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test.py
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import os
import math
import argparse
import random
import logging
import torch
import torch.distributed as dist
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models import create_model
import numpy as np
import cv2
from PIL import Image
def compute_ssim(img1, img2):
ssims = []
for i in range(3):
ssims.append(_ssim(img1[i], img2[i]))
return np.array(ssims).mean()
def _ssim(img1, img2):
"""Calculate SSIM (structural similarity) for one channel images.
It is called by func:`calculate_ssim`.
Args:
img1 (ndarray): Images with range [0, 255] with order 'HWC'.
img2 (ndarray): Images with range [0, 255] with order 'HWC'.
Returns:
float: ssim result.
"""
C1 = (0.01 * 255)**2
C2 = (0.03 * 255)**2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) *
(2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def main():
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YMAL file.')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
opt['dist'] = True
#### loading resume state if exists
if opt['path'].get('resume_state', None):
device_id = torch.cuda.current_device()
resume_state = torch.load(opt['path']['resume_state'],
map_location=lambda storage, loc: storage.cuda(device_id))
option.check_resume(opt, resume_state['iter']) # check resume options
else:
resume_state = None
#### mkdir and loggers
if resume_state is None:
util.mkdir_and_rename(
opt['path']['experiments_root']) # rename experiment folder if exists
util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
# config loggers. Before it, the log will not work
util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
util.setup_logger('val', opt['path']['log'], 'val_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
# convert to NoneDict, which returns None for missing keys
opt = option.dict_to_nonedict(opt)
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
#### create train and val dataloader
for phase, dataset_opt in opt['datasets'].items():
if phase == 'test':
val_set_hazy, val_set_rain, val_set_noise15, val_set_noise25, val_set_noise50 = create_dataset(dataset_opt)
val_loader_hazy = create_dataloader(val_set_hazy, dataset_opt, opt, None)
val_loader_rain = create_dataloader(val_set_rain, dataset_opt, opt, None)
val_loader_noise15 = create_dataloader(val_set_noise15, dataset_opt, opt, None)
val_loader_noise25 = create_dataloader(val_set_noise25, dataset_opt, opt, None)
val_loader_noise50 = create_dataloader(val_set_noise50, dataset_opt, opt, None)
logger.info('Number of val images in [{:s}]: {:d}'.format(
dataset_opt['name'], len(val_loader_noise15)))
else:
raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
# assert train_loader is not None
#### create model
model = create_model(opt)
#### resume training
if resume_state:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
resume_state['epoch'], resume_state['iter']))
start_epoch = resume_state['epoch']
current_step = resume_state['iter']
model.resume_training(resume_state) # handle optimizers and schedulers
else:
current_step = 0
start_epoch = 0
#### training
# validation
avg_psnr_hazy = 0.0
avg_psnr_rain = 0.0
avg_psnr_noise15 = 0.0
avg_psnr_noise25 = 0.0
avg_psnr_noise50 = 0.0
avg_ssim_hazy = 0.0
avg_ssim_rain = 0.0
avg_ssim_noise15 = 0.0
avg_ssim_noise25 = 0.0
avg_ssim_noise50 = 0.0
idx = 0
for val_data in val_loader_hazy:
idx += 1
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
out_img = visuals['out_img'].numpy()
gt_img = visuals['gt_img'].numpy()
c, h, w = gt_img.shape
out_img = out_img[:c, :h, :w]
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
gt_img = np.clip(gt_img, 0, 1)
out_img = np.clip(out_img, 0, 1)
curr_psnr = compute_psnr(out_img, gt_img, 1)
curr_ssim = compute_ssim(out_img * 255, gt_img * 255)
avg_psnr_hazy += curr_psnr
