import argparse import time from collections import OrderedDict from datetime import datetime from data import * from models import model_factory from utils import * from optim import Nadam, AdaBound import scheduler import torch import torch.nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data as data import torchvision.utils torch.backends.cudnn.benchmark = True parser = argparse.ArgumentParser(description='Training') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL', help='Name of model to train (default: "countception"') parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', help='Optimizer (default: "sgd"') parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON', help='Optimizer Epsilon (default: 1e-8)') parser.add_argument('--gp', default='avg', type=str, metavar='POOL', help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")') parser.add_argument('--tta', type=int, default=0, metavar='N', help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)') parser.add_argument('--pretrained', action='store_true', default=False, help='Start with pretrained version of specified network (if avail)') parser.add_argument('--img-size', type=int, default=224, metavar='N', help='Image patch size (default: 224)') parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N', help='input batch size for training (default: 32)') parser.add_argument('-s', '--initial-batch-size', type=int, default=0, metavar='N', help='initial input batch size for training (default: 0)') parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of epochs to train (default: 2)') parser.add_argument('--start-epoch', default=None, type=int, metavar='N', help='manual epoch number (useful on restarts)') parser.add_argument('--decay-epochs', type=int, default=30, metavar='N', help='epoch interval to decay LR') parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', help='LR decay rate (default: 0.1)') parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER', help='LR scheduler (default: "step"') parser.add_argument('--drop', type=float, default=0.0, metavar='DROP', help='Dropout rate (default: 0.1)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--weight-decay', type=float, default=0.0005, metavar='M', help='weight decay (default: 0.0001)') parser.add_argument('--seed', type=int, default=42, metavar='S', help='random seed (default: 42)') parser.add_argument('--log-interval', type=int, default=50, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--recovery-interval', type=int, default=1000, metavar='N', help='how many batches to wait before writing recovery checkpoint') parser.add_argument('-j', '--workers', type=int, default=4, metavar='N', help='how many training processes to use (default: 1)') parser.add_argument('--num-gpu', type=int, default=1, help='Number of GPUS to use') parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH', help='path to init checkpoint (default: none)') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--save-images', action='store_true', default=False, help='save images of input bathes every log interval for debugging') parser.add_argument('--output', default='', type=str, metavar='PATH', help='path to output folder (default: none, current dir)') def main(): args = parser.parse_args() if args.output: output_base = args.output else: output_base = './output' exp_name = '-'.join([ datetime.now().strftime("%Y%m%d-%H%M%S"), args.model, str(args.img_size)]) output_dir = get_outdir(output_base, 'train', exp_name) batch_size = args.batch_size torch.manual_seed(args.seed) data_mean, data_std = get_model_meanstd(args.model) dataset_train = Dataset(os.path.join(args.data, 'train')) loader_train = create_loader( dataset_train, img_size=args.img_size, batch_size=batch_size, is_training=True, use_prefetcher=True, random_erasing=0.5, mean=data_mean, std=data_std, num_workers=args.workers, ) dataset_eval = Dataset(os.path.join(args.data, 'validation')) loader_eval = create_loader( dataset_eval, img_size=args.img_size, batch_size=4 * args.batch_size, is_training=False, use_prefetcher=True, mean=data_mean, std=data_std, num_workers=args.workers, ) model = model_factory.create_model( args.model, pretrained=args.pretrained, num_classes=1000, drop_rate=args.drop, global_pool=args.gp, checkpoint_path=args.initial_checkpoint) # optionally resume from a checkpoint start_epoch = 0 if args.start_epoch is None else args.start_epoch optimizer_state = None if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: new_state_dict = OrderedDict() for k, v in checkpoint['state_dict'].items(): if k.startswith('module'): name = k[7:] # remove `module.