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train_main.py
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train_main.py
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"""
Training script for HyperRes: Hypernetwork-Based Adaptive Image Restoration
Paper: https://arxiv.org/abs/2206.05970
By Shai Aharon [email protected]
"""
from __future__ import print_function
import argparse
import itertools
import shutil
import time
import os
from itertools import chain
import functools
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import numpy as np
from models.Unet import RDUNet, init_weights
from models.HyperRes import HyperRes
from utils import AverageMeter, Logger
from utils.DataUtils import CommonTools
from utils.DataUtils.CommonTools import calculate_psnr, saveImage, weights_init_kaiming, postProcessForStats
from utils.DataUtils.TrainLoader import NoisyDataset
import cv2
parser = argparse.ArgumentParser(description='PyTorch Multiple Objectives Network Training')
parser.add_argument('--data', metavar='DIR', help='path to dataset')
parser.add_argument('--workers', default=0, type=int, metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='Set manual epoch number (for resuming)')
parser.add_argument('--batch-size', default=16, type=int,
metavar='N',
help='Mini-batch size (default: 16)')
parser.add_argument('--lr', default=1e-4, type=float, metavar='LR', help='Initial learning rate',
dest='lr')
parser.add_argument('--weight-decay', default=0, type=float,
metavar='W', help='Weight decay (default: 0)',
dest='weight_decay')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='Path to the latest checkpoint (default: none)')
parser.add_argument('--seed', default=0, type=int, help='seed for initializing training. ')
parser.add_argument('--schedule', type=int, nargs='+', default=[500000], help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
parser.add_argument('--norm_f', type=float, default=255, help='The normalization factor for the distortion levels.')
parser.add_argument('--checkpoint', default='pre_trained', type=str, metavar='PATH',
help='Path to save checkpoint (default: pre_trained)')
parser.add_argument('--lvls', type=int, nargs='+', default=[15], help='A list of corruptions levels to train on')
parser.add_argument('--meta_blocks', type=int, default=16, help='Number of Meta Blocks (default: 16)')
parser.add_argument('--steps', type=int, default=1000000, help='Number of total steps')
parser.add_argument('--valid', type=str, default='valid',
help='Which dataset to use for evaluation, Validation or Test')
parser.add_argument('--device', type=str, default='cpu', help='Device to run on,[cpu,cuda,cuda:0..]')
parser.add_argument('--val_iter', type=int, nargs='+', default=[1, 10, 10, 50],
help='Run validation every X iterations, last interval is applied until the end of the training')
parser.add_argument('--no_bias', dest='bias', default=True, action='store_false',
help='Add Bias in the ResBlocks')
parser.add_argument('--parallel','-p', dest='parallel', default=False, action='store_true',
help='Check to run on parallel GPUs')
parser.add_argument('-y', '-Y', '--gray', dest='y_channel', default=False, action='store_true',
help='Train on Grayscale only')
parser.add_argument('--data_type', type=str, default='n', choices=['n', 'sr', 'j'],
help='Defines the task data, de(n)oise, super-resolution(sr), de(j)peg.')
def main():
global args
# Args handling
args = parser.parse_args()
# Create model
CommonTools.set_random_seed(args.seed)
print("=> Assembling model 'HyperRes Network'")
model = HyperRes(meta_blocks=args.meta_blocks, level=args.lvls, device=args.device,
gray=args.y_channel, norm_factor=args.norm_f)
# model = RDUNet(levels=args.lvls)
# Weight Normalization
model.apply(functools.partial(weights_init_kaiming, scale=0.1))
# model.apply(functools.partial(init_weights()))
model.to(args.device)
if args.parallel:
model = torch.nn.DataParallel(model).to(args.device)
# Define loss function (criterion) and optimizer
criterion = nn.L1Loss().cuda()
optimizer = torch.optim.Adam(model.parameters(), args.lr, weight_decay=args.weight_decay)
# Optionally resume from a checkpoint
title = 'UnetHyperRes'
if not os.path.isdir(args.checkpoint):
os.makedirs(args.checkpoint, exist_ok=True)
best_prec1 = 0
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
args.checkpoint = os.path.dirname(args.resume)
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
torch.backends.cudnn.benckmark = True
# Data loading code
crop_size = 96
args.crop_size = crop_size
train_loader = NoisyDataset(
os.path.join(args.data, 'train'),
cor_lvls=args.lvls,
crop_size=crop_size,
phase='train',
lr_prefix=args.data_type,
interp=False
)
