eval.py

import matlab.engine
import argparse
import torch
from torch.autograd import Variable
import numpy as np
import time, math, glob
import scipy.io as sio
import cv2

parser = argparse.ArgumentParser(description="PyTorch EDSR Eval")
parser.add_argument("--cuda", action="store_true", help="use cuda?")
parser.add_argument("--model", default="checkpoint/model_edsr.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
eng = matlab.engine.start_matlab()

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 = sio.loadmat(image_name)['im_l']

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

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

    im_input = im_l.astype(np.float32).transpose(2,0,1)
    im_input = im_input.reshape(1,im_input.shape[0],im_input.shape[1],im_input.shape[2])
    im_input = Variable(torch.from_numpy(im_input/255.).float())

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

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

    HR_4x = HR_4x.cpu()

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

    im_h = im_h*255.
    im_h = np.clip(im_h, 0., 255.)
    im_h = im_h.transpose(1,2,0).astype(np.float32)

    im_h_matlab = matlab.double((im_h / 255.).tolist())
    im_h_ycbcr = eng.rgb2ycbcr(im_h_matlab)
    im_h_ycbcr = np.array(im_h_ycbcr._data).reshape(im_h_ycbcr.size, order='F').astype(np.float32) * 255.
    im_h_y = im_h_ycbcr[:,:,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)))