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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, self.target) * self.strength | |
elif self.mode == 'capture': | |
self.target = Variable((input.clone().data),requires_grad=False).detach() | |
self.output = input | |
return self.output | |
def backward(self, grad_input, grad_output): | |
#print("ContentLoss Backward Hook") | |
i = 0 | |
gradInputTuple = () | |
gradOutput = grad_input[0] | |
for input in grad_input: | |
if input.nelement() == grad_output[0].nelement(): | |
if input.nelement() == self.target.nelement(): | |
diff = Variable((input.clone().data),requires_grad=True) | |
diff.backward(self.target) #self.target,retain_graph=True | |
self.gradInput = diff.grad | |
#print("gradInput.size(): " + str(self.gradInput.size())) | |
if self.normalize == 'True': | |
self.gradInput = self.gradInput.div(torch.norm(self.gradInput, 1) + 1e-8) # Normalize Gradients | |
self.gradInput = self.gradInput.mul(self.strength) | |
self.gradInput = self.gradInput.add(gradOutput) | |
else: | |
self.gradInput = input | |
i = i + 1 | |
gradInputTuple = list(gradInputTuple) | |
gradInputTuple.append(self.gradInput) | |
gradInputTuple = tuple(gradInputTuple) | |
return gradInputTuple | |
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()) | |
self.gradInput = torch.mm(gradOutput, x_flat) | |
self.gradInput.addmm(gradOutput.t(), x_flat) | |
self.gradInput = self.gradInput.view(C, H, W) | |
#print(self.gradInput.size()) | |
self.gradInput = self.gradInput.unsqueeze(0) | |
#print("fake batch") | |
#print(self.gradInput.size()) | |
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 = self.G.div(input.nelement()) | |
if self.mode == 'capture': | |
if self.blend_weight == None: | |
self.target = Variable((self.G.clone().data),requires_grad=False) | |
elif self.target.nelement() == 0: | |
self.target = self.G * self.blend_weight | |
else: | |
self.target = self.target + self.blend_weight * self.G | |
elif self.mode == 'loss': | |
self.loss = self.strength * self.crit(self.G, self.target) | |
return self.output | |
def backward(self, grad_input, grad_output): | |
#print("StyleLoss Backward Hook") | |
#print("self.G: " + str(self.G.size())) | |
#print("self.target: " + str(self.target.size())) | |
#print("grad_output[0]: " + str(grad_output[0].size())) | |
i = 0 | |
gradInputTuple = () | |
gradOutput = grad_input[0] | |
for input in grad_input: | |
if input.nelement() == grad_output[0].nelement(): | |
#print("test") | |
self.G2 = Variable((self.G.clone().data),requires_grad=True) | |
self.G2.backward(self.target,retain_graph=True) #,retain_graph=True | |
dG = self.G2.grad | |
#print("dG: " + str(dG)) | |
self.gradInput = self.gram.backward(input, dG) | |
if self.normalize == 'True': | |
self.gradInput = self.gradInput.div_(torch.norm(self.gradInput, 1) + 1e-8) # Normalize Gradients | |
self.gradInput = self.gradInput.mul_(self.strength) | |
self.gradInput = self.gradInput.add_(gradOutput) | |
else: | |
self.gradInput = input | |
gradInputTuple = list(gradInputTuple) | |
gradInputTuple.append(self.gradInput) | |
gradInputTuple = tuple(gradInputTuple) | |
return gradInputTuple |
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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) | |
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) | |
loss_module.register_backward_hook(ContentLoss.backward) | |
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) | |
loss_module.register_backward_hook(StyleLoss.backward) | |
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 = models.vgg19() | |
print(cnn) | |
quit() | |
cnn.load_state_dict(torch.load(params.model_file)) | |
cnn = cnn.features | |
if "vgg19" in str(params.model_file): | |
Layer_List = VGG19_Layer_List | |
print("VGG-19 Architecture Detected") | |
elif "vgg16" in str(params.model_file): | |
Layer_List = VGG16_Layer_List | |
print("VGG-16 Architecture Detected") | |
# 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] <= 1: | |
def feval(): | |
num_calls[0] += 1 | |
img.data.clamp_(-1, 1) | |
#optimizer.zero_grad() | |
print(torch.mean(img.data)) | |
net(img) | |
optimizer.zero_grad() | |
loss = 0 | |
for mod in content_losses: | |
#mod.loss.backward() | |
loss = loss + mod.loss | |
for mod in style_losses: | |
#mod.backward(img, dy) | |
loss = loss + mod.loss | |
#loss.backward(retain_graph=True) | |
loss.backward() | |
maybe_print(num_calls[0], loss) | |
maybe_save(num_calls[0]) | |
return loss | |
optimizer.step(feval) |
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