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eval_4x.py
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from __future__ import print_function
import argparse
import os
import torch
from model import ABPN_v5
from dbpn_v1 import MSRResNet
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import numpy as np
from os.path import join
import time
import math
from dataset import is_image_file
from functools import reduce
#import cv2
from PIL import Image, ImageOps
from os import listdir
from prepare_images import *
import torch.utils.data as utils
from torch.autograd import Variable
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor")
parser.add_argument('--testBatchSize', type=int, default=16, help='testing batch size')
parser.add_argument('--gpu_mode', type=bool, default=True)
parser.add_argument('--chop_forward', type=bool, default=True)
parser.add_argument('--patch_size', type=int, default=32, help='0 to use original frame size')
parser.add_argument('--stride', type=int, default=32, help='0 to use original patch size')
parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--gpus', default=1, type=int, help='number of gpu')
parser.add_argument('--image_dataset', type=str, default='/home/data1/Test')
parser.add_argument('--model_type', type=str, default='ABPN')
parser.add_argument('--output', default='/home/data1/Test', help='Location to save checkpoint models')
parser.add_argument('--model', default='Model/ABPN_4x.pth', help='sr pretrained base model')
opt = parser.parse_args()
gpus_list = range(opt.gpus)
print(opt)
cuda = opt.gpu_mode
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
torch.manual_seed(opt.seed)
if cuda:
torch.cuda.manual_seed(opt.seed)
print('===> Building model ', opt.model_type)
model = ABPN_v5(input_dim=3, dim=32)
#model = torch.nn.DataParallel(model, device_ids=gpus_list)
if cuda:
model = model.cuda(gpus_list[0])
model_name = os.path.join(opt.model)
if os.path.exists(model_name):
model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
print('===> Loading datasets')
img_splitter = ImageSplitter(opt.patch_size, opt.upscale_factor, opt.stride)
def eval():
model.eval()
LR_filename = os.path.join(opt.image_dataset, 'LR')
HR_filename = os.path.join(opt.image_dataset, 'HR')
LR_image = [join(LR_filename, x) for x in listdir(LR_filename) if is_image_file(x)]
HR_image = [join(HR_filename, x) for x in listdir(HR_filename) if is_image_file(x)]
SR_image = [join(os.path.join(opt.image_dataset, 'SR'), x) for x in listdir(HR_filename) if is_image_file(x)]
count = 0
avg_psnr_predicted = 0.0
for i in range(LR_image.__len__()):
t0 = time.time()
target = Image.open(HR_image[i]).convert('RGB')
LR = Image.open(LR_image[i]).convert('RGB')
with torch.no_grad():
prediction = chop_forward(LR, model, opt.upscale_factor, opt.stride, opt.patch_size)
t1 = time.time()
#print("===> Processing: %s || Timer: %.4f sec." % (str(i), (t1 - t0)))
prediction = prediction * 255.0
prediction = prediction.clip(0, 255)
Image.fromarray(np.uint8(prediction)).save(SR_image[i])
GT = np.array(target).astype(np.float32)
GT_Y = rgb2ycbcr(GT)
prediction = np.array(prediction).astype(np.float32)
prediction_Y = rgb2ycbcr(prediction)
psnr_predicted = PSNR(prediction_Y, GT_Y, shave_border=opt.upscale_factor)
avg_psnr_predicted += psnr_predicted
count += 1
print("PSNR_predicted=", avg_psnr_predicted / count)
def modcrop(img, modulo):
(ih, iw) = img.size
ih = ih - (ih % modulo)
iw = iw - (iw % modulo)
img = img.crop((0, 0, ih, iw))
#y, cb, cr = img.split()
return img
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
float32, [0, 255]
float32, [0, 255]
'''
img.astype(np.float32)
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
rlt = rlt.round()
return rlt
def PSNR(pred, gt, shave_border):
pred = pred[shave_border:-shave_border, shave_border:-shave_border]
gt = gt[shave_border:-shave_border, shave_border:-shave_border]
imdff = pred - gt
rmse = math.sqrt(np.mean(imdff ** 2))
if rmse == 0:
return 100
return 20 * math.log10(255.0 / rmse)
transform = transforms.Compose([
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
]
)
def chop_forward(img, network, scale, stride, patch_size):
channel_swap = (1, 2, 0)
img = transform(img).unsqueeze(0)
img_patch = img_splitter.split_img_tensor(img)
testset = utils.TensorDataset(img_patch)
test_dataloader = utils.DataLoader(testset, num_workers=opt.threads,
drop_last=False, batch_size=opt.testBatchSize, shuffle=False)
out_box = []
for iteration, batch in enumerate(test_dataloader, 1):
input = Variable(batch[0]).cuda(gpus_list[0])
with torch.no_grad():
prediction = network(input)
for j in range(prediction.shape[0]):
out_box.append(prediction[j,:,:,:])
SR = img_splitter.merge_img_tensor(out_box)
SR = SR.data[0].numpy().transpose(channel_swap)
return SR
##Eval Start!!!!
eval()