LossModules2.py

import torch
import torch.nn as nn
#import torch.legacy.nn as lnn
import torchvision
from torch.autograd import Variable


class ContentLoss(nn.Module):

    def __init__(self, strength, normalize):
        super(ContentLoss, self).__init__()
        self.target = Variable((torch.Tensor()),requires_grad=False).detach()
        self.strength = strength
        self.crit = nn.MSELoss()
        self.mode = None
        self.normalize = 'False'

    def forward(self, input):
        if self.mode == 'loss':
            self.loss = self.crit(input.cpu(), self.target.cpu())  * self.strength
        elif self.mode == 'capture':
            self.target.data.resize_as_(input.cpu().data).copy_(input.cpu().data)   
        self.output = input
        return self.output

    def backward(self, input, gradOutput, retain_graph=True):
        if self.mode == 'loss':
          if input.nelement() == self.target.nelement():
             self.loss.backward(retain_graph=retain_graph)
             #G = Variable(self.target.data, requires_grad=True) #This works?
             #G.backward(input.data) #This works?
          if self.normalize == 'True':
             self.loss.div(torch.norm(self.loss, 1) + 1e-8) # Normalize Gradients
          self.loss.mul(self.strength)
          self.loss.add(gradOutput)
        else:
          self.target.resize_as_(gradOutput).copy_(gradOutput)
        return self.loss

class GramMatrix(nn.Module):

    def forward(self, input):
        print("input - gram - forward " + str(input.size())) 
        B, C, H, W = input.size()
        x_flat = input.view(B * C, H * W)
        print("x_flat - gram - forward " + str(x_flat.size())) 
        self.output = torch.mm(x_flat, x_flat.t())
        self.output.div_(H*W)
        return self.output
		
    def backward(self, input, gradOutput):
        #gradOutput = gradOutput.grad.div(input.nelement())
        print("input - gram - backward " + str(input.size())) 
        print("gradOutput - gram - backward " + str(gradOutput.size())) 
	B, C, H, W = input.size()
	x_flat = input.view(B * C, H * W)
        print("x_flat - gram - backward " + str(x_flat.size())) 
        print(gradOutput.size())
        print(x_flat.size())
        quit()
	self.gradInput = torch.mm(gradOutput, x_flat) 
        self.gradInput.addmm(gradOutput.t(), x_flat)
        self.gradInput = self.gradInput.view(C, H, W)
	return self.gradInput


#def BackwardsStyleHook(self, input, gradOutput)

class StyleLoss(nn.Module):

    def __init__(self, strength, normalize):
        super(StyleLoss, self).__init__()
        self.target = Variable((torch.Tensor()),requires_grad=False).detach()
        self.strength = strength
        self.gram = GramMatrix()
        self.crit = nn.MSELoss()
        self.mode = None
        self.blend_weight = None
        self.G = None
        self.normalize = 'False'


    def forward(self, input):
        self.output = input.clone()
        self.G = self.gram.forward(input)
        self.G.div(input.nelement())
        if self.mode == 'capture':
          if self.blend_weight == None:
            self.target.data.resize_as_(self.G.cpu().data).copy_(self.G.cpu().data)
          elif self.target.nelement() == 0:
            self.target.data.resize_as_(self.G.cpu().data).copy_(self.G.cpu().data).mul_(self.blend_weight)
          else:
            self.target.data.add(self.blend_weight, self.G.data)
        elif self.mode == 'loss':
            self.loss = self.strength * self.crit(self.G.cpu(), self.target.cpu())
        return self.output

    def backward(self, input, gradOutput):
        if self.mode == 'loss':
          #dG = Variable(self.G.data, requires_grad=True) #This works?
          #dG.backward(self.target.data) #This works?
          dG = Variable(self.target.data, requires_grad=True) #This works?
          dG.backward(self.G.data) #This works?
          self.gradInput = self.gram.backward(input, dG.grad)
          if self.normalize == 'True':
	     self.gradInput.div(torch.norm(self.gradInput, 1) + 1e-8) # Normalize Gradients
	  self.gradInput.mul(self.strength)
          self.gradInput.add(gradOutput)
        else:
          self.gradInput = gradOutput
        return self.gradInput

st.py

import torch
import torch.nn as nn
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from torch.autograd import Variable, Function
import torch.optim as optim

from PIL import Image
import os
import sys
from LossModules2 import ContentLoss
from LossModules2 import StyleLoss
from LossModules2 import GramMatrix

import argparse
parser = argparse.ArgumentParser()
# Basic options
parser.add_argument("-style_image", help="Style target image", default='examples/inputs/seated-nude.jpg')
parser.add_argument("-content_image", help="Content target image", default='examples/inputs/tubingen.jpg')
parser.add_argument("-image_size", help="Maximum height / width of generated image", type=int, default=512)

