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test_DGF_codalab.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
import scipy.io
import scipy.io as sio
torch.manual_seed(0)
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_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
mat_folders = glob.glob(os.path.join(args.noise_dir, '*'))
model.eval()
torch.manual_seed(0)
trans = transforms.ToPILImage()
if not os.path.exists(args.save_img):
os.makedirs(args.save_img)
for mat_folder in mat_folders:
save_mat_folder = os.path.join(args.save_img,mat_folder.split("/")[-1])
for mat_file in glob.glob(os.path.join(mat_folder, '*')):
mat_contents = sio.loadmat(mat_file)
sub_image, y_gb, x_gb = mat_contents['image'], mat_contents['y_gb'][0][0], mat_contents['x_gb'][0][0]
# print(sub_image)
image_noise = transforms.ToTensor()(Image.fromarray(np.array(sub_image)))
image_noise,image_noise_hr = load_data(image_noise,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
print(pred.size())
pred = np.array(trans(pred[0].cpu()))
print(pred.shape)
if args.save_img != '':
if not os.path.exists(save_mat_folder):
os.makedirs(save_mat_folder)
# mat_contents['image'] = pred
# print(mat_contents)
print("save : ", os.path.join(save_mat_folder,mat_file.split("/")[-1]))
data = {"image": pred, "y_gb": mat_contents['y_gb'][0][0], "x_gb": mat_contents['x_gb'][0][0],
"y_lc": mat_contents['y_lc'][0][0], "x_lc": mat_contents['x_lc'][0][0], 'size': mat_contents['size'][0][0],
"H": mat_contents['H'][0][0], "W": mat_contents['W'][0][0]}
# print(data)
sio.savemat(os.path.join(save_mat_folder,mat_file.split("/")[-1]), data)
if __name__ == "__main__":
# argparse
parser = argparse.ArgumentParser(description='parameters for training')
parser.add_argument('--noise_dir','-n', default='/home/dell/Downloads/FullTest/test/', 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="/home/dell/Downloads/FullTest/test_re", type=str, help='save image in eval_img folder ')
parser.add_argument('--load_type', "-l" ,default="lastest", type=str, help='Load type best_or_latest ')
args = parser.parse_args()
#
test_multi(args)
# gt_file = "data/ValidationGtBlocksSrgb.mat"
# mat = scipy.io.loadmat(gt_file)['ValidationGtBlocksSrgb']
# print(mat.shape)
# img = Image.fromarray(mat[0][1], 'RGB')
# gt = transforms.ToTensor()(img)
# print(gt)