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IQAmain.py
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import os
import torch
import random
import logger
import datetime
import numpy as np
from QAloss import QALoss
from IQAmodel import QAModel
from ignite.engine import Events
from argparse import ArgumentParser
from IQAdataset import get_data_loaders
from QAperformance import QAPerformance
from torch.optim import Adam, lr_scheduler
from torch.utils.tensorboard import SummaryWriter
from modified_ignite_engine import create_supervised_trainer, create_supervised_evaluator
metrics_printed = ['SROCC', 'KROCC', 'PLCC']
def writer_add_scalar(writer, status, dataset, scalars, iter):
for metric_print in metrics_printed:
writer.add_scalar('{}/{}/{}'.format(status, dataset, metric_print), scalars[metric_print], iter)
def run(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = QAModel(arch=args.arch, pretrained=args.pretrained, pool_mode=args.pool_mode).to(device) #
logger.log.info(model)
total_params = sum(p.numel() for p in model.parameters())
logger.log.info('total_params: {}'.format(total_params))
if args.ft_lr_ratio == 0.0:
for param in model.features.parameters():
param.requires_grad = False
optimizer = Adam([{'params': model.regression.parameters()},
{'params': model.fp.parameters()},
{'params': model.dr.parameters()}],
lr=args.lr, weight_decay=args.weight_decay)
else:
optimizer = Adam([{'params': model.regression.parameters()},
{'params': model.fp.parameters()},
{'params': model.dr.parameters()},
{'params': model.features.parameters(), 'lr': args.lr * args.ft_lr_ratio}],
lr=args.lr, weight_decay=args.weight_decay)
train_loader, val_loader, test_loader = get_data_loaders(args)
evaluator = create_supervised_evaluator(model, metrics={'IQA_performance': QAPerformance()}, device=device)
if args.evaluate:
checkpoint = torch.load(args.trained_model_file)
model.load_state_dict(checkpoint['model'])
evaluator.run(test_loader)
performance = evaluator.state.metrics
for metric_print in metrics_printed:
logger.log.info('{}, {}: {:.5f}'.format(args.dataset, metric_print, performance[metric_print].item()))
for metric_print in metrics_printed:
logger.log.info('{:.5f}'.format(performance[metric_print].item()))
np.save(args.save_result_file, performance)
return
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.lr_decay_step, gamma=args.lr_decay)
loss_func = QALoss()
trainer = create_supervised_trainer(model, optimizer, loss_func, device=device)
current_time = datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")
writer = SummaryWriter(log_dir='runs/{}-{}'.format(args.format_str, current_time))
global best_val_criterion, best_epoch
best_val_criterion, best_epoch = -100, -1
@trainer.on(Events.ITERATION_COMPLETED)
def iter_event_function(engine):
writer.add_scalar("train/loss", engine.state.output, engine.state.iteration)
# if args.debug:
# logger.log.info(engine.state.output)
@trainer.on(Events.EPOCH_COMPLETED)
def epoch_event_function(engine):
evaluator.run(train_loader)
performance = evaluator.state.metrics
writer_add_scalar(writer, 'train', args.dataset, performance, engine.state.epoch)
logger.log.info('Train {}: {:.5f} @epoch: {}'.format(args.val_criterion, performance[args.val_criterion], engine.state.epoch))
evaluator.run(val_loader)
performance = evaluator.state.metrics
writer_add_scalar(writer, 'val', args.dataset, performance, engine.state.epoch)
val_criterion = performance[args.val_criterion]
evaluator.run(test_loader)
performance = evaluator.state.metrics
writer_add_scalar(writer, 'test', args.dataset, performance, engine.state.epoch)
global best_val_criterion, best_epoch
if val_criterion > best_val_criterion:
checkpoint = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(checkpoint, args.trained_model_file)
best_val_criterion = val_criterion
best_epoch = engine.state.epoch
logger.log.info('Save current best model @best_val_criterion ({}): {:.5f} @epoch: {}'.format(args.val_criterion, best_val_criterion, best_epoch))
else:
