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test.py
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import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from ptflops import get_model_complexity_info
from pytorch_msssim import ssim
from torch.utils.data import DataLoader
from collections import OrderedDict
from utils import AverageMeter, write_img, chw_to_hwc
from datasets.loader import PairLoader
from models import *
from utils import AverageMeter, write_img, chw_to_hwc
from datasets.loader import PairLoader
from models import *
# from ptflops import get_model_complexity_info
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='MixDehazeNet-l', type=str, help='model name')
parser.add_argument('--num_workers', default=16, type=int, help='number of workers')
parser.add_argument('--data_dir', default='./data/', type=str, help='path to dataset')
parser.add_argument('--save_dir', default='./saved_models/', type=str, help='path to models saving')
parser.add_argument('--result_dir', default='./results/', type=str, help='path to results saving')
parser.add_argument('--dataset', default='Haze-4K/', type=str, help='dataset name')
parser.add_argument('--exp', default='haze4k', type=str, help='experiment setting')
args = parser.parse_args()
def single(save_dir):
state_dict = torch.load(save_dir)['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
return new_state_dict
def test(test_loader, network, result_dir):
PSNR = AverageMeter()
SSIM = AverageMeter()
torch.cuda.empty_cache()
network.eval()
os.makedirs(os.path.join(result_dir, 'imgs'), exist_ok=True)
f_result = open(os.path.join(result_dir, 'results.csv'), 'w')
for idx, batch in enumerate(test_loader):
input = batch['source'].cuda()
target = batch['target'].cuda()
filename = batch['filename'][0]
with torch.no_grad():
output = network(input).clamp_(-1, 1)
# [-1, 1] to [0, 1]
output = output * 0.5 + 0.5
target = target * 0.5 + 0.5
psnr_val = 10 * torch.log10(1 / F.mse_loss(output, target)).item()
_, _, H, W = output.size()
down_ratio = max(1, round(min(H, W) / 256)) # Zhou Wang
ssim_val = ssim(F.adaptive_avg_pool2d(output, (int(H / down_ratio), int(W / down_ratio))),
F.adaptive_avg_pool2d(target, (int(H / down_ratio), int(W / down_ratio))),
data_range=1, size_average=False).item()
PSNR.update(psnr_val)
SSIM.update(ssim_val)
print('Test: [{0}]\t'
'PSNR: {psnr.val:.02f} ({psnr.avg:.02f})\t'
'SSIM: {ssim.val:.03f} ({ssim.avg:.03f})'
.format(idx, psnr=PSNR, ssim=SSIM))
f_result.write('%s,%.02f,%.03f\n'%(filename, psnr_val, ssim_val))
out_img = chw_to_hwc(output.detach().cpu().squeeze(0).numpy())
write_img(os.path.join(result_dir, 'imgs', filename), out_img)
f_result.close()
os.rename(os.path.join(result_dir, 'results.csv'),
os.path.join(result_dir, '%.02f | %.04f.csv'%(PSNR.avg, SSIM.avg)))
if __name__ == '__main__':
network = eval(args.model.replace('-', '_'))()
network.cuda()
saved_model_dir = os.path.join(args.save_dir, args.exp, args.model+'.pth')
if os.path.exists(saved_model_dir):
print('==> Start testing, current model name: ' + args.model)
network.load_state_dict(single(saved_model_dir))
else:
print('==> No existing trained model!')
