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main_test_swinir.py
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import argparse
import cv2
import glob
import numpy as np
from collections import OrderedDict
import os
import torch
import requests
from math import log10
from models.network_swinir import DenseSwinIR as net_Dense
from models.network_swinir import SwinIR as net
from models.network_swinir import SwinIR_D as net_D
from models.network_swinir import DenseSwinIR_withSE as net_Dense_SE
from utils import utils_image as util
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--training_patch_size', type=int, default=120, help='patch size used in training SwinIR. '
'Just used to differentiate two different settings in Table 2 of the paper. '
'Images are NOT tested patch by patch.')
parser.add_argument('--model_path', type=str, default='/home/hallasan/nvme0n1/JK/myProject/sdr2hdr_Dense_se/swinir_sdr2hdr_Dense_se/models/1029838_G.pth')
parser.add_argument('--model', type=str, default='swinIR_Dense_withSE')
parser.add_argument('--folder_lq', type=str, default='/home/hallasan/nvme0n1/HDRTV_dataset/test/test_sdr_1K_gpu', help='input low-quality test image folder')
parser.add_argument('--folder_gt', type=str, default='/home/hallasan/nvme0n1/HDRTV_dataset/test/test_hdr_1K_gpu', help='input ground-truth test image folder')
parser.add_argument('--index', type=int, default=0)
parser.add_argument('--save_img', action='store_true')
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# set up model
if os.path.exists(args.model_path):
print(f'loading model from {args.model_path}')
if args.model_path.endswith(".pth"):
modelpaths = [args.model_path]
args.index = 0
folder, save_dir, border, window_size = args.folder_lq, f"./inference_results/{args.model}/{modelpaths[args.index].split('/')[-1].split('_')[0]}", 0, 8
os.makedirs(save_dir, exist_ok=True)
f = open(f"{save_dir}/{modelpaths[args.index].split('/')[-1].split('_')[0]}.txt", 'w')
print(f"{save_dir}/{modelpaths[args.index].split('/')[-1].split('_')[0]}.txt")
else:
modelpaths = [path for path in sorted(os.listdir(args.model_path)) if path.endswith('_G.pth') and int(path.split('_')[0]) >= 500000]
folder, save_dir, border, window_size = args.folder_lq, f"./inference_results/{args.model}/{modelpaths[args.index].split('_')[0]}", 0, 8
os.makedirs(save_dir, exist_ok=True)
f = open(f"{save_dir}/{modelpaths[args.index].split('_')[0]}.txt", 'w')
print(f"{save_dir}/{modelpaths[args.index].split('/')[-1].split('_')[0]}.txt")
model = define_model(args, os.path.join(args.model_path, modelpaths[args.index]))
model.eval()
model = model.to(device)
criterion = torch.nn.MSELoss().cuda()
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
imgnames = sorted([x for x in os.listdir(args.folder_lq) if x.endswith('.png')])
for i in range(len(imgnames)):
# read image
imgname = imgnames[i]
lq_path = os.path.join(args.folder_lq, imgname)
gt_path = os.path.join(args.folder_gt, imgname)
img_lq, img_gt = get_image_pair(args, lq_path, gt_path) # image to HWC-BGR, float32
img_lq, img_gt_scaled = img_lq / 255., img_gt / 65535.
img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]], (2, 0, 1)) # HCW-BGR to CHW-RGB
img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device) # CHW-RGB to NCHW-RGB
img_gt_t = np.transpose(img_gt_scaled if img_gt_scaled.shape[2] == 1 else img_gt_scaled[:, :, [2, 1, 0]], (2, 0, 1))
img_gt_t = torch.from_numpy(img_gt_t).float().unsqueeze(0).to(device)
# inference
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
output = model(img_lq)
output = output[..., :h_old, :w_old]
mse = criterion(output, img_gt_t)
psnr_t = 10 * log10(1 / mse.item())
test_results['psnr'].append(psnr_t)
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = (output * 65535.0).round().astype(np.uint16) # float32 to uint8
ssim = util.calculate_ssim(output, img_gt, 16)
test_results['ssim'].append(ssim)
print('Testing {:d} {:20s} - PSNR TORCH: {:.2f} dB SSIM {:.4f}'.
format(i, imgname, psnr_t, ssim))
f.write('Testing {:d} {:20s} - PSNR TORCH: {:.2f} dB SSIM {:.4f}\n'.
format(i, imgname, psnr_t, ssim))
# save image
if args.save_img:
cv2.imwrite(f'{save_dir}/{imgname}', output)
# evaluate psnr/ssim/psnr_b
'''
if img_gt is not None:
img_gt = np.squeeze(img_gt)
output = output.astype(np.float32) / 65535.0
psnr = util.calculate_psnr(output, img_gt)
test_results['psnr'].append(psnr)
print('Testing {:d} {:20s} - PSNR: {:.2f} dB PSNR TORCH: {:.2f}'.
format(i, imgname, psnr, psnr_t))
else:
print('Testing {:d} {:20s}'.format(i, imgname))
'''
# summarize psnr/ssim
if img_gt is not None:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
print('\n{} \n-- Average PSNR: {:.2f} dB'.format(save_dir, ave_psnr))
print('\n{} \n-- Average SSIM: {:.4f}'.format(save_dir, ave_ssim))
f.write('\n{} \n-- Average PSNR: {:.2f} dB'.format(save_dir, ave_psnr))
f.write('\n{} \n-- Average SSIM: {:.4f}'.format(save_dir, ave_ssim))
f.close()
def define_model(args, modelpath):
# SwinIR
if args.model == 'swinIR':
model = net(upscale=1, in_chans=3, img_size=120, window_size=8,
img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6],
mlp_ratio=2, upsampler=None, resi_connection='1conv')
elif args.model == 'swinIR_D':
# SwinIR_D
model = net_D(upscale=1, in_chans=3, img_size=120, window_size=8,
img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6],
mlp_ratio=2, upsampler=None, resi_connection='1conv')
elif args.model == 'swinIR_Dense':
# SwinIR_Dense
model = net_Dense(growth_rate=30,
upscale=1,
in_chans=3,
img_size=120,
window_size=8,
img_range=1.,
depths=[6, 6, 6, 6],
embed_dim=60,
num_heads=[6, 6, 6, 6],
mlp_ratio=2,
upsampler=None,
resi_connection='1conv')
elif args.model == 'swinIR_Dense_withSE':
model = net_Dense_SE(growth_rate=30,
upscale=1,
in_chans=3,
img_size=120,
window_size=8,
img_range=1.,
depths=[6, 6, 6, 6],
embed_dim=60,
num_heads=[6, 6, 6, 6],
mlp_ratio=2,
upsampler=None,
resi_connection='1conv')
model.load_state_dict(torch.load(modelpath), strict=True)
return model
def get_image_pair(args, lq_path, gt_path, opt=None):
img_gt = cv2.imread(gt_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
img_lq = cv2.imread(lq_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
if opt == '2K':
img_gt = cv2.imread(gt_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
img_lq = cv2.imread(lq_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
elif opt == '4K':
img_gt = cv2.imread(gt_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
img_lq = cv2.imread(lq_path, cv2.IMREAD_UNCHANGED).astype(np.float32)
return img_lq, img_gt
if __name__ == '__main__':
main()