# Optimization options
parser.add_argument("-num_iterations", help="iterations", type=int, default=1000)
parser.add_argument("-optimizer", help="optimiser", default="lbfgs", choices=["lbfgs", "adam"])
parser.add_argument("-learning_rate", default=1)
# Output options      
parser.add_argument("-print_iter", type=int, default=50)
parser.add_argument("-save_iter", type=int, default=100)
parser.add_argument("-output_image", default='out.png')
# Other options
parser.add_argument("-model_file", help="VGG 19 model file location", type=str, default='vgg19-d01eb7cb.pth')
parser.add_argument("-seed", help="random number seed", type=int, default=-1)
params = parser.parse_args()

use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor

# Initialize the image
if params.seed >= 0:
    torch.manual_seed(params.seed)
    torch.cuda.manual_seed(params.seed)

def ImageSetup2(image_name, image_size):
    image = Image.open(image_name)
    image = image.convert('RGB')
    Loader = transforms.Compose([transforms.Resize((image_size)), transforms.ToTensor()])  # resize and convert to tensor
    #Normalize = transforms.Compose([transforms.Normalize(mean=[ 0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])]) # BGR STD & Mean
    #image = Variable(Normalize(Loader(image)))
    image = Variable(Loader(image))
    image = image.unsqueeze(0)
    print(image.size())
    return image

def ImageSetup(image_name, image_size):
    image = Image.open(image_name)
    image = image.convert('RGB')
    Loader = transforms.Compose([transforms.Resize((image_size)), transforms.ToTensor()])  # resize and convert to tensor
    Normalize = transforms.Compose([transforms.Normalize(mean=[ 0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])]) # BGR STD & Mean
    image = Variable(Normalize(Loader(image)).clamp_(-1, 1))
    image = image.unsqueeze(0)
    return image
	
def SaveImage(output_img, output_name): 
   torchvision.utils.save_image(output_img, output_name, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)

#from ImageStuff import preprocess, deprocess
#content_image = preprocess(params.content_image).cpu()
#style_image = preprocess(params.style_image).cpu()

content_image = ImageSetup2(params.content_image, params.image_size).cpu()
style_image = ImageSetup2(params.style_image, params.image_size).cpu()



# Separate names for layers
VGG19_Layer_List = ['conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5', 'torch_view', 'fc6', 'relu6', 'drop6', 'fc7', 'relu7', 'drop7', 'fc8', 'prob']
VGG16_layer_List = ['conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'pool5', 'torch_view', 'fc6', 'relu6', 'drop6', 'fc7', 'relu7', 'drop7', 'fc8', 'prob']
NIN_Layer_List = ['conv1', 'relu0', 'cccp1', 'relu1', 'cccp2', 'relu2', 'pool0', 'conv2', 'relu3', 'cccp3', 'relu5', 'cccp4', 'relu6', 'pool2', 'conv3', 'relu7', 'cccp5', 'relu8', 'cccp6', 'relu9', 'pool3', 'drop', 'conv4-1024', 'relu10', 'cccp7-1024', 'relu11', 'cccp8-1024', 'relu12', 'pool4', 'loss']

  
def ModelSetup(cnn, style_weight, content_weight, Layer_List, content_layers, style_layers, normalize_gradients):
    content_losses = []
    style_losses = []
    next_content_idx = 1
    next_style_idx = 1
    net = nn.Sequential()
    net = net.cpu()
	  
    i = 0
    for layer in list(cnn):
        l = int(i)
        layer_name = Layer_List[l]

        if "conv" in layer_name:
            net.add_module(layer_name, layer)
            if layer_name in content_layers:
                print("Setting up content layer " + str(next_content_idx) + ": " + str(layer_name))
                norm = normalize_gradients
                loss_module = ContentLoss(content_weight, norm)
                net.add_module(layer_name, loss_module)
                content_losses.append(loss_module)
                next_content_idx = next_content_idx + 1
            if layer_name in style_layers:
                print("Setting up style layer " + str(next_style_idx) + ": " + str(layer_name))
                norm = normalize_gradients
                loss_module = StyleLoss(style_weight, norm)
                net.add_module(layer_name, loss_module)
                style_losses.append(loss_module)
                next_style_idx = next_style_idx + 1

