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predict.py
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executable file
·77 lines (64 loc) · 3.14 KB
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from __future__ import print_function
import os
from batch_utils import UNI
import torch
from torch.utils.data import DataLoader
import cv2
import Attention_GAN
import argparse
def check_path(path):
if not os.path.exists(path):
print('creating:{}'.format(path))
os.makedirs(path)
return path
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', type=str, help="the dataset name")
parser.add_argument('--data_root', type=str, help="The data root of the data")
parser.add_argument('--crop_size', type=int, help='the size of cropped images')
parser.add_argument('--phase', type=str, help='train, val, test')
parser.add_argument('--noise', type=int, help='the noise level, 0, 1, 2, 3, 4, 5')
parser.add_argument('--batch_size', type=int, help='training batch_size')
parser.add_argument('--gpu_id', type=str, help='GPU ID')
parser.add_argument('--checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--results_dir', type=str, help='path to save checkpoints')
parser.add_argument('--n_channel', type=int, default=64)
parser.add_argument('--in_channel_num', type=int, default=3)
parser.add_argument('--out_channel_num', type=int, default=3)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
# define dataset
valid_set = UNI(args.dataset_name, args.data_root, args.crop_size, 'test', args.noise)
valid_data_loader = DataLoader(dataset=valid_set, num_workers=8, batch_size=args.batch_size,
shuffle=False, drop_last=False)
im_save_path = check_path(args.results_dir)
print("===> Building model")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.cuda.empty_cache()
# build generator and discriminator
netG = Attention_GAN.Generator(n_channels=args.n_channel, in_channels=args.in_channel_num, batch_norm=False,
out_channels=args.out_channel_num, padding=1, pooling_mode="maxpool",).to(device)
msg = netG.load_state_dict(torch.load(args.checkpoint_path), strict=True)
print(msg)
netG.eval()
with torch.no_grad():
for i, batch in enumerate(valid_data_loader):
print('Progress: [{}/{}]'.format(i, len(valid_data_loader)))
val_input = (batch['input']).to(device)
val_target = (batch['gt']).to(device)
name = batch['name']
val_fake = netG(val_input)
val_fake = ((val_fake + 1)/2).cpu()
val_target = ((val_target + 1)/2).cpu()
val_input = ((val_input + 1)/2).cpu()
# save images;
for i in range(len(val_target)):
path = os.path.join(im_save_path, name[i])
img = torch.cat([val_input[i], val_fake[i], val_target[i]], dim=-1)
img = img.permute(1, 2, 0).clip_(0, 1.0).numpy()*255
cv2.imwrite(path, img.astype('uint8')[:, :, ::-1])
print('Done')