Spaces:
Running
Running
File size: 6,496 Bytes
6710c89 |
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 |
import os
import argparse
import albumentations
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler
import processing
from utils import build_loss, misc
from model.build_model import build_model
from datasets.build_dataset import dataset_generator
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--workers', type=int, default=8,
metavar='N', help='Dataloader threads.')
parser.add_argument('--batch_size', type=int, default=16,
help='You can override model batch size by specify positive number.')
parser.add_argument('--device', type=str, default='cuda',
help="Whether use cuda, 'cuda' or 'cpu'.")
parser.add_argument('--epochs', type=int, default=60,
help='Epochs number.')
parser.add_argument('--lr', type=int, default=1e-4,
help='Learning rate.')
parser.add_argument('--save_path', type=str, default="./logs",
help='Where to save logs and checkpoints.')
parser.add_argument('--dataset_path', type=str, default=r".\iHarmony4",
help='Dataset path.')
parser.add_argument('--print_freq', type=int, default=100,
help='Number of iterations then print.')
parser.add_argument('--base_size', type=int, default=256,
help='Base size. Resolution of the image input into the Encoder')
parser.add_argument('--input_size', type=int, default=256,
help='Input size. Resolution of the image that want to be generated by the Decoder')
parser.add_argument('--INR_input_size', type=int, default=256,
help='INR input size. Resolution of the image that want to be generated by the Decoder. '
'Should be the same as `input_size`')
parser.add_argument('--INR_MLP_dim', type=int, default=32,
help='Number of channels for INR linear layer.')
parser.add_argument('--LUT_dim', type=int, default=7,
help='Dim of the output LUT. Refer to https://ieeexplore.ieee.org/abstract/document/9206076')
parser.add_argument('--activation', type=str, default='leakyrelu_pe',
help='INR activation layer type: leakyrelu_pe, sine')
parser.add_argument('--pretrained', type=str,
default=None,
help='Pretrained weight path')
parser.add_argument('--param_factorize_dim', type=int,
default=10,
help='The intermediate dimensions of the factorization of the predicted MLP parameters. '
'Refer to https://arxiv.org/abs/2011.12026')
parser.add_argument('--embedding_type', type=str,
default="CIPS_embed",
help='Which embedding_type to use.')
parser.add_argument('--optim', type=str,
default='adamw',
help='Which optimizer to use.')
parser.add_argument('--INRDecode', action="store_false",
help='Whether INR decoder. Set it to False if you want to test the baseline '
'(https://github.com/SamsungLabs/image_harmonization)')
parser.add_argument('--isMoreINRInput', action="store_false",
help='Whether to cat RGB and mask. See Section 3.4 in the paper.')
parser.add_argument('--hr_train', action="store_true",
help='Whether use hr_train. See section 3.4 in the paper.')
parser.add_argument('--isFullRes', action="store_true",
help='Whether for original resolution. See section 3.4 in the paper.')
opt = parser.parse_args()
opt.save_path = misc.increment_path(os.path.join(opt.save_path, "exp1"))
try:
import wandb
opt.wandb = True
wandb.init(config=opt, project="INR_Harmonization", name=os.path.basename(opt.save_path))
except:
opt.wandb = False
return opt
def main_process(opt):
logger = misc.create_logger(os.path.join(opt.save_path, "log.txt"))
cudnn.benchmark = True
trainset_path = os.path.join(opt.dataset_path, "IHD_train.txt")
valset_path = os.path.join(opt.dataset_path, "IHD_test.txt")
opt.transform_mean = [.5, .5, .5]
opt.transform_var = [.5, .5, .5]
torch_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(opt.transform_mean, opt.transform_var)])
trainset_alb_transform = albumentations.Compose(
[
RandomResizedCrop(opt.input_size, opt.input_size, scale=(0.5, 1.0)),
HorizontalFlip()],
additional_targets={'real_image': 'image', 'object_mask': 'image'}
)
valset_alb_transform = albumentations.Compose([Resize(opt.input_size, opt.input_size)],
additional_targets={'real_image': 'image', 'object_mask': 'image'})
trainset = dataset_generator(trainset_path, trainset_alb_transform, torch_transform, opt, mode='Train')
valset = dataset_generator(valset_path, valset_alb_transform, torch_transform, opt, mode='Val')
train_loader = DataLoader(trainset, opt.batch_size, shuffle=True, drop_last=True,
pin_memory=True,
num_workers=opt.workers, persistent_workers=True)
val_loader = DataLoader(valset, opt.batch_size, shuffle=False, drop_last=False, pin_memory=True,
num_workers=opt.workers, persistent_workers=True)
model = build_model(opt).to(opt.device)
loss_fn = build_loss.loss_generator()
optimizer_params = {
'lr': opt.lr,
'weight_decay': 1e-2
}
optimizer = misc.get_optimizer(model, opt.optim, optimizer_params)
scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=opt.lr, total_steps=opt.epochs * len(train_loader),
pct_start=0.0)
processing.train(train_loader, val_loader, model, optimizer, scheduler, loss_fn, logger, opt)
if __name__ == '__main__':
opt = parse_args()
os.makedirs(opt.save_path, exist_ok=True)
main_process(opt)
|