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77b48d9 | 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 | import argparse
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from pathlib import Path
from utils.utils import *
from utils.models import *
from tqdm import tqdm
from torchvision.utils import save_image
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--content_dir', type=str, default='/home/ubuntu/Desktop/NST_Code/content_data',
help='Location of content dataset')
parser.add_argument('--style_dir', type=str, default='/home/ubuntu/Desktop/NST_Code/style_data',
help='Location of style dataset')
parser.add_argument('--vgg', type=str, default='/home/ubuntu/Desktop/NST_Code/vgg_normalised.pth',
help='Location of pre-trained VGG')
parser.add_argument('--experiment', type=str, default='experiment1',
help='Name of experiment')
parser.add_argument('--final_size', type=int, default=256,
help='Size of final image')
parser.add_argument('--content_size', type=int, default=512,
help='Size of content image')
parser.add_argument('--style_size', type=int, default=512,
help='Size of style image')
parser.add_argument('--crop', action='store_true', default=True,
help='Crop image')
parser.add_argument('--batch_size', type=int, default=4,
help='Batch size')
parser.add_argument('--lr', type=float, default=1e-4,
help='Learning rate')
parser.add_argument('--lr_decay', type=float, default=5e-5,
help='Learning rate decay')
parser.add_argument('--epochs', type=int, default=1,
help='Number of epochs')
parser.add_argument('--content_weight', type=float, default=1.0,
help='Content weight')
parser.add_argument('--style_weight', type=float, default=5,
help='Style weight')
parser.add_argument('--log_interval', type=int, default=1,
help='Log interval')
parser.add_argument('--save_interval', type=int, default=2,
help='Save interval')
parser.add_argument('--resume', action='store_true', default=False,
help='Resume training')
parser.add_argument('--decoder_path', type=str, default=None,
help='Path to decoder checkpoint')
parser.add_argument('--optimizer_path', type=str, default=None,
help='Path to optimizer checkpoint')
return parser.parse_args()
def main():
args = parse_arguments()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path('experiment') / args.experiment
save_dir.mkdir(exist_ok=True, parents=True)
#Save argument values
with open(save_dir / 'args.txt', 'w') as args_file:
for key, value in vars(args).items():
args_file.write(f'{key}: {value}\n')
content_transform = get_transform(args.content_size, args.crop, args.final_size)
style_transform = get_transform(args.style_size, args.crop, args.final_size)
content_dataset = ImageFolderDataset(args.content_dir, content_transform)
style_dateset = ImageFolderDataset(args.style_dir, style_transform)
content_dataloader = DataLoader(content_dataset,
batch_size=args.batch_size,
shuffle = True,
pin_memory=True,
drop_last=True)
style_dataloader = DataLoader(style_dateset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
drop_last=True)
print('Number of batches in content dataset: ', len(content_dataloader))
print('Number of batches in style dataset: ', len(style_dataloader))
encoder = VGGEncoder(args.vgg).to(device)
decoder = Decoder().to(device)
optimizer = optim.Adam(decoder.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda = lambda epoch: 1.0 / (1.0 + args.lr_decay * epoch)
)
if args.resume:
decoder.load_state_dict(torch.load(args.decoder_path))
optimizer.load_state_dict(torch.load(args.optimizer_path))
print('Training...')
mse_loss = torch.nn.MSELoss()
encoder.eval()
running_loss = None
running_closs = None
running_sloss = None
for epoch in range(args.epochs):
progress_bar = tqdm(zip(content_dataloader, style_dataloader),
total=min(len(content_dataloader), len(style_dataloader)))
running_loss = 0
running_closs = 0
running_sloss = 0
for content_batch, style_batch in progress_bar:
content_batch = content_batch.to(device)
style_batch = style_batch.to(device)
c_feats = encoder(content_batch)
s_feats = encoder(style_batch)
t = adaptive_instance_normalization(c_feats[-1], s_feats[-1])
g = decoder(t)
g_feats = encoder(g)
loss_c = mse_loss(g_feats[-1], t) * args.content_weight
loss_s = 0
for g_f, s_f in zip(g_feats, s_feats):
g_mean, g_std = calc_mean_std(g_f)
s_mean, s_std = calc_mean_std(s_f)
loss_s += mse_loss(g_mean, s_mean) + mse_loss(g_std, s_std)
loss_s = loss_s * args.style_weight
loss = loss_c + loss_s
optimizer.zero_grad()
loss.backward()
optimizer.step()
progress_bar.set_description(f'Loss:{loss.item():4f}, Content Loss: {loss_c.item():4f}, Style Loss: {loss_s.item():4f}')
running_loss += loss.item()
running_closs += loss_c.item()
running_sloss += loss_s.item()
scheduler.step()
running_loss /= len(content_dataloader)
running_closs /= len(content_dataloader)
running_sloss /= len(content_dataloader)
if (epoch+1) % args.log_interval == 0:
tqdm.write(f'Iter {epoch+1}: Loss:{running_loss:4f}, Content Loss: {running_closs:4f}, Style Loss: {running_sloss:4f}')
if (epoch+1) % args.save_interval == 0:
torch.save(decoder.state_dict(), save_dir / f'decoder_{epoch+1}.pth')
torch.save(optimizer.state_dict(), save_dir / f'optimizer_{epoch+1}.pth')
with torch.no_grad():
output = torch.cat([content_batch, style_batch, g], dim=0)
save_image(output, save_dir / f'output_{epoch+1}.png', nrow=args.batch_size)
if __name__ == '__main__':
main() |