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test_custom_DGF.py
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test_custom_DGF.py
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import argparse
from utils.training_util import load_checkpoint
from utils.data_provider import *
from model.KPN_DGF import KPN_DGF,Att_KPN_DGF,Att_Weight_KPN_DGF,Att_KPN_Wavelet_DGF
from collections import OrderedDict
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import glob
from PIL import Image
import time
import math
from utils.training_util import calculate_psnr, calculate_ssim
torch.manual_seed(0)
def load_data(image_file,burst_length):
image_noise = transforms.ToTensor()(Image.open(image_file).convert('RGB'))
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_hr2 = pixel_shuffle(image_noise.unsqueeze(0), upscale_factor).squeeze(0)
print(image_noise_hr - image_noise_hr2)
image_noise_burst_crop = image_noise.unsqueeze(0)
return image_noise_burst_crop,image_noise_hr.unsqueeze(0)
def test_multi(args):
color = True
burst_length = args.burst_length
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model_type == "attKPN":
model = Att_KPN_DGF(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attKPN_Wave":
model = Att_KPN_Wavelet_DGF(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attWKPN":
model = Att_Weight_KPN_DGF(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "KPN":
model = KPN_DGF(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
checkpoint_dir = "checkpoints/" + args.checkpoint
if not os.path.exists(checkpoint_dir) or len(os.listdir(checkpoint_dir)) == 0:
print('There is no any checkpoint file in path:{}'.format(checkpoint_dir))
# load trained model
ckpt = load_checkpoint(checkpoint_dir,cuda=device=='cuda',best_or_latest=args.load_type)
state_dict = ckpt['state_dict']
# if not args.cuda:
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
# else:
# model.load_state_dict(ckpt['state_dict'])
model.to(device)
print('The model has been loaded from epoch {}, n_iter {}.'.format(ckpt['epoch'], ckpt['global_iter']))
# switch the eval mode
model.eval()
# model= save_dict['state_dict']
trans = transforms.ToPILImage()
torch.manual_seed(0)
noisy_path = sorted(glob.glob(args.noise_dir+ "/*.png"))
clean_path = [ i.replace("noisy","clean") for i in noisy_path]
upscale_factor = int(math.sqrt(burst_length))
for i in range(len(noisy_path)):
image_noise,image_noise_hr = load_data(noisy_path[i],burst_length)
image_noise_hr = image_noise_hr.to(device)
# begin = time.time()
image_noise_batch = image_noise.to(device)
# print(image_noise_batch.size())
burst_size = image_noise_batch.size()[1]
burst_noise = image_noise_batch.to(device)
# print(burst_noise.size())
# print(image_noise_hr.size())
if color:
b, N, c, h, w = burst_noise.size()
feedData = burst_noise.view(b, -1, h, w)
else:
feedData = burst_noise
# print(feedData.size())
pred_i, pred = model(feedData, burst_noise[:, 0:burst_length, ...],image_noise_hr)
# del pred_i
pred_i = pred_i.detach().cpu()
print(pred_i.size())
pred_full = pixel_shuffle(pred_i,upscale_factor)
pred_full = pred_full
print(pred_full.size())
pred = pred.detach().cpu()
# print("Time : ", time.time()-begin)
gt = transforms.ToTensor()(Image.open(clean_path[i]).convert('RGB'))
gt = gt.unsqueeze(0)
# print(pred_i.size())
# print(pred[0].size())
psnr_t = calculate_psnr(pred, gt)
ssim_t = calculate_ssim(pred, gt)
print(i," pixel_shuffle UP : PSNR : ", str(calculate_psnr(pred_full, gt))," : SSIM : ", str(calculate_ssim(pred_full, gt)))
print(i," 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 = noisy_path[i].split("/")[-1].split(".")[0]
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)
"""
if args.save_img:
# print(np.array(trans(mf8[0])))
plt.figure(figsize=(30, 9))
plt.subplot(1,3,1)
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise DGF "+args.model_type, fontsize=26)
plt.subplot(1,3,2)
plt.imshow(np.array(trans(gt[0])))
plt.title("gt ", fontsize=26)
plt.subplot(1,3,3)
plt.imshow(np.array(trans(image_noise_hr[0])))
plt.title("noise ", fontsize=26)
plt.axis("off")
plt.suptitle(str(i)+" UP : PSNR : "+ str(psnr_t)+" : SSIM : "+ str(ssim_t), fontsize=26)
plt.savefig("checkpoints/22_DGF_" + args.checkpoint+str(i)+'.png',pad_inches=0)
"""
if __name__ == "__main__":
# argparse
parser = argparse.ArgumentParser(description='parameters for training')
parser.add_argument('--noise_dir','-n', default='/home/dell/Downloads/FullTest/noisy', help='path to noise image file')
parser.add_argument('--gt','-g', default='/home/dell/Downloads/FullTest/clean', help='path to noise image file')
parser.add_argument('--burst_length','-b' ,default=4, type=int, help='batch size')
parser.add_argument('--cuda', '-c', action='store_true', help='whether to train on the GPU')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='att_kpn_dgf_4_new',
help='the checkpoint to eval')
parser.add_argument('--model_type','-m' ,default="attKPN", help='type of model : KPN, attKPN, attWKPN , attKPN_Wave')
parser.add_argument('--save_img', "-s" ,default="", type=str, help='save image in eval_img folder ')
parser.add_argument('--load_type', "-l" ,default="best", type=str, help='Load type best_or_latest ')
args = parser.parse_args()
#
test_multi(args)