        if "relu" in layer_name:
            net.add_module(layer_name, layer)
            if layer_name in content_layers:
                print("Setting up content layer " + str(next_content_idx) + ": " + str(layer_name))
                norm = normalize_gradients
                loss_module = ContentLoss(content_weight, norm)
                net.add_module(layer_name, loss_module)
                content_losses.append(loss_module)
                next_content_idx = next_content_idx + 1
            if layer_name in style_layers:
                print("Setting up style layer " + str(next_style_idx) + ": " + str(layer_name))
                norm = normalize_gradients
                loss_module = StyleLoss(style_weight, norm)
                net.add_module(layer_name, loss_module)
                style_losses.append(loss_module)
                next_style_idx = next_style_idx + 1
				
        if "pool" in layer_name:
            net.add_module(layer_name, layer)  # ***
			
        i = i + 1
    cnn = None
    return net, style_losses, content_losses


model_type ='vgg19' # Default value for testing
style_weight = 1000 # Default value for testing
content_weight = 100 # Default value for testing
normalize_gradients = 'False' # Default value for testing
content_layers = ['relu4_2'] # Default value for testing
style_layers = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1'] # Default value for testing
max_iter = 1000 # Default value for testing


cnn = None
Layer_List = []
model_name = os.path.splitext(params.model_file)[0].split('-')[0]
print(model_name)
cnn = getattr(models, model_name)()
cnn.load_state_dict(torch.load(params.model_file))
cnn = cnn.features

if model_type == 'vgg19':
   #cnn = models.vgg19(pretrained=True).features
   Layer_List = VGG19_Layer_List
elif model_type == 'vgg16':
   #cnn = models.vgg16(pretrained=True).features
   Layer_List = VGG16_Layer_List



# Build the style transfer model:
net, style_losses, content_losses = ModelSetup(cnn, style_weight, content_weight, Layer_List, content_layers, style_layers, normalize_gradients)
net = net.cpu()

img = content_image.clone()
img = nn.Parameter(img.data,requires_grad=True)
content_image = nn.Parameter(content_image.data,requires_grad=True)
style_image = nn.Parameter(style_image.data,requires_grad=True)



# Capture content targets
for i in content_losses:
  i.mode = 'capture'
net(content_image).cpu()

print("Capturing content targets")
# Capture style targets
for i in content_losses:
  i.mode = None
for j in style_losses:
  j.mode = 'capture'
  #j.blend_weight = style_blend_weights[i]
net(style_image)

# Set all loss modules to loss mode
for i in content_losses:
  i.mode = 'loss'
for i in style_losses:
  i.mode = 'loss'


def maybe_print(t, loss): 
   if params.print_iter > 0 and t % params.print_iter == 0:
    print("Iteration: " + str(t) + " / "+ str(params.num_iterations)) 
    c = 1
    for i in content_losses:
      print("  Content " + str(c) + " loss: "+ "loss value recording is broken")
      c = c+1
    s = 1 
    for i in style_losses:
      print("  Style " + str(s) + " loss: "+ "loss value recording is broken") 
      s = s+1
    print("  Total loss " + "loss value recording is broken") 

def maybe_save(t):   
  should_save = params.save_iter > 0 and t % params.save_iter == 0
  should_save = should_save or t == params.num_iterations
  if should_save:
      output_filename, file_extension = os.path.splitext(params.output_image)
      if t == params.num_iterations:
        filename = output_filename + str(file_extension)
      else:
        filename = str(output_filename) + "_" + str(t) + str(file_extension)
      SaveImage(img.data, filename)

#optim_state = None
#if params.optimizer == 'lbfgs':
  #optim_state = (max_iter = params.num_iterations, tolerance_change = -1, tolerance_grad = -1)
#elif params.optimizer == 'adam':
#  optim_state = {
#    "lr": 1,
#  }

optimizer = None
# Run optimization.
if params.optimizer == 'lbfgs':
  print("Running optimization with L-BFGS")
  optimizer = optim.LBFGS([img], max_iter = params.num_iterations, tolerance_change = -1, tolerance_grad = -1)
elif params.optimizer == 'adam':
  print("Running optimization with ADAM")
  for t in xrange(params.num_iterations):
    optimizer = optim.Adam([img], lr = params.learning_rate)

y = net(img) 
dy = Variable(y.data.resize_as_(content_image.data).zero_())

num_calls = [0]
while num_calls[0] <= params.num_iterations:
  def feval():
    num_calls[0] += 1
    img.data.clamp_(-1, 1)
    optimizer.zero_grad()
    #print(torch.mean(img.data))
    net(img)
    loss = 0
    for mod in content_losses:
      mod.backward(img, dy)
      loss = loss + mod.loss
    for mod in style_losses:
      mod.backward(img, dy)
      loss = loss + mod.loss
    maybe_print(num_calls[0], loss)
    maybe_save(num_calls[0])
    return -loss
  optimizer.step(feval)