Spaces:
Runtime error
Runtime error
File size: 8,193 Bytes
eca77db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 |
import os
import sys
sys.path.append(os.path.abspath(os.path.pardir))
from argparse import ArgumentParser
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torchvision.models import vgg19
from PIL import Image
import matplotlib.pyplot as plt
from nst.models.vgg19 import VGG19
from nst.losses import ContentLoss, StyleLoss
from tqdm import tqdm
from typing import List, Union
def main() -> None:
# command line args
parser = ArgumentParser()
parser.add_argument("--use_gpu", default=True, type=bool)
parser.add_argument("--content_dir", default="../images/content/dancing.jpg", type=str)
parser.add_argument("--style_dir", default="../images/style/picasso.jpg", type=str)
parser.add_argument("--input_image", default="content", type=str)
parser.add_argument("--output_dir", default="../result/result.jpg", type=str)
parser.add_argument("--iterations", default=100, type=int)
parser.add_argument("--alpha", default=1, type=int)
parser.add_argument("--beta", default=1000000, type=int)
parser.add_argument("--style_layer_weight", default=1.0, type=float)
args = parser.parse_args()
device = torch.device("cuda") if (torch.cuda.is_available() and args.use_gpu) else torch.device("cpu")
print(f"training on device {device}")
# content and style images
content = image_loader(args.content_dir, device)
style = image_loader(args.style_dir, device)
# input image
if args.input_image == "content":
x = content.clone()
elif args.input_image == "style":
x = style.clone()
else:
x = torch.randn(content.data.size(), device=device)
# mean and std for vgg19
mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
std = torch.tensor([0.229, 0.224, 0.225]).to(device)
# vgg19 model
model = VGG19(mean=mean, std=std).to(device=device)
model = load_vgg19_weights(model, device)
# LBFGS optimizer like in paper
optimizer = optim.LBFGS([x.requires_grad_()])
# computing content and style representations
content_outputs = model(content)
style_outputs = model(style)
# defining content and style losses
content_loss = ContentLoss(content_outputs["conv4"][1], device)
style_losses = []
for i in range(1, 6):
style_losses.append(StyleLoss(style_outputs[f"conv{i}"][0], device))
# run style transfer
output = train(model, optimizer, content_loss, style_losses, x,
iterations=args.iterations, alpha=args.alpha, beta=args.beta,
style_weight=args.style_layer_weight)
output = output.detach().to("cpu")
# save result
plt.imsave(args.output_dir, output[0].permute(1, 2, 0).numpy())
def image_loader(path: str, device: torch.device=torch.device("cuda")) -> torch.Tensor:
"""
Loads and resizes the image.
Args:
path (str): Path to the image.
device (torch.device): device to load the image in.
Returns:
img (torch.Tensor): Loaded image as torch.Tensor.
"""
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
])
img = Image.open(path)
img = transform(img)
img = img.unsqueeze(0).to(device=device)
return img
def load_vgg19_weights(model: nn.Module, device: torch.device) -> nn.Module:
"""
Loads VGG19 pretrained weights from ImageNet for style transfer.
Args:
model (nn.Module): VGG19 feature module with randomized weights.
device (torch.device): The device to load the model in.
Returns:
model (nn.Module): VGG19 module with pretrained ImageNet weights loaded.
"""
pretrained_model = vgg19(pretrained=True).features.to(device).eval()
matching_keys = {
"conv1.conv1.weight": "0.weight",
"conv1.conv1.bias": "0.bias",
"conv1.conv2.weight": "2.weight",
"conv1.conv2.bias": "2.bias",
"conv2.conv1.weight": "5.weight",
"conv2.conv1.bias": "5.bias",
"conv2.conv2.weight": "7.weight",
"conv2.conv2.bias": "7.bias",
"conv3.conv1.weight": "10.weight",
"conv3.conv1.bias": "10.bias",
"conv3.conv2.weight": "12.weight",
"conv3.conv2.bias": "12.bias",
"conv3.conv3.weight": "14.weight",
"conv3.conv3.bias": "14.bias",
"conv3.conv4.weight": "16.weight",
"conv3.conv4.bias": "16.bias",
"conv4.conv1.weight": "19.weight",
"conv4.conv1.bias": "19.bias",
"conv4.conv2.weight": "21.weight",
"conv4.conv2.bias": "21.bias",
"conv4.conv3.weight": "23.weight",
"conv4.conv3.bias": "23.bias",
"conv4.conv4.weight": "25.weight",
"conv4.conv4.bias": "25.bias",
"conv5.conv1.weight": "28.weight",
"conv5.conv1.bias": "28.bias",
"conv5.conv2.weight": "30.weight",
"conv5.conv2.bias": "30.bias",
"conv5.conv3.weight": "32.weight",
"conv5.conv3.bias": "32.bias",
"conv5.conv4.weight": "34.weight",
"conv5.conv4.bias": "34.bias",
}
pretrained_dict = pretrained_model.state_dict()
model_dict = model.state_dict()
for key, value in matching_keys.items():
model_dict[key] = pretrained_dict[value]
model.load_state_dict(model_dict)
return model
def train(model: nn.Module, optimizer: torch.optim, content_loss: ContentLoss, style_losses: List[StyleLoss],
x: torch.Tensor, iterations: int=100, alpha: int=1, beta: int=1000000, style_weight: Union[int, float]=1.0) -> torch.Tensor:
"""
Train the neural style transfer algorithm.
Args:
model (nn.Module): The VGG19 feature extractor for training the style transfer algorithm.
optimizer (torch.optim): The optimization module to use.
content_loss (ContentLoss): The content loss to preserve the content representation during style transfer.
style_losses (List[StyleLoss]): A list of style loss objects to preserve the style representation across
different layers during style transfer.
x (torch.Tensor): The input image for style transfer.
iterations (int): Number of iterations to run.
alpha (int): The weight given to content loss while computing the total loss.
beta (int): The weight given to style loss while computing the total loss.
style_weight Union[int, float]: The weight given to style loss of each layer while computing total style loss.
Returns:
x (torch.Tensor): The input image with the content and style transfered.
"""
with tqdm(range(iterations)) as iterations:
for iteration in iterations:
iterations.set_description(f"Iteration: {iteration}")
def closure():
optimizer.zero_grad()
# correcting to 0-1 range
x.data.clamp_(0, 1)
outputs = model(x)
# input content and style representations
content_feature_maps = outputs["conv4"][1]
style_feature_maps = []
for i in range(1, 6):
style_feature_maps.append(outputs[f"conv{i}"][0])
# input content and style losses
total_content_loss = content_loss(content_feature_maps)
total_style_loss = 0
for feature_map, style_loss in zip(style_feature_maps, style_losses):
total_style_loss += (style_weight * style_loss(feature_map))
# total loss
loss = (alpha * total_content_loss) + (beta * total_style_loss)
loss.backward()
iterations.set_postfix({
"content loss": total_content_loss.item(),
"style loss": total_style_loss.item(),
"total loss": loss.item()
})
return loss
optimizer.step(closure)
# final correction
x.data.clamp_(0, 1)
return x
if __name__ == "__main__":
main() |