a f. map (N=c*d)

    features = input.view(a * b, c * d)  # resize F_XL into \hat F_XL

    G = torch.mm(features, features.t())  # compute the gram product

    # we 'normalize' the values of the gram matrix
    # by dividing by the number of element in each feature maps.
    return G.div(a * b * c * d)

class StyleLoss(nn.Module):

    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()

    def forward(self, input):
        G = gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input  

#Image Transform
        
transform = transforms.Compose([
    transforms.Resize((128,128)),  # scale imported image
    transforms.ToTensor()])  # transform it into a torch tensor

def image_transform(image):
  
    if image is not None:
        if isinstance(image, str):
            # If image is a path to a file, open it using PIL
            image = Image.open(image).convert('RGB')
        else:
            # If image is a NumPy array, convert it to a PIL image
            image = Image.fromarray(image.astype('uint8'), 'RGB')
        # Apply the same transformations as before
        image = transform(image).unsqueeze(0)
    return image


# create a module to normalize input image so we can easily put it in a
# ``nn.Sequential``
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        # .view the mean and std to make them [C x 1 x 1] so that they can
        # directly work with image Tensor of shape [B x C x H x W].
        # B is batch size. C is number of channels. H is height and W is width.
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = torch.tensor(std).view(-1, 1, 1)

    def forward(self, img):
        # normalize ``img``
        return (img - self.mean) / self.std

    
# desired depth layers to compute style/content losses :


content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']

def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img,
                               content_layers=content_layers_default,
                               style_layers=style_layers_default):
    # normalization module
    normalization = Normalization(normalization_mean, normalization_std)

    # just in order to have an iterable access to or list of content/style
    # losses
    content_losses = []
    style_losses = []

    # assuming that ``cnn`` is a ``nn.Sequential``, so we make a new ``nn.Sequential``
    # to put in modules that are supposed to be activated sequentially
    model = nn.Sequential(normalization)

    i = 0  # increment every time we see a conv
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = 'conv_{}'.format(i)
        elif isinstance(layer, nn.ReLU):
            name = 'relu_{}'.format(i)
            # The in-place version doesn't play very nicely with the ``ContentLoss``
            # and ``StyleLoss`` we insert below. So we replace with out-of-place
            # ones here.
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = 'pool_{}'.format(i)
        elif isinstance(layer, nn.BatchNorm2d):
            name = 'bn_{}'.format(i)
        else:
            raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))

        model.add_module(name, layer)

        if name in content_layers:
            # add content loss:
            target = model(content_img).detach()
            content_loss = ContentLoss(target)
            model.add_module("content_loss_{}".format(i), content_loss)
            content_losses.append(content_loss)

        if name in style_layers:
            # add style loss:
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            model.add_module("style_loss_{}".format(i), style_loss)
            style_losses.append(style_loss)

    # now we trim off the layers after the last content and style losses
    for i in range(len(model) - 1, -1, -1):
        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
            break

    model = model[:(i + 1)]

    return model, style_losses, content_losses 
                                   
def get_input_optimizer(input_img):
    # this line to show that input is a parameter that requires a gradient
    optimizer = optim.LBFGS([input_img])
    return optimizer