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demo_4x.py
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demo_4x.py
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from __future__ import print_function
import argparse
import os
import torch
from model import ABPN_v5
import torchvision.transforms as transforms
from collections import OrderedDict
import logging
import numpy as np
from os.path import join
import time
import math
from dataset import is_image_file
import utils_logger
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=8, 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=64, help='0 to use original frame size')
parser.add_argument('--stride', type=int, default=64, help='0 to use original patch size')
parser.add_argument('--threads', type=int, default=0, 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='LR')
parser.add_argument('--model_type', type=str, default='ABPN')
parser.add_argument('--image_output', default='Result', help='Location to save checkpoint models')
parser.add_argument('--model', default='Model/ABPN_4x.pth', help='sr pretrained base model')
opt = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('===> Building model ', opt.model_type)
model = ABPN_v5(input_dim=3, dim=32)
model = model.to(device)
model_name = os.path.join(opt.model)
if os.path.exists(model_name):
pretrained_dict = torch.load(model_name, map_location=lambda storage, loc: storage)
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Pre-trained SR model is loaded.')
img_splitter = ImageSplitter(opt.patch_size, opt.upscale_factor, opt.stride)
def eval():
utils_logger.logger_info('AIM-track', log_path='AIM-track.log')
logger = logging.getLogger('AIM-track')
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
# number of parameters
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
LR_filename = opt.image_dataset
LR_image = [join(LR_filename, x) for x in listdir(LR_filename) if is_image_file(x)]
SR_image = [join(opt.image_output, x) for x in listdir(LR_filename) if is_image_file(x)]
# record PSNR, runtime
test_results = OrderedDict()
test_results['runtime'] = []
logger.info(opt.image_dataset)
logger.info(opt.image_output)
idx = 0
for i in range(LR_image.__len__()):
idx += 1
img_name, ext = os.path.splitext(LR_image[i])
logger.info('{:->4d}--> {:>10s}'.format(idx, img_name+ext))
LR = Image.open(LR_image[i]).convert('RGB')
LR_90 = LR.transpose(Image.ROTATE_90)
LR_180 = LR.transpose(Image.ROTATE_180)
LR_270 = LR.transpose(Image.ROTATE_270)
LR_f = LR.transpose(Image.FLIP_LEFT_RIGHT)
LR_90f = LR_90.transpose(Image.FLIP_LEFT_RIGHT)
LR_180f = LR_180.transpose(Image.FLIP_LEFT_RIGHT)
LR_270f = LR_270.transpose(Image.FLIP_LEFT_RIGHT)
with torch.no_grad():
pred = chop_forward(LR, model)
pred_90 = chop_forward(LR_90, model)
pred_180 = chop_forward(LR_180, model)
pred_270 = chop_forward(LR_270, model)
pred_f = chop_forward(LR_f, model)
pred_90f = chop_forward(LR_90f, model)
pred_180f = chop_forward(LR_180f, model)
pred_270f = chop_forward(LR_270f, model)
pred_90 = np.rot90(pred_90, 3)
pred_180 = np.rot90(pred_180, 2)
pred_270 = np.rot90(pred_270, 1)
pred_f = np.fliplr(pred_f)
pred_90f = np.rot90(np.fliplr(pred_90f), 3)
pred_180f = np.rot90(np.fliplr(pred_180f), 2)
pred_270f = np.rot90(np.fliplr(pred_270f), 1)
prediction = (pred + pred_90 + pred_180 + pred_270 + pred_f + pred_90f + pred_180f + pred_270f) * 255.0 / 8.0
prediction = prediction.clip(0, 255)
Image.fromarray(np.uint8(prediction)).save(SR_image[i])
# 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):
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]).to(device)
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()