eval.py

import argparse
import torch
from torch.autograd import Variable
import numpy as np
import time, math, glob
import scipy.io as sio
import matplotlib.pyplot as plt

parser = argparse.ArgumentParser(description="PyTorch LapSRN Eval")
parser.add_argument("--cuda", action="store_true", help="use cuda?")
parser.add_argument("--model", default="model/model_epoch_100.pth", type=str, help="model path")
parser.add_argument("--dataset", default="Set5", type=str, help="dataset name, Default: Set5")
parser.add_argument("--scale", default=4, type=int, help="scale factor, Default: 4")

def PSNR(pred, gt, shave_border=0):
    height, width = pred.shape[:2]
    pred = pred[shave_border:height - shave_border, shave_border:width - shave_border]
    gt = gt[shave_border:height - shave_border, shave_border:width - shave_border]
    imdff = pred - gt
    rmse = math.sqrt(np.mean(imdff ** 2))
    if rmse == 0:
        return 100
    return 20 * math.log10(255.0 / rmse)

opt = parser.parse_args()
cuda = opt.cuda

if cuda and not torch.cuda.is_available():
    raise Exception("No GPU found, please run without --cuda")

model = torch.load(opt.model)["model"]

image_list = glob.glob(opt.dataset+"/*.*") 

avg_psnr_predicted = 0.0
avg_psnr_bicubic = 0.0
avg_elapsed_time = 0.0

for image_name in image_list:
    print("Processing ", image_name)
    im_gt_y = sio.loadmat(image_name)['im_gt_y']
    im_b_y = sio.loadmat(image_name)['im_b_y']
    im_l_y = sio.loadmat(image_name)['im_l_y']

    im_gt_y = im_gt_y.astype(float)
    im_b_y = im_b_y.astype(float)
    im_l_y = im_l_y.astype(float)

    psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=opt.scale)
    avg_psnr_bicubic += psnr_bicubic

    im_input = im_l_y/255.

    im_input = Variable(torch.from_numpy(im_input).float()).view(1, -1, im_input.shape[0], im_input.shape[1])

    if cuda:
        model = model.cuda()
        im_input = im_input.cuda()
    else:
        model = model.cpu()

    start_time = time.time()
    HR_2x, HR_4x = model(im_input)
    elapsed_time = time.time() - start_time
    avg_elapsed_time += elapsed_time

    HR_4x = HR_4x.cpu()

    im_h_y = HR_4x.data[0].numpy().astype(np.float32)

    im_h_y = im_h_y*255.
    im_h_y[im_h_y<0] = 0
    im_h_y[im_h_y>255.] = 255.
    im_h_y = im_h_y[0,:,:]

    psnr_predicted = PSNR(im_gt_y, im_h_y,shave_border=opt.scale)
    avg_psnr_predicted += psnr_predicted

print("Scale=", opt.scale)
print("Dataset=", opt.dataset)
print("PSNR_predicted=", avg_psnr_predicted/len(image_list))
print("PSNR_bicubic=", avg_psnr_bicubic/len(image_list))
print("It takes average {}s for processing".format(avg_elapsed_time/len(image_list)))