avg_ssim_hazy += curr_ssim
if idx % 100 == 0:
print('idx_HAZY', idx, curr_psnr,curr_ssim)
avg_psnr_hazy = avg_psnr_hazy / idx
avg_ssim_hazy = avg_ssim_hazy / idx
# ————————————————————————————————————————————————————————————————————
idx = 0
for val_data in val_loader_rain:
idx += 1
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
out_img = visuals['out_img'].numpy()
gt_img = visuals['gt_img'].numpy()
c, h, w = gt_img.shape
out_img = out_img[:c, :h, :w]
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
gt_img = np.clip(gt_img, 0, 1)
out_img = np.clip(out_img, 0, 1)
curr_psnr = compute_psnr(out_img, gt_img, 1)
curr_ssim = compute_ssim(out_img * 255, gt_img * 255)
avg_psnr_rain += curr_psnr
avg_ssim_rain += curr_ssim
if idx % 50 == 0:
print('idx_Rain', idx, curr_psnr,curr_ssim)
avg_psnr_rain = avg_psnr_rain / idx
avg_ssim_rain = avg_ssim_rain / idx
# ————————————————————————————————————————————————————————————————————
idx = 0
for val_data in val_loader_noise15:
idx += 1
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
out_img = visuals['out_img'].numpy()
gt_img = visuals['gt_img'].numpy()
c, h, w = gt_img.shape
out_img = out_img[:c, :h, :w]
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
gt_img = np.clip(gt_img, 0, 1)
out_img = np.clip(out_img, 0, 1)
curr_psnr = compute_psnr(out_img, gt_img, 1)
curr_ssim = compute_ssim(out_img * 255, gt_img * 255)
avg_psnr_noise15 += curr_psnr
avg_ssim_noise15 += curr_ssim
if idx % 10 == 0:
print('idx_Noise15', idx, curr_psnr, curr_ssim)
avg_psnr_noise15 = avg_psnr_noise15 / idx
avg_ssim_noise15 = avg_ssim_noise15 / idx
# ————————————————————————————————————————————————————————————————————
# ————————————————————————————————————————————————————————————————————
idx = 0
for val_data in val_loader_noise25:
idx += 1
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
out_img = visuals['out_img'].numpy()
gt_img = visuals['gt_img'].numpy()
c, h, w = gt_img.shape
out_img = out_img[:c, :h, :w]
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
gt_img = np.clip(gt_img, 0, 1)
out_img = np.clip(out_img, 0, 1)
curr_psnr = compute_psnr(out_img, gt_img, 1)
curr_ssim = compute_ssim(out_img * 255, gt_img * 255)
avg_psnr_noise25 += curr_psnr
avg_ssim_noise25 += curr_ssim
if idx % 10 == 0:
print('idx_Noise25', idx, curr_psnr, curr_ssim)
avg_psnr_noise25 = avg_psnr_noise25 / idx
avg_ssim_noise25 = avg_ssim_noise25 / idx
# ————————————————————————————————————————————————————————————————————
# ————————————————————————————————————————————————————————————————————
idx = 0
for val_data in val_loader_noise50:
idx += 1
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
out_img = visuals['out_img'].numpy()
gt_img = visuals['gt_img'].numpy()
c, h, w = gt_img.shape
out_img = out_img[:c, :h, :w]
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
gt_img = np.clip(gt_img, 0, 1)
out_img = np.clip(out_img, 0, 1)
curr_psnr = compute_psnr(out_img, gt_img, 1)
curr_ssim = compute_ssim(out_img * 255, gt_img * 255)
avg_psnr_noise50 += curr_psnr
avg_ssim_noise50 += curr_ssim
if idx % 10 == 0:
print('idx_Noise50', idx, curr_psnr)
avg_psnr_noise50 = avg_psnr_noise50 / idx
avg_ssim_noise50 = avg_ssim_noise50 / idx
# ————————————————————————————————————————————————————————————————————
# log
logger_val = logging.getLogger('val') # validation logger
logger_val.info(
'HAZY_PSNR: {:.4f} Rain_PSNR: {:.4f} Noise15_PSNR: {:.4f} Noise25_PSNR: {:.4f} Noise50_PSNR: {:.4f}.'.format(
avg_psnr_hazy, avg_psnr_rain, avg_psnr_noise15, avg_psnr_noise25,
avg_psnr_noise50))
logger_val.info(
' HAZY_SSIM: {:.4f} Rain_SSIM: {:.4f} Noise15_SSIM: {:.4f} Noise25_SSIM: {:.4f} Noise50_SSIM: {:.4f}.'.format(
avg_ssim_hazy, avg_ssim_rain, avg_ssim_noise15, avg_ssim_noise25,
avg_ssim_noise50))
# log))
# ————————————————————————————————————————————————————————————————————
if __name__ == '__main__':
main()