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test.py
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test.py
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import torch
import argparse
import utility
from model.mwcnn import Model
from torch.utils.data import DataLoader
# import h5py
from option import args
from data.data_provider import SingleLoader
torch.set_num_threads(4)
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
# import model
from torchsummary import summary
from utils.metric import calculate_psnr
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms
from collections import OrderedDict
if __name__ == "__main__":
data_set = SingleLoader(noise_dir=args.noise_dir,gt_dir=args.gt_dir,image_size=args.image_size)
data_loader = DataLoader(
data_set,
batch_size=1,
shuffle=False,
num_workers=4
)
model = Model(args)
state_dict = torch.load('experiment/checkpoint')
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(new_state_dict)
# model.load_state_dict(state_dict)
model.eval()
trans = transforms.ToPILImage()
for epoch in range(10):
for step, (noise, gt) in enumerate(data_loader):
pred = model(noise,[10])
print(pred.size())
plt.subplot(1, 2, 1)
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise ", fontsize=20)
plt.subplot(1,2,2)
plt.imshow(np.array(trans(gt[0])))
plt.show()
print("PSNR : ",calculate_psnr(pred,gt)) # print(model)
# print(summary(model,[(3,512,512),[8]]))