exit(0)
# macs, params = get_model_complexity_info(network, (3, 224, 224), as_strings=True,
# print_per_layer_stat=True, verbose=True)
# print('{:<30} {:<8}'.format('Computational complexity: ', macs))
# print('{:<30} {:<8}'.format('Number of parameters: ', params))
dataset_dir = os.path.join(args.data_dir, args.dataset)
test_dataset = PairLoader(dataset_dir, 'test', 'test')
test_loader = DataLoader(test_dataset,
batch_size=1,
num_workers=args.num_workers,
pin_memory=True)
result_dir = os.path.join(args.result_dir, args.dataset, args.model)
test(test_loader, network, result_dir)
# import os
# import argparse
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# from pytorch_msssim import ssim
# from torch.utils.data import DataLoader
# from collections import OrderedDict
#
# from utils import AverageMeter, write_img, chw_to_hwc
# from datasets.loader import PairLoader
# from models import *
# # from ptflops import get_model_complexity_info
#
# parser = argparse.ArgumentParser()
# parser.add_argument('--model', default='MixDehazeNet-s', type=str, help='model name')
# parser.add_argument('--num_workers', default=1, type=int, help='number of workers')
# parser.add_argument('--data_dir', default='./data/', type=str, help='path to dataset')
# parser.add_argument('--save_dir', default='./saved_models/reside6k/', type=str, help='path to models saving')
# parser.add_argument('--result_dir', default='./result/', type=str, help='path to results saving')
# parser.add_argument('--dataset', default='RESIDE-6K/reside6k/', type=str, help='dataset name')
# parser.add_argument('--exp', default='', type=str, help='experiment setting')
# parser.add_argument('--gpu', default='1', type=str, help='GPUs used for training')
# args = parser.parse_args()
#
# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
#
# def single(save_dir):
# state_dict = torch.load(save_dir)['state_dict']
# new_state_dict = OrderedDict()
#
# for k, v in state_dict.items():
# name = k[7:]
# new_state_dict[name] = v
#
# return new_state_dict
#
#
# def test(test_loader, network, result_dir):
# PSNR = AverageMeter()
# SSIM = AverageMeter()
#
# torch.cuda.empty_cache()
#
# network.eval()
#
# os.makedirs(os.path.join(result_dir, 'imgs'), exist_ok=True)
# f_result = open(os.path.join(result_dir, 'results.csv'), 'w')
#
# for idx, batch in enumerate(test_loader):
# input = batch['source'].cuda()
# target = batch['target'].cuda()
#
# filename = batch['filename'][0]
#
# with torch.no_grad():
# output = network(input).clamp_(-1, 1)
#
# # [-1, 1] to [0, 1]
# output = output * 0.5 + 0.5
# target = target * 0.5 + 0.5
#
# psnr_val = 10 * torch.log10(1 / F.mse_loss(output, target)).item()
#
# _, _, H, W = output.size()
# down_ratio = max(1, round(min(H, W) / 256)) # Zhou Wang
# ssim_val = ssim(F.adaptive_avg_pool2d(output, (int(H / down_ratio), int(W / down_ratio))),
# F.adaptive_avg_pool2d(target, (int(H / down_ratio), int(W / down_ratio))),
# data_range=1, size_average=False).item()
#
# PSNR.update(psnr_val)
# SSIM.update(ssim_val)
#
# print('Test: [{0}]\t'
# 'PSNR: {psnr.val:.02f} ({psnr.avg:.02f})\t'
# 'SSIM: {ssim.val:.03f} ({ssim.avg:.03f})'
# .format(idx, psnr=PSNR, ssim=SSIM))
#
# f_result.write('%s,%.02f,%.03f\n'%(filename, psnr_val, ssim_val))
#
# out_img = chw_to_hwc(output.detach().cpu().squeeze(0).numpy())
# write_img(os.path.join(result_dir, 'imgs', filename), out_img)
#
# f_result.close()
#
# os.rename(os.path.join(result_dir, 'results.csv'),
# os.path.join(result_dir, '%.02f | %.04f.csv'%(PSNR.avg, SSIM.avg)))
#
#
# if __name__ == '__main__':
# network = eval(args.model.replace('-', '_'))()
# # network = eval(network)()
# network.cuda()
# saved_model_dir = os.path.join(args.save_dir, args.exp, args.model+'.pth')
# # saved_model_dir = os.path.join(args.save_dir, args.exp, args.model + 'mutilAtten.pth')
#
# # if os.path.exists(saved_model_dir):
# # print('==> Start testing, current model name: ' + args.model)
# # network.load_state_dict(single(saved_model_dir))
# # else:
# # print('==> No existing trained model!')
# # exit(0)
# #
# # macs, params = get_model_complexity_info(network, (3, 224, 224), as_strings=True,
# # print_per_layer_stat=True, verbose=True)
# # print('{:<30} {:<8}'.format('Computational complexity: ', macs))
# # print('{:<30} {:<8}'.format('Number of parameters: ', params))
#
# dataset_dir = os.path.join(args.data_dir, args.dataset)
# test_dataset = PairLoader(dataset_dir, 'test', 'test')
# test_loader = DataLoader(test_dataset,
# batch_size=1,
# num_workers=args.num_workers,
# pin_memory=True)
#
# result_dir = os.path.join(args.result_dir, args.dataset, args.model)
# test(test_loader, network, result_dir)