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test_dgf_raw_mat.py
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test_dgf_raw_mat.py
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import torch
from utils.metric import calculate_psnr,calculate_ssim
import os
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms
from utils.training_util import load_checkpoint
from PIL import Image
import time
import scipy.io
from model.mwcnn_dgf import MWCNN_DGF
from model.mwcnn_noise_estimate import MWCNN_noise
from collections import OrderedDict
from utils.raw_util import pack_raw
import argparse
# from torchsummary import summary
import math
from data.data_provider import pixel_unshuffle
def load_data(image_noise, burst_length):
image_noise_hr = image_noise
upscale_factor = int(math.sqrt(burst_length))
image_noise = pixel_unshuffle(image_noise, upscale_factor)
while len(image_noise) < burst_length:
image_noise = torch.cat((image_noise, image_noise[-2:-1]), dim=0)
if len(image_noise) > burst_length:
image_noise = image_noise[0:8]
image_noise_burst_crop = image_noise.unsqueeze(0)
return image_noise_burst_crop, image_noise_hr.unsqueeze(0)
def test(args):
if args.model_type == "DGF":
model = MWCNN_DGF(n_colors= args.n_colors)
elif args.model_type == "noise":
model = MWCNN_noise(n_colors=args.n_colors)
else:
print(" Model type not valid")
exit()
checkpoint_dir = args.checkpoint
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# try:
checkpoint = load_checkpoint(checkpoint_dir, device == 'cuda', 'latest')
start_epoch = checkpoint['epoch']
global_step = checkpoint['global_iter']
state_dict = checkpoint['state_dict']
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# name = "model." + k # remove `module.`
# new_state_dict[name] = v
model.load_state_dict(state_dict)
print('=> loaded checkpoint (epoch {}, global_step {})'.format(start_epoch, global_step))
# except:
# print('=> no checkpoint file to be loaded.') # model.load_state_dict(state_dict)
# exit(1)
model.eval()
model = model.to(device)
trans = transforms.ToPILImage()
torch.manual_seed(0)
all_noisy_imgs = scipy.io.loadmat(args.noise_dir)['ValidationNoisyBlocksRaw']
all_clean_imgs = scipy.io.loadmat(args.gt_dir)['ValidationGtBlocksRaw']
# noisy_path = sorted(glob.glob(args.noise_dir+ "/*.png"))
# clean_path = [ i.replace("noisy","clean") for i in noisy_path]
i_imgs,i_blocks, _,_ = all_noisy_imgs.shape
psnrs = []
ssims = []
# print(noisy_path)
for i_img in range(i_imgs):
for i_block in range(i_blocks):
noise = transforms.ToTensor()(pack_raw(all_noisy_imgs[i_img][i_block]))
image_noise, image_noise_hr = load_data(noise, args.burst_length)
image_noise_hr = image_noise_hr.to(device)
burst_noise = image_noise.to(device)
begin = time.time()
_, pred = model(burst_noise,image_noise_hr)
pred = pred.detach().cpu()
gt = transforms.ToTensor()((pack_raw(all_clean_imgs[i_img][i_block])))
gt = gt.unsqueeze(0)
psnr_t = calculate_psnr(pred, gt)
ssim_t = calculate_ssim(pred, gt)
psnrs.append(psnr_t)
ssims.append(ssim_t)
print(i_img, " UP : PSNR : ", str(psnr_t), " : SSIM : ", str(ssim_t))
if args.save_img != '':
if not os.path.exists(args.save_img):
os.makedirs(args.save_img)
plt.figure(figsize=(15, 15))
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise KPN DGF " + args.model_type, fontsize=25)
image_name = str(i_img)
plt.axis("off")
plt.suptitle(image_name + " UP : PSNR : " + str(psnr_t) + " : SSIM : " + str(ssim_t), fontsize=25)
plt.savefig(os.path.join(args.save_img, image_name + "_" + args.checkpoint + '.png'), pad_inches=0)
print(" AVG : PSNR : "+ str(np.mean(psnrs))+" : SSIM : "+ str(np.mean(ssims)))
if __name__ == "__main__":
# argparse
parser = argparse.ArgumentParser(description='parameters for training')
parser.add_argument('--noise_dir','-n', default='data/ValidationNoisyBlocksSrgb.mat', help='path to noise image file')
parser.add_argument('--gt_dir','-g', default='data/ValidationGtBlocksSrgb.mat', help='path to noise image file')
# parser.add_argument('--noise_dir','-n', default='/home/dell/Downloads/noise/0001_NOISY_SRGB', help='path to noise image file')
parser.add_argument('--cuda', '-c', action='store_true', help='whether to train on the GPU')
parser.add_argument('--burst_length', '-b', default=4, type=int, help='batch size')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='checkpoint',
help='the checkpoint to eval')
parser.add_argument('--image_size', '-sz', default=64, type=int, help='size of image')
parser.add_argument('--model_type','-m' ,default="KPN", help='type of model : KPN, MIR')
parser.add_argument('--n_colors', '-nc', default=3,type=int, help='number of color dim')
parser.add_argument('--out_channels', '-oc', default=3,type=int, help='number of out_channels')
parser.add_argument('--save_img', "-s" ,default="", type=str, help='save image in eval_img folder ')
args = parser.parse_args()
#
# args.noise_dir = '/home/dell/Downloads/FullTest/noisy'
test(args)