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
parser = argparse.ArgumentParser(description="PyTorch VDSR Eval")
parser.add_argument("--cuda", action="store_true", help="use cuda?")
parser.add_argument("--model", default="model/model_epoch_50.pth", type=str, help="model path")
parser.add_argument("--dataset", default="Set5", type=str, help="dataset name, Default: Set5")
parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
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:
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 = torch.load(opt.model, map_location=lambda storage, loc: storage)["model"]
scales = [2,3,4]
image_list = glob.glob(opt.dataset+"_mat/*.*")
for scale in scales:
avg_psnr_predicted = 0.0
avg_psnr_bicubic = 0.0
avg_elapsed_time = 0.0
count = 0.0
for image_name in image_list:
if str(scale) in image_name:
count += 1
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_gt_y = im_gt_y.astype(float)
im_b_y = im_b_y.astype(float)
psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=scale)
avg_psnr_bicubic += psnr_bicubic
im_input = im_b_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 = model(im_input)
elapsed_time = time.time() - start_time
avg_elapsed_time += elapsed_time
HR = HR.cpu()
im_h_y = HR.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=scale)
avg_psnr_predicted += psnr_predicted
print("Scale=", scale)
print("Dataset=", opt.dataset)
print("PSNR_predicted=", avg_psnr_predicted/count)
print("PSNR_bicubic=", avg_psnr_bicubic/count)
print("It takes average {}s for processing".format(avg_elapsed_time/count))