292 lines
12 KiB
Python
292 lines
12 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision.transforms as transforms
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import torchvision.models as models
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from PIL import Image
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import copy
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import pydiffvg
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import argparse
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def main(args):
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pydiffvg.set_use_gpu(torch.cuda.is_available())
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canvas_width, canvas_height, shapes, shape_groups = pydiffvg.svg_to_scene(args.content_file)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(canvas_width, # width
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canvas_height, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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None,
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*scene_args)
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# Transform to gamma space
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pydiffvg.imwrite(img.cpu(), 'results/style_transfer/init.png', gamma=1.0)
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# HWC -> NCHW
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img = img.unsqueeze(0)
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img = img.permute(0, 3, 1, 2) # NHWC -> NCHW
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loader = transforms.Compose([
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transforms.ToTensor()]) # transform it into a torch tensor
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def image_loader(image_name):
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image = Image.open(image_name)
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# fake batch dimension required to fit network's input dimensions
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image = loader(image).unsqueeze(0)
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return image.to(pydiffvg.get_device(), torch.float)
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style_img = image_loader(args.style_img)
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# alpha blend content with a gray background
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content_img = img[:, :3, :, :] * img[:, 3, :, :] + \
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0.5 * torch.ones([1, 3, img.shape[2], img.shape[3]]) * \
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(1 - img[:, 3, :, :])
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assert style_img.size() == content_img.size(), \
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"we need to import style and content images of the same size"
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unloader = transforms.ToPILImage() # reconvert into PIL image
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class ContentLoss(nn.Module):
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def __init__(self, target,):
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super(ContentLoss, self).__init__()
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# we 'detach' the target content from the tree used
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# to dynamically compute the gradient: this is a stated value,
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# not a variable. Otherwise the forward method of the criterion
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# will throw an error.
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self.target = target.detach()
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def forward(self, input):
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self.loss = F.mse_loss(input, self.target)
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return input
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def gram_matrix(input):
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a, b, c, d = input.size() # a=batch size(=1)
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# b=number of feature maps
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# (c,d)=dimensions of a f. map (N=c*d)
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features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
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G = torch.mm(features, features.t()) # compute the gram product
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# we 'normalize' the values of the gram matrix
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# by dividing by the number of element in each feature maps.
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return G.div(a * b * c * d)
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class StyleLoss(nn.Module):
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def __init__(self, target_feature):
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super(StyleLoss, self).__init__()
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self.target = gram_matrix(target_feature).detach()
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def forward(self, input):
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G = gram_matrix(input)
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self.loss = F.mse_loss(G, self.target)
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return input
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device = pydiffvg.get_device()
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cnn = models.vgg19(pretrained=True).features.to(device).eval()
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cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
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cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
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# create a module to normalize input image so we can easily put it in a
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# nn.Sequential
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class Normalization(nn.Module):
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def __init__(self, mean, std):
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super(Normalization, self).__init__()
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# .view the mean and std to make them [C x 1 x 1] so that they can
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# directly work with image Tensor of shape [B x C x H x W].
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# B is batch size. C is number of channels. H is height and W is width.
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self.mean = mean.clone().view(-1, 1, 1)
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self.std = std.clone().view(-1, 1, 1)
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def forward(self, img):
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# normalize img
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return (img - self.mean) / self.std
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# desired depth layers to compute style/content losses :
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content_layers_default = ['conv_4']
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style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
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def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
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style_img, content_img,
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content_layers=content_layers_default,
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style_layers=style_layers_default):
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cnn = copy.deepcopy(cnn)
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# normalization module
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normalization = Normalization(normalization_mean, normalization_std).to(device)
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# just in order to have an iterable access to or list of content/syle
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# losses
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content_losses = []
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style_losses = []
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# assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
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# to put in modules that are supposed to be activated sequentially
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model = nn.Sequential(normalization)
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i = 0 # increment every time we see a conv
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for layer in cnn.children():
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if isinstance(layer, nn.Conv2d):
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i += 1
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name = 'conv_{}'.format(i)
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elif isinstance(layer, nn.ReLU):
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name = 'relu_{}'.format(i)
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# The in-place version doesn't play very nicely with the ContentLoss
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# and StyleLoss we insert below. So we replace with out-of-place
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# ones here.
