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main.py
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from __future__ import print_function
import argparse
from math import log10
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from modules.MSDTGP_arc import Net as MSDTGP
from data.data import get_training_set, get_eval_set, get_test_set
import pdb
import socket
import time
import math
import numpy as np
from tensorboardX import SummaryWriter
# from ptflops import get_model_complexity_info
# 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('--batchSize', type=int, default=1, help='training batch size')
parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size')
parser.add_argument('--start_epoch', type=int, default=1, help='Starting epoch for continuing training')
parser.add_argument('--nEpochs', type=int, default=50, help='number of epochs to train for')
parser.add_argument('--snapshots', type=int, default=1, help='Snapshots')
parser.add_argument('--lr', type=float, default=8e-5, help='Learning Rate. Default=0.01')
parser.add_argument('--gpu_mode', type=bool, default=True)
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')
# Training set------------------------------------------------------------
parser.add_argument('--data_dir', type=str, default='D:\Matlab/bin\Video_processing\Jilin-1 dataset/train')
parser.add_argument('--file_list', type=str, default='Jilin189.txt')
parser.add_argument('--other_dataset', type=bool, default=True, help="use other dataset than vimeo-90k")
# Test while training------------------------------------------------------------
parser.add_argument('--test_dir', type=str, default='D:\Matlab/bin\Video_processing\Jilin-1 dataset\eval')
parser.add_argument('--test_file_list', type=str, default='002.txt')
#-------------------------------------------------------------------
parser.add_argument('--future_frame', type=bool, default=True, help="use future frame")
parser.add_argument('--nFrames', type=int, default=5)
parser.add_argument('--patch_size', type=int, default=32, help='0 to use original frame size')
parser.add_argument('--data_augmentation', type=bool, default=True)
parser.add_argument('--model_type', type=str, default='MSDTGP')
parser.add_argument('--residual', type=bool, default=False)
parser.add_argument('--pretrained_sr', default='4x_DESKTOP-0NFK80ARBPNF7_epoch_44.pth',
help='sr pretrained base model default=3x_dl10VDBPNF7_epoch_84.pth')
parser.add_argument('--pretrained', type=bool, default=False)
parser.add_argument('--save_folder', default='weights_Full_model_F5/', help='Location to save checkpoint models')
parser.add_argument('--prefix', default='F7', help='Location to save checkpoint models')
opt = parser.parse_args()
gpus_list = range(opt.gpus)
hostname = str(socket.gethostname())
cudnn.benchmark = True
print(opt)
writer = SummaryWriter('runs/Full_model_F5')
def PSNR(pred, gt, shave_border=0):
imdff = pred - gt
rmse = math.sqrt(np.mean(imdff ** 2))
if rmse == 0:
return 100
return 20 * math.log10(255.0 / rmse)
def print_network(net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print(net)
print('Total number of parameters: %f M' % (num_params / 1e6))
def checkpoint(epoch):
model_out_path = opt.save_folder + str(
opt.upscale_factor) + 'x_' + hostname + opt.model_type + opt.prefix + "_epoch_{}.pth".format(epoch)
torch.save(model.state_dict(), model_out_path)
print("Checkpoint saved to {}".format(model_out_path))
def save_best_model(bestepoch):
model_out_path = opt.save_folder + 'best_' + str(
opt.upscale_factor) + 'x_' + opt.model_type + "_epoch_{}.pth".format(bestepoch)
torch.save(model.state_dict(), model_out_path)
print("BestModel saved to {}".format(model_out_path))
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('===> Loading datasets')
train_set = get_training_set(opt.data_dir, opt.nFrames, opt.upscale_factor, opt.data_augmentation, opt.file_list,