logger.log.info('Model is not updated @val_criterion ({}): {:.5f} @epoch: {}'.format(args.val_criterion, val_criterion, engine.state.epoch))
scheduler.step()
@trainer.on(Events.COMPLETED)
def final_testing_results(engine):
writer.close () # close the Tensorboard writer
logger.log.info('best epoch: {}'.format(best_epoch))
checkpoint = torch.load(args.trained_model_file)
model.load_state_dict(checkpoint['model'])
evaluator.run(test_loader)
performance = evaluator.state.metrics
for metric_print in metrics_printed:
logger.log.info('{}, {}: {:.5f}'.format(args.dataset, metric_print, performance[metric_print].item()))
np.save(args.save_result_file, performance)
logger.destroy_logger()
trainer.run(train_loader, max_epochs=args.epochs)
if __name__ == "__main__":
parser = ArgumentParser(description='')
parser.add_argument("--seed", type=int, default=19920517)
parser.add_argument("--exp_id", type=int, default=0,
help='the exp split idx (default: 0)')
parser.add_argument("--model", type=str, default='IQA')
parser.add_argument('--arch', default='resnext101_32x8d', type=str,
help='arch name (default: resnext101_32x8d)')
parser.add_argument('--pool_mode', default='mean', type=str,
help='pool mode (default: mean)')
parser.add_argument('-pretrained', '--pretrained', type=int, default=1,
help='feature extractor (fe) network init mode， 0 for default random, 1 for ImageNet-pretrained (default: 1)')
parser.add_argument('-lr', '--lr', type=float, default=1e-4,
help='learning rate (default: 1e-4)')
parser.add_argument('-bs', '--batch_size', type=int, default=8,
help='batch size for training (default: 8)')
parser.add_argument('-e', '--epochs', type=int, default=30,
help='number of epochs to train (default: 30)')
parser.add_argument('-ft_lr_ratio', '--ft_lr_ratio', type=float, default=0.1,
help='ft_lr_ratio for fe (default: 0.1)')
parser.add_argument('-wd', '--weight_decay', type=float, default=0.0,
help='weight decay (default: 0.0)')
parser.add_argument('-lrd', '--lr_decay', type=float, default=0.1,
help='lr decay (default: 0.1)')
parser.add_argument('-olrd', '--overall_lr_decay', type=float, default=0.01,
help='overall lr decay (default: 0.01)')
parser.add_argument('-nrz', '--noresize', action='store_true',
help='No Resize?')
parser.add_argument('-rs_h', '--resize_size_h', default=498, type=int,
help='resize_size_h (default: 498)')
parser.add_argument('-rs_w', '--resize_size_w', default=664, type=int,
help='resize_size_w (default: 664)')
parser.add_argument('-eval', '--evaluate', action='store_true',
help='Evaluate only?')
parser.add_argument('-random', '--randomness', action='store_true',
help='Allow randomness during training?')
parser.add_argument('-debug', '--debug', action='store_true',
help='Debug?')
args = parser.parse_args()
args.val_criterion = 'SROCC'
if args.lr_decay == 1 or args.epochs < 3: # no lr decay
args.lr_decay_step = args.epochs
else: #
args.lr_decay_step = int(args.epochs/(1+np.log(args.overall_lr_decay)/np.log(args.lr_decay)))
if args.ft_lr_ratio != 0: #
if args.pretrained:
args.ft_lr_ratio = 0.1
else:
args.ft_lr_ratio = 1.0
args.dataset = 'KonIQ-10k' # ln -s database_path xxx
args.data_info = './data/KonIQ-10kinfo.mat'
args.train_ratio = 7058/10073
args.train_and_val_ratio = 8058/10073
if args.noresize:
args.resize_size_h = 768
args.resize_size_w = 1024
if not args.randomness:
torch.manual_seed(args.seed) #
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(args.seed)
random.seed(args.seed)
torch.utils.backcompat.broadcast_warning.enabled = True
args.format_str = '{}-{}-pretrained={}-pmode={}-resize={}-lr={}-bs={}-e={}-ftlrr={}-exp{}'\
.format(args.model, args.arch, args.pretrained, args.pool_mode, not args.noresize,
args.lr, args.batch_size, args.epochs, args.ft_lr_ratio, args.exp_id)
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
args.trained_model_file = 'checkpoints/' + args.format_str
if not os.path.exists('results'):
os.makedirs('results')
args.save_result_file = 'results/' + args.format_str
logger.create_logger('logs', args.format_str, False)
logger.log.info(args)
run(args)