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layer = nn.ReLU(inplace=False)
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elif isinstance(layer, nn.MaxPool2d):
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name = 'pool_{}'.format(i)
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elif isinstance(layer, nn.BatchNorm2d):
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name = 'bn_{}'.format(i)
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else:
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raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
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model.add_module(name, layer)
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if name in content_layers:
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# add content loss:
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target = model(content_img).detach()
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content_loss = ContentLoss(target)
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model.add_module("content_loss_{}".format(i), content_loss)
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content_losses.append(content_loss)
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if name in style_layers:
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# add style loss:
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target_feature = model(style_img).detach()
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style_loss = StyleLoss(target_feature)
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model.add_module("style_loss_{}".format(i), style_loss)
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style_losses.append(style_loss)
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# now we trim off the layers after the last content and style losses
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for i in range(len(model) - 1, -1, -1):
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if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
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break
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model = model[:(i + 1)]
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return model, style_losses, content_losses
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def run_style_transfer(cnn, normalization_mean, normalization_std,
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content_img, style_img,
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canvas_width, canvas_height,
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shapes, shape_groups,
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num_steps=500, style_weight=5000, content_weight=1):
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"""Run the style transfer."""
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print('Building the style transfer model..')
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model, style_losses, content_losses = get_style_model_and_losses(cnn,
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normalization_mean, normalization_std, style_img, content_img)
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point_params = []
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color_params = []
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stroke_width_params = []
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for shape in shapes:
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if isinstance(shape, pydiffvg.Path):
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point_params.append(shape.points.requires_grad_())
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stroke_width_params.append(shape.stroke_width.requires_grad_())
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for shape_group in shape_groups:
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if isinstance(shape_group.fill_color, torch.Tensor):
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color_params.append(shape_group.fill_color.requires_grad_())
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elif isinstance(shape_group.fill_color, pydiffvg.LinearGradient):
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point_params.append(shape_group.fill_color.begin.requires_grad_())
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point_params.append(shape_group.fill_color.end.requires_grad_())
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color_params.append(shape_group.fill_color.stop_colors.requires_grad_())
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if isinstance(shape_group.stroke_color, torch.Tensor):
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color_params.append(shape_group.stroke_color.requires_grad_())
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elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient):
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point_params.append(shape_group.stroke_color.begin.requires_grad_())
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point_params.append(shape_group.stroke_color.end.requires_grad_())
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color_params.append(shape_group.stroke_color.stop_colors.requires_grad_())
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point_optimizer = optim.Adam(point_params, lr=1.0)
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color_optimizer = optim.Adam(color_params, lr=0.01)
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stroke_width_optimizers = optim.Adam(stroke_width_params, lr=0.1)
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print('Optimizing..')
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run = [0]
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while run[0] <= num_steps:
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point_optimizer.zero_grad()
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color_optimizer.zero_grad()
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stroke_width_optimizers.zero_grad()
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(canvas_width, # width
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canvas_height, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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None,
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*scene_args)
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# alpha blend img with a gray background
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img = img[:, :, :3] * img[:, :, 3:4] + \
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0.5 * torch.ones([img.shape[0], img.shape[1], 3]) * \
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(1 - img[:, :, 3:4])
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pydiffvg.imwrite(img.cpu(),
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'results/style_transfer/step_{}.png'.format(run[0]),
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gamma=1.0)
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# HWC to NCHW
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img = img.permute([2, 0, 1]).unsqueeze(0)
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model(img)
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style_score = 0
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content_score = 0
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for sl in style_losses:
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style_score += sl.loss
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for cl in content_losses:
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content_score += cl.loss
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style_score *= style_weight
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content_score *= content_weight
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loss = style_score + content_score
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loss.backward()
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run[0] += 1
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if run[0] % 1 == 0:
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print("run {}:".format(run))
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print('Style Loss : {:4f} Content Loss: {:4f}'.format(
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style_score.item(), content_score.item()))
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print()
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point_optimizer.step()
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color_optimizer.step()
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stroke_width_optimizers.step()
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for color in color_params:
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color.data.clamp_(0, 1)
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for w in stroke_width_params:
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w.data.clamp_(0.5, 4.0)
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return shapes, shape_groups
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shapes, shape_groups = run_style_transfer(\
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cnn, cnn_normalization_mean, cnn_normalization_std,
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content_img, style_img,
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canvas_width, canvas_height, shapes, shape_groups)
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scene_args = pydiffvg.RenderFunction.serialize_scene(shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(canvas_width, # width
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canvas_height, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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None,
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*scene_args)
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# Transform to gamma space
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pydiffvg.imwrite(img.cpu(), 'results/style_transfer/output.png', gamma=1.0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("content_file", help="source SVG path")
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parser.add_argument("style_img", help="target image path")
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args = parser.parse_args()
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main(args)
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