<reponame>i-dubits/tg-bot-style
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 18:57:02 2021

@author: Ilya
"""


from __future__ import print_function

import asyncio

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from PIL import Image
import matplotlib.pyplot as plt

import torchvision.transforms as transforms
import torchvision.models as models

import copy

from bot import config

class Image_proc():
    
    def __init__(self, imsize, device):
        
        self.imsize = imsize
        self.device = device
        self.loader = transforms.Compose([                 
            transforms.Resize(imsize),  # scale imported image
            transforms.CenterCrop(imsize),
            transforms.ToTensor()])   

    async def image_loader(self, image_name):
        image = Image.open(image_name)
        # fake batch dimension required to fit network's input dimensions
        image = self.loader(image).unsqueeze(0)
        return image.to(self.device, torch.float)  
    
    async def my_imsave(self, tensor, filename='output.png'):
        image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
        image = image.squeeze(0)      # remove the fake batch dimension
        unloader = transforms.ToPILImage()
        image = unloader(image)
        image.save(filename)
        #set_trace()    
        
        
# Losses

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()
            self.loss = F.mse_loss(self.target, self.target ) #to initialize with something

        def forward(self, input):
            self.loss = F.mse_loss(input, self.target)
            return input
        
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)  # resise 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()
        self.loss = F.mse_loss(self.target, self.target)# to initialize with something

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

        
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
        
class Run_NST():
    
        def __init__(self, style_img, content_img, device):
            
            self.style_img = style_img
            self.content_img = content_img
            self.device = device
            self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(self.device)
            self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(self.device)
            
            self.content_layers_default = ['conv_4']
            self.style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
            
            self.cnn =  models.vgg19()
            self.cnn.load_state_dict(torch.load(config.nst_state_dict))
            self.cnn = self.cnn.features.to(device).eval()
            
            
        async def get_style_model_and_losses(self):
            cnn = copy.deepcopy(self.cnn)
        
            # normalization module
            normalization = Normalization(self.cnn_normalization_mean, self.cnn_normalization_std).to(self.device)
        
            # just in order to have an iterable access to or list of content/syle
            # 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:
                    #set_trace()
                    raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
        
                model.add_module(name, layer)
        
                if name in self.content_layers_default:
                    # add content loss:
                    target = model(self.content_img).detach()
                    content_loss = ContentLoss(target)
                    model.add_module("content_loss_{}".format(i), content_loss)
                    content_losses.append(content_loss)
        
                if name in self.style_layers_default:
                    # add style loss:
                    target_feature = model(self.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
              
            
        async def run_style_transfer(self, num_steps=300, style_weight=1000000, content_weight=1):
            """Run the style transfer."""
            print('Building the style transfer model..')
            model, style_losses, content_losses = await self.get_style_model_and_losses()
            
            input_img = self.content_img.clone().detach()
            optimizer = optim.LBFGS([input_img.requires_grad_()])
        
            print('Optimizing..')
            run = [0]
            while run[0] <= num_steps:
        
                def closure():
                    # correct the values of updated input image
                    input_img.data.clamp_(0, 1)
        
                    optimizer.zero_grad()
                    model(input_img)
                    style_score = 0
                    content_score = 0
        
                    for sl in style_losses:
                        style_score += sl.loss
                    for cl in content_losses:
                        content_score += cl.loss
        
                    style_score *= style_weight
                    content_score *= content_weight
        
                    loss = style_score + content_score
                    loss.backward()
        
                    run[0] += 1
                    if run[0] % 50 == 0:
                        print("run {}:".format(run))
                        print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                            style_score.item(), content_score.item()))
                        print()
        
                    return style_score + content_score
        
                optimizer.step(closure)
                await asyncio.sleep(0.4)
        
            # a last correction...
            input_img.data.clamp_(0, 1)
        
            return input_img
            
        
            
            
            
            
            
            
            
            
            
            
            
            
        