data_set_len = len(train_loader)
train_loader = torch.utils.data.DataLoader(
train_loader,
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True)
val_loader = NoisyDataset(
os.path.join(args.data, args.valid),
cor_lvls=args.lvls,
phase='test',
lr_prefix=args.data_type,
interp=False
)
val_loader = torch.utils.data.DataLoader(
val_loader,
batch_size=1, shuffle=False,
num_workers=1, pin_memory=True)
val_loader_len = len(val_loader)
tot_epochs = int(np.ceil(args.steps / data_set_len)) * 16 + 5
args.epochs = tot_epochs
val_idx = 0
global curr_step
curr_step = 0
# Training loop
for epoch in range(args.start_epoch, tot_epochs):
print('\nEpoch: [Epoch: {:d}/{:d}, Step: {:d}/{:d}]'.format(epoch + 1, tot_epochs, curr_step, args.steps))
print('LR: {}'.format(optimizer.param_groups[0]['lr']))
# Train for one epoch
train_loss, train_acc = train(train_loader, model, criterion, optimizer)
print("=============")
# Evaluate on validation set
is_best = False
if epoch % 2 == 0:
val_idx += 1
last_val = epoch
val_loss, prec1 = evaluate(val_loader, val_loader_len, model, criterion)
print("Validation PSNR:")
[print("\t{}: {:.3f}".format(lvl, psnr)) for lvl, psnr in zip(args.lvls, prec1)]
lr = optimizer.param_groups[0]['lr']
# append logger file
logger.append([lr, train_loss, val_loss, train_acc[0], prec1[0]])
is_best = sum(prec1) / len(prec1) > best_prec1
best_prec1 = max(sum(prec1) / len(prec1), best_prec1)
if curr_step > args.steps:
break
save_checkpoint({
'epoch': epoch + 1,
'arch': 'Unet',
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
'sigmas': args.lvls,
}, is_best, checkpoint=args.checkpoint, epoch=epoch)
# Run Test
test_loader = NoisyDataset(
os.path.join(args.data, 'test'),
cor_lvls=args.lvls,
phase='test', lr_prefix=args.data_type)
test_loader = torch.utils.data.DataLoader(
test_loader,
batch_size=1, shuffle=False,
num_workers=1, pin_memory=True)
test_loader_len = len(test_loader)
loss_avg, psnr_avg = evaluate(test_loader, test_loader_len, model, criterion)
print("Test PSNR")
[print("\t{}: {:.3f}".format(lvl, psnr)) for lvl, psnr in zip(args.lvls, psnr_avg)]
logger.close()
print('Best accuracy:')
print(best_prec1)
def train(train_loader, model, criterion, optimizer):
global curr_step
losses = AverageMeter()
sig_loss = [AverageMeter() for _ in range(len(args.lvls))]
# switch to train mode
model.train()
sig_frac = np.array([1.0 / len(args.lvls) for _ in args.lvls])
st = time.time()
for i, data in enumerate(train_loader):
updateLR(curr_step, optimizer)
target = data['target'].to(args.device, non_blocking=True)
target = [target for _ in range(len(args.lvls))]
images = [x.to(args.device) for x in data['image']]
# Compute output
optimizer.zero_grad()
output = model(images)
loss = 0
for j in range(len(args.lvls)):
loss += criterion(output[j], target[j]).mul(sig_frac[j])
# Compute gradient and do SGD step
loss.backward()
optimizer.step()
log_stat = {"Loss": loss.item(), 'LR': optimizer.param_groups[0]['lr']}
curr_step += 1 #train_loader.batch_size
losses.update(loss.item())
if i % 10 == 0:
print("\rStep: {}|\tLoss: {:.4f}".format(curr_step, losses.avg), end='')
if curr_step > args.steps:
break
print("\nEpoch Time: {:.3f}".format(time.time() - st))
return losses.avg, [t.avg for t in sig_loss]
def updateLR(curr_step, optimizer):
lr = args.lr * 0.1 ** sum([curr_step > x for x in args.schedule])
optimizer.param_groups[0]['lr'] = lr
def evaluate(val_loader, val_loader_len, model, criterion,val_phase='Test'):
print("Running Evaluation...")
losses = AverageMeter()
lvl_loss = [AverageMeter() for _ in range(len(args.lvls))]
# Switch to evaluate mode
model.eval()
smp_plot_n = len(args.lvls)
loss = 0
wand_img = []
random_image = np.random.randint(val_loader_len)
model.eval()
with torch.no_grad():
for i, data in enumerate(val_loader):
target = data['target'].to(args.device, non_blocking=True)
images = [x.to(args.device) for x in data['image']]
# Compute output
output = model(images)
for j in range(len(args.lvls)):
loss += criterion(output[j], target)
if i == random_image:
im1 = saveImage(
output[j].detach().cpu().numpy()[0],
images[j].detach().cpu().numpy()[0],
j)
# Measure PSNR
for out_idx, out in enumerate(output[j]):
imgs = postProcessForStats([target[out_idx], out])
trg, out = imgs
psnr = calculate_psnr(out, trg)
lvl_loss[j].update(psnr)
losses.update(loss.item())
# Switch back to train mode
model.train()
return losses.avg, [t.avg for t in lvl_loss]
def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth', epoch=None):
filepath = os.path.join(checkpoint, filename)
torch.save(state, filepath)
shutil.copyfile(filepath, os.path.join(checkpoint, 'latest.pth'))
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth'))
if __name__ == '__main__':
main()