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gray_eval.py
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import argparse, os
import torch
from torch.autograd import Variable
import numpy as np
import time, math, glob
import scipy.io as sio
from matplotlib import pyplot as plt
import torch.nn as nn
import imageio
from gray_model import _NetG
parser = argparse.ArgumentParser(description="PyTorch DIDN Eval")
parser.add_argument("--cuda", action="store_true", help="use cuda?")
parser.add_argument("--model", default="./checkpoint/pretrained_gray/gray_model.pth", type=str, help="model path")
parser.add_argument("--output_path", default="./result/result_images/", type=str, help="output path")
parser.add_argument("--self_ensemble", action="store_true", help="Use self ensemble?")
parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
opt = parser.parse_args()
cuda = opt.cuda
def output_psnr_mse(img_orig, img_out):
squared_error = np.square(img_orig - img_out)
mse = np.mean(squared_error)
psnr = 10 * np.log10(1.0 / mse)
return psnr
if cuda:
print("=> use gpu id: '{}'".format(opt.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
if not torch.cuda.is_available():
raise Exception("No GPU found or Wrong gpu id, please run without --cuda")
model = _NetG()
checkpoint = torch.load(opt.model, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['model'].state_dict())
with torch.no_grad():
model.eval()
if cuda:
model = model.cuda()
else:
model = model.cpu()
origin_list = sorted(glob.glob("./data/Test/BSD68/original_mat_int/" + "*.*"))
noisy_list = sorted(glob.glob("./data/Test/BSD68/noisy_mat_s10_int/" + "*.*"))
origin_names = os.listdir("./data/Test/BSD68/original_mat_int/")
avg_psnr_predicted = 0.0
avg_psnr_noisy = 0.0
avg_elapsed_time = 0.0
ct = 0.0
for n in range(origin_list.__len__()):
origin_name = origin_list[n]
noisy_name = noisy_list[n]
origin = sio.loadmat(origin_name)['origin']
noisy = sio.loadmat(noisy_name)['noisy']
origin = origin.astype(float) / 255.
noisy = noisy.astype(float) / 255.
psnr_noisy = output_psnr_mse(origin, noisy)
avg_psnr_noisy += psnr_noisy
noisy_data = []
out_data = []
output = 0
if opt.self_ensemble:
# rotate / flip
noisy_data.append(noisy)
noisy_data.append(np.rot90(noisy, 1).copy())
noisy_data.append(np.rot90(noisy, 2).copy())
noisy_data.append(np.rot90(noisy, 3).copy())
noisy_data.append(np.fliplr(noisy_data[0]).copy())
noisy_data.append(np.fliplr(noisy_data[1]).copy())
noisy_data.append(np.fliplr(noisy_data[2]).copy())
noisy_data.append(np.fliplr(noisy_data[3]).copy())
for x in range(8):
noisy = Variable(torch.from_numpy(noisy_data[x]).float()).view(1, 1, noisy_data[x].shape[0], noisy_data[x].shape[1])
noisy = noisy.cuda()
start_time = time.time()
out = model(noisy)
out_data.append(out[0, 0, :, :].cpu().detach().numpy().astype(np.float32))
elapsed_time = time.time() - start_time
avg_elapsed_time += elapsed_time
out_data[4] = np.fliplr(out_data[4])
out_data[5] = np.fliplr(out_data[5])
out_data[6] = np.fliplr(out_data[6])
out_data[7] = np.fliplr(out_data[7])
out_data[1] = np.rot90(out_data[1], -1)
out_data[2] = np.rot90(out_data[2], -2)
out_data[3] = np.rot90(out_data[3], -3)
out_data[5] = np.rot90(out_data[5], -1)
out_data[6] = np.rot90(out_data[6], -2)
out_data[7] = np.rot90(out_data[7], -3)
for x in range(8):
output += out_data[x]
output /= 8.0
else: # no self ensemble
noisy = Variable(torch.from_numpy(noisy).float()).view(1, 1, noisy.shape[0], noisy.shape[1])
noisy = noisy.cuda()
start_time = time.time()
out = model(noisy)
elapsed_time = time.time() - start_time
avg_elapsed_time += elapsed_time
output = out.cpu()
output = output.data[0].numpy().astype(np.float32)
output = output[0,:,:]
output[output>1] = 1
output[output<0] = 0
psnr_predicted = output_psnr_mse(origin, output)
avg_psnr_predicted += psnr_predicted
ct += 1
output = output * 255.
output = np.uint8(np.round(output))
test_name = origin_names[n][:-4] + '.png' # .mat -> .png
imageio.imwrite(opt.output_path + test_name, output) # save result images
print(100 * ct / (origin_list.__len__()), "percent done")
avg_psnr_predicted = avg_psnr_predicted / ct
avg_psnr_noisy = avg_psnr_noisy / ct
print("PSNR_noisy=", avg_psnr_noisy)
print("PSNR_predicted=", avg_psnr_predicted)