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test_custom_noise.py
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test_custom_noise.py
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import argparse
import os
import torch
from utils.training_util import load_checkpoint
from utils.data_provider_DGF import pixel_unshuffle
from model.KPN_noise_estimate import KPN_noise,Att_KPN_noise,Att_Weight_KPN_noise
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms
import glob
from PIL import Image
import time
from torch.nn import functional as F
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 = pixel_unshuffle(image_noise, 2)
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
def test_multi(args):
color = True
burst_length = 8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model_type == "attKPN":
model = Att_KPN_noise(
color=color,
burst_length=burst_length,
blind_est=False,
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_noise(
color=color,
burst_length=burst_length,
blind_est=False,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "KPN":
model = KPN_noise(
color=color,
burst_length=burst_length,
blind_est=False,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
# model2 = KPN(
# 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
# )
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'])
#############################################
# checkpoint_dir = "checkpoints/" + "kpn"
# 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')
# state_dict = ckpt['state_dict']
# new_state_dict = OrderedDict()
# if not args.cuda:
# for k, v in state_dict.items():
# name = k[7:] # remove `module.`
# new_state_dict[name] = v
# # model.load_state_dict(ckpt['state_dict'])
# model2.load_state_dict(new_state_dict)
###########################################
print('The model has been loaded from epoch {}, n_iter {}.'.format(ckpt['epoch'], ckpt['global_iter']))
# switch the eval mode
model.to(device)
model.eval()
# model2.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]
for i in range(len(noisy_path)):
image_noise = load_data(noisy_path[i],burst_length)
begin = time.time()
image_noise_batch = image_noise.to(device)
burst_noise = image_noise_batch.to(device)
pred_i, pred = model(burst_noise)
del pred_i
pred = pred.detach().cpu()
gt = transforms.ToTensor()(Image.open(clean_path[i]).convert('RGB'))
# print(pred_i.size())
# print(pred.size())
# print(gt.size())
gt = gt.unsqueeze(0)
_, _, h_hr, w_hr = gt.size()
_, _, h_lr, w_lr = pred.size()
gt_down = F.interpolate(gt,(h_lr,w_lr), mode='bilinear', align_corners=True)
pred_up = F.interpolate(pred,(h_hr,w_hr), mode='bilinear', align_corners=True)
# print("After interpolate")
# print(pred_up.size())
# print(gt_down.size())
psnr_t_up = calculate_psnr(pred_up, gt)
ssim_t_up = calculate_ssim(pred_up, gt)
psnr_t_down = calculate_psnr(pred, gt_down)
ssim_t_down = calculate_ssim(pred, gt_down)
print(i," UP : PSNR : ", str(psnr_t_up)," : SSIM : ", str(ssim_t_up), " : DOWN : PSNR : ", str(psnr_t_down)," : SSIM : ", str(ssim_t_down))
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 noise estimate "+ 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_up)+" : SSIM : "+ str(ssim_t_up), fontsize=25)
plt.savefig( os.path.join(args.save_img,image_name + "_" + args.checkpoint + '.png'),pad_inches=0)
# print(np.array(trans(mf8[0])))
"""
if args.save_img:
plt.figure(figsize=(30, 9))
plt.subplot(1,3,1)
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise "+args.model_type, fontsize=26)
# plt.subplot(1,3,2)
# plt.imshow(np.array(trans(pred2[0])))
# plt.title("denoise KPN")
# plt.show()
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[0][1])))
plt.title("noise ", fontsize=26)
plt.suptitle(str(i)+" UP : PSNR : "+ str(psnr_t_up)+" : SSIM : "+ str(ssim_t_up)+ " : DOWN : PSNR : "+ str(psnr_t_down)+" : SSIM : "+ str(ssim_t_down), fontsize=26)
plt.savefig("checkpoints/"+ args.model_type+str(i)+'.png',pad_inches=0)
"""
# plt.show()
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('--cuda', '-c', action='store_true', help='whether to train on the GPU')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='att_kpn_noise',
help='the checkpoint to eval')
parser.add_argument('--model_type', '-m' ,default="attKPN", help='type of model : KPN, attKPN, attWKPN')
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)