opt.patch_size)
training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)
eval_set = get_eval_set(opt.test_dir, opt.nFrames, opt.upscale_factor, opt.test_file_list)
eval_data_loader = DataLoader(dataset=eval_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)
print('===> Building model ', opt.model_type)
if opt.model_type == 'MSDTGP':
model = MSDTGP(num_channels=3, base_filter=256, feat=64, num_stages=3, n_resblock=5, nFrames=opt.nFrames,
scale_factor=opt.upscale_factor)
model = torch.nn.DataParallel(model, device_ids=gpus_list)
criterion = nn.L1Loss()
print('---------- Networks architecture -------------')
print_network(model)
print('----------------------------------------------')
if opt.pretrained:
model_name = os.path.join(opt.save_folder + opt.pretrained_sr)
if os.path.exists(model_name):
# model= torch.load(model_name, map_location=lambda storage, loc: storage)
model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
if cuda:
model = model.cuda(gpus_list[0])
criterion = criterion.cuda(gpus_list[0])
optimizer = optim.Adam(model.parameters(), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8)
best_epoch = 0
best_test_psnr = 0.0
for epoch in range(opt.start_epoch, opt.nEpochs + 1):
epoch_loss = 0
model.train()
for iteration, batch in enumerate(training_data_loader, 1):
gt, input, neigbor, bicubic = batch[0], batch[1], batch[2], batch[3]
if cuda:
gt = Variable(gt).cuda(gpus_list[0])
input = Variable(input).cuda(gpus_list[0])
bicubic = Variable(bicubic).cuda(gpus_list[0])
neigbor = [Variable(j).cuda(gpus_list[0]) for j in neigbor]
optimizer.zero_grad()
t0 = time.time()
prediction = model(input, neigbor)
if opt.residual:
prediction = prediction + bicubic
loss = criterion(prediction, gt)
t1 = time.time()
epoch_loss += loss.item()
loss.backward()
optimizer.step()
print("===> Epoch[{}]({}/{}): Loss: {:.4f} || Timer: {:.4f} sec.".format(epoch, iteration,
len(training_data_loader), loss.item(),
(t1 - t0)))
print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss / len(training_data_loader)))
writer.add_scalar('Avg. Loss', epoch_loss / len(training_data_loader), epoch)
# test while training
count = 1
avg_psnr_predicted = 0.0
avg_test_psnr = 0.0
model.eval()
for batch in eval_data_loader:
gt, input, neigbor, bicubic = batch[0], batch[1], batch[2], batch[3]
with torch.no_grad():
gt = Variable(gt).cuda(gpus_list[0])
input = Variable(input).cuda(gpus_list[0])
bicubic = Variable(bicubic).cuda(gpus_list[0])
neigbor = [Variable(j).cuda(gpus_list[0]) for j in neigbor]
t0 = time.time()
with torch.no_grad():
prediction = model(input, neigbor)
if opt.residual:
prediction = prediction + bicubic
t1 = time.time()
print("===> Processing: %s || Timer: %.4f sec." % (str(count), (t1 - t0)))
prediction = prediction.cpu()
prediction = prediction.data[0].numpy().astype(np.float32)
prediction = prediction * 255.
gt = gt.cpu()
gt = gt.squeeze().numpy().astype(np.float32)
gt = gt * 255.
psnr_predicted = PSNR(prediction, gt, shave_border=opt.upscale_factor)
print(psnr_predicted)
avg_psnr_predicted += psnr_predicted
avg_test_psnr = avg_psnr_predicted / len(eval_data_loader)
count += 1
print("===> Epoch {} Complete: Avg. PSNR: {:.4f}".format(epoch, avg_psnr_predicted / len(eval_data_loader)))
if avg_test_psnr > best_test_psnr:
best_epoch = epoch
best_test_psnr = avg_test_psnr
if epoch == opt.nEpochs:
print('Best_epoch:{:.4f},Best_psnr={:.6f}'.format(best_epoch, best_test_psnr))
save_best_model(best_epoch)
writer.add_scalar('Avg. PSNR', avg_psnr_predicted / len(eval_data_loader), epoch)
# learning rate is decayed by a factor of 10 every half of total epochs
if (epoch+1) % (opt.nEpochs/2) == 0:
for param_group in optimizer.param_groups:
param_group['lr'] /= 10.0
print('Learning rate decay: lr={}'.format(optimizer.param_groups[0]['lr']))
if (epoch+1) % (opt.snapshots) == 0:
checkpoint(epoch)