        
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 18:57:02 2021

@author: Ilya
"""


from __future__ import print_function

import asyncio

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from PIL import Image
import matplotlib.pyplot as plt

import torchvision.transforms as transforms
import torchvision.models as models

import copy

from bot import config

class Image_proc():
    
    def __init__(self, imsize, device):
        
        self.imsize = imsize
        self.device = device
        self.loader = transforms.Compose([                 
            transforms.Resize(imsize),  # scale imported image
            transforms.CenterCrop(imsize),
            transforms.ToTensor()])   

    async def image_loader(self, image_name):
        image = Image.open(image_name)
        # fake batch dimension required to fit network's input dimensions
        image = self.loader(image).unsqueeze(0)
        return image.to(self.device, torch.float)  
    
    async def my_imsave(self, tensor, filename='output.png'):
        image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
        image = image.squeeze(0)      # remove the fake batch dimension
        unloader = transforms.ToPILImage()
        image = unloader(image)
        image.save(filename)
        #set_trace()    
        
        
# Losses

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()
            self.loss = F.mse_loss(self.target, self.target ) #to initialize with something

        def forward(self, input):
            self.loss = F.mse_loss(input, self.target)
            return input
        
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)  # resise 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()
        self.loss = F.mse_loss(self.target, self.target)# to initialize with something

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

        
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
        
class Run_NST():
    
        def __init__(self, style_img, content_img, device):
            
            self.style_img = style_img
            self.content_img = content_img
            self.device = device
            self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(self.device)
            self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(self.device)
            
            self.content_layers_default = ['conv_4']
            self.style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
            
            self.cnn =  models.vgg19()
            self.cnn.load_state_dict(torch.load(config.nst_state_dict))
            self.cnn = self.cnn.features.to(device).eval()
            
            
        async def get_style_model_and_losses(self):
            cnn = copy.deepcopy(self.cnn)
        
            # normalization module
            normalization = Normalization(self.cnn_normalization_mean, self.cnn_normalization_std).to(self.device)
        
            # just in order to have an iterable access to or list of content/syle
            # 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:
                    #set_trace()
                    raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
        
                model.add_module(name, layer)
        
                if name in self.content_layers_default:
                    # add content loss:
                    target = model(self.content_img).detach()
                    content_loss = ContentLoss(target)
                    model.add_module("content_loss_{}".format(i), content_loss)
                    content_losses.append(content_loss)
        
                if name in self.style_layers_default:
                    # add style loss:
                    target_feature = model(self.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
              
            
        async def run_style_transfer(self, num_steps=300, style_weight=1000000, content_weight=1):
            """Run the style transfer."""
            print('Building the style transfer model..')
            model, style_losses, content_losses = await self.get_style_model_and_losses()
            
            input_img = self.content_img.clone().detach()
            optimizer = optim.LBFGS([input_img.requires_grad_()])
        
            print('Optimizing..')
            run = [0]
            while run[0] <= num_steps:
        
                def closure():
                    # correct the values of updated input image
                    input_img.data.clamp_(0, 1)
        
                    optimizer.zero_grad()
                    model(input_img)
                    style_score = 0
                    content_score = 0
        
                    for sl in style_losses:
                        style_score += sl.loss
                    for cl in content_losses:
                        content_score += cl.loss
        
                    style_score *= style_weight
                    content_score *= content_weight
        
                    loss = style_score + content_score
                    loss.backward()
        
                    run[0] += 1
                    if run[0] % 50 == 0:
                        print("run {}:".format(run))
                        print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                            style_score.item(), content_score.item()))
                        print()
        
                    return style_score + content_score
        
                optimizer.step(closure)
                await asyncio.sleep(0.4)
        
            # a last correction...
            input_img.data.clamp_(0, 1)
        
            return input_img
# -*- coding: utf-8 -*- Created on Mon Jan 25 18:57:02 2021  @author: Ilya # scale imported image # fake batch dimension required to fit network's input dimensions # we clone the tensor to not do changes on it # remove the fake batch dimension #set_trace() # Losses # 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. #to initialize with something # a=batch size(=1) # b=number of feature maps # (c,d)=dimensions of a f. map (N=c*d) # resise F_XL into \hat F_XL # compute the gram product # we 'normalize' the values of the gram matrix # by dividing by the number of element in each feature maps. # to initialize with something # .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. # normalize img # normalization module # just in order to have an iterable access to or list of content/syle # 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 # increment every time we see a conv # 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. #set_trace() # add content loss: # add style loss: # now we trim off the layers after the last content and style losses Run the style transfer. # correct the values of updated input image # a last correction...