Spaces:
Running
Running
File size: 10,806 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
import os
import argparse
import albumentations
from albumentations import Resize
import torch
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from model.build_model import build_model
from datasets.build_dataset import dataset_generator
from utils import misc, metrics
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--workers', type=int, default=1,
metavar='N', help='Dataloader threads.')
parser.add_argument('--batch_size', type=int, default=1,
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('--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('--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=r'.\pretrained_models\Resolution_RAW_iHarmony4.pth',
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, "test1"))
return opt
def inference(val_loader, model, logger, opt):
current_process = 10
model.eval()
metric_log = {
'HAdobe5k': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'HCOCO': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'Hday2night': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'HFlickr': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'All': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
}
lut_metric_log = {
'HAdobe5k': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'HCOCO': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'Hday2night': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'HFlickr': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
'All': {'Samples': 0, 'MSE': 0, 'fMSE': 0, 'PSNR': 0, 'SSIM': 0},
}
for step, batch in enumerate(val_loader):
composite_image = batch['composite_image'].to(opt.device)
real_image = batch['real_image'].to(opt.device)
mask = batch['mask'].to(opt.device)
category = batch['category']
fg_INR_coordinates = batch['fg_INR_coordinates'].to(opt.device)
with torch.no_grad():
fg_content_bg_appearance_construct, _, lut_transform_image = model(
composite_image,
mask,
fg_INR_coordinates,
)
if opt.INRDecode:
pred_fg_image = fg_content_bg_appearance_construct[-1]
else:
pred_fg_image = misc.lin2img(fg_content_bg_appearance_construct,
val_loader.dataset.INR_dataset.size) if fg_content_bg_appearance_construct is not None else None
if not opt.INRDecode:
pred_harmonized_image = None
else:
pred_harmonized_image = pred_fg_image * (mask > 100 / 255.) + real_image * (~(mask > 100 / 255.))
lut_transform_image = lut_transform_image * (mask > 100 / 255.) + real_image * (~(mask > 100 / 255.))
misc.visualize(real_image, composite_image, mask, pred_fg_image,
pred_harmonized_image, lut_transform_image, opt, -1, show=False,
wandb=False, isAll=True, step=step)
if opt.INRDecode:
mse, fmse, psnr, ssim = metrics.calc_metrics(misc.normalize(pred_harmonized_image, opt, 'inv'),
misc.normalize(real_image, opt, 'inv'), mask)
lut_mse, lut_fmse, lut_psnr, lut_ssim = metrics.calc_metrics(misc.normalize(lut_transform_image, opt, 'inv'),
misc.normalize(real_image, opt, 'inv'), mask)
for idx in range(len(category)):
if opt.INRDecode:
metric_log[category[idx]]['Samples'] += 1
metric_log[category[idx]]['MSE'] += mse[idx]
metric_log[category[idx]]['fMSE'] += fmse[idx]
metric_log[category[idx]]['PSNR'] += psnr[idx]
metric_log[category[idx]]['SSIM'] += ssim[idx]
metric_log['All']['Samples'] += 1
metric_log['All']['MSE'] += mse[idx]
metric_log['All']['fMSE'] += fmse[idx]
metric_log['All']['PSNR'] += psnr[idx]
metric_log['All']['SSIM'] += ssim[idx]
lut_metric_log[category[idx]]['Samples'] += 1
lut_metric_log[category[idx]]['MSE'] += lut_mse[idx]
lut_metric_log[category[idx]]['fMSE'] += lut_fmse[idx]
lut_metric_log[category[idx]]['PSNR'] += lut_psnr[idx]
lut_metric_log[category[idx]]['SSIM'] += lut_ssim[idx]
lut_metric_log['All']['Samples'] += 1
lut_metric_log['All']['MSE'] += lut_mse[idx]
lut_metric_log['All']['fMSE'] += lut_fmse[idx]
lut_metric_log['All']['PSNR'] += lut_psnr[idx]
lut_metric_log['All']['SSIM'] += lut_ssim[idx]
if (step + 1) / len(val_loader) * 100 >= current_process:
logger.info(f'Processing: {current_process}')
current_process += 10
logger.info('=========================')
for key in metric_log.keys():
if opt.INRDecode:
msg = f"{key}-'MSE': {metric_log[key]['MSE'] / metric_log[key]['Samples']:.2f}\n" \
f"{key}-'fMSE': {metric_log[key]['fMSE'] / metric_log[key]['Samples']:.2f}\n" \
f"{key}-'PSNR': {metric_log[key]['PSNR'] / metric_log[key]['Samples']:.2f}\n" \
f"{key}-'SSIM': {metric_log[key]['SSIM'] / metric_log[key]['Samples']:.4f}\n" \
f"{key}-'LUT_MSE': {lut_metric_log[key]['MSE'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_fMSE': {lut_metric_log[key]['fMSE'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_PSNR': {lut_metric_log[key]['PSNR'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_SSIM': {lut_metric_log[key]['SSIM'] / lut_metric_log[key]['Samples']:.4f}\n"
else:
msg = f"{key}-'LUT_MSE': {lut_metric_log[key]['MSE'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_fMSE': {lut_metric_log[key]['fMSE'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_PSNR': {lut_metric_log[key]['PSNR'] / lut_metric_log[key]['Samples']:.2f}\n" \
f"{key}-'LUT_SSIM': {lut_metric_log[key]['SSIM'] / lut_metric_log[key]['Samples']:.4f}\n"
logger.info(msg)
logger.info('=========================')
def main_process(opt):
logger = misc.create_logger(os.path.join(opt.save_path, "log.txt"))
cudnn.benchmark = True
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)])
valset_alb_transform = albumentations.Compose([Resize(opt.input_size, opt.input_size)],
additional_targets={'real_image': 'image', 'object_mask': 'image'})
valset = dataset_generator(valset_path, valset_alb_transform, torch_transform, opt, mode='Val')
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)
logger.info(f"Load pretrained weight from {opt.pretrained}")
load_dict = torch.load(opt.pretrained)['model']
for k in load_dict.keys():
if k not in model.state_dict().keys():
print(f"Skip {k}")
model.load_state_dict(load_dict, strict=False)
inference(val_loader, model, logger, opt)
if __name__ == '__main__':
opt = parse_args()
os.makedirs(opt.save_path, exist_ok=True)
main_process(opt) |