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submit_dgf_sidd.py
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submit_dgf_sidd.py
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import torch
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 option import args
from model.mwcnn_dgf import MWCNN_DGF
from collections import OrderedDict
from data.data_provider import pixel_unshuffle
import math
# from torchsummary import summary
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):
model = MWCNN_DGF(args)
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)['BenchmarkNoisyBlocksSrgb']
mat_re = np.zeros_like(all_noisy_imgs)
i_imgs,i_blocks, _,_,_ = all_noisy_imgs.shape
for i_img in range(i_imgs):
for i_block in range(i_blocks):
noise = transforms.ToTensor()(Image.fromarray(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()
mat_re[i_img][i_block] = np.array(trans(pred[0]))
return mat_re
if __name__ == "__main__":
mat_re = test(args)
mat = scipy.io.loadmat(args.noise_dir)
del mat['BenchmarkNoisyBlocksSrgb']
mat['DenoisedNoisyBlocksSrgb'] = mat_re
# print(mat)
scipy.io.savemat("SubmitSrgb.mat",mat)