` else: name = k new_state_dict[name] = v model.load_state_dict(new_state_dict) if 'optimizer' in checkpoint: optimizer_state = checkpoint['optimizer'] print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) start_epoch = checkpoint['epoch'] if args.start_epoch is None else args.start_epoch else: model.load_state_dict(checkpoint) else: print("=> no checkpoint found at '{}'".format(args.resume)) return False if args.num_gpu > 1: model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda() else: model.cuda() train_loss_fn = validate_loss_fn = torch.nn.CrossEntropyLoss().cuda() optimizer = create_optimizer(args, model.parameters()) if optimizer_state is not None: optimizer.load_state_dict(optimizer_state) lr_scheduler, num_epochs = create_scheduler(args, optimizer) print(num_epochs) saver = CheckpointSaver(checkpoint_dir=output_dir) best_loss = None try: for epoch in range(start_epoch, num_epochs): train_metrics = train_epoch( epoch, model, loader_train, optimizer, train_loss_fn, args, lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir) eval_metrics = validate( model, loader_eval, validate_loss_fn, args) if lr_scheduler is not None: lr_scheduler.step(epoch, eval_metrics['eval_loss']) update_summary( epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'), write_header=best_loss is None) # save proper checkpoint with eval metric best_loss = saver.save_checkpoint({ 'epoch': epoch + 1, 'arch': args.model, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict(), 'args': args, }, epoch=epoch + 1, metric=eval_metrics['eval_loss']) except KeyboardInterrupt: pass print('*** Best loss: {0} (epoch {1})'.format(best_loss[1], best_loss[0])) def train_epoch( epoch, model, loader, optimizer, loss_fn, args, lr_scheduler=None, saver=None, output_dir=''): batch_time_m = AverageMeter() data_time_m = AverageMeter() losses_m = AverageMeter() model.train() end = time.time() last_idx = len(loader) - 1 num_updates = epoch * len(loader) for batch_idx, (input, target) in enumerate(loader): last_batch = batch_idx == last_idx data_time_m.update(time.time() - end) output = model(input) loss = loss_fn(output, target) losses_m.update(loss.item(), input.size(0)) optimizer.zero_grad() loss.backward() optimizer.step() torch.cuda.synchronize() num_updates += 1 batch_time_m.update(time.time() - end) if last_batch or batch_idx % args.log_interval == 0: lrl = [param_group['lr'] for param_group in optimizer.param_groups] lr = sum(lrl) / len(lrl) print('Train: {} [{}/{} ({:.0f}%)] ' 'Loss: {loss.val:.6f} ({loss.avg:.4f}) ' 'Time: {batch_time.val:.3f}s, {rate:.3f}/s ' '({batch_time.avg:.3f}s, {rate_avg:.3f}/s) ' 'LR: {lr:.4f} ' 'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format( epoch, batch_idx, len(loader), 100. * batch_idx / last_idx, loss=losses_m, batch_time=batch_time_m, rate=input.size(0) / batch_time_m.val, rate_avg=input.size(0) / batch_time_m.avg, lr=lr, data_time=data_time_m)) if args.save_images: torchvision.utils.save_image( input, os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx), padding=0, normalize=True) if saver is not None and last_batch or (batch_idx + 1) % args.recovery_interval == 0: save_epoch = epoch + 1 if last_batch else epoch saver.save_recovery({ 'epoch': save_epoch, 'arch': args.model, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict(), 'args': args, }, epoch=save_epoch, batch_idx=batch_idx) if lr_scheduler is not None: lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg) end = time.time() return OrderedDict([('train_loss', losses_m.avg)]) def validate(model, loader, loss_fn, args): batch_time_m = AverageMeter() losses_m = AverageMeter() prec1_m = AverageMeter() prec5_m = AverageMeter() model.eval() end = time.time() last_idx = len(loader) - 1 with torch.no_grad(): for batch_idx, (input, target) in enumerate(loader): last_batch = batch_idx == last_idx output = model(input) if isinstance(output, (tuple, list)): output = output[0] # augmentation reduction reduce_factor = args.tta if reduce_factor > 1: output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2) target = target[0:target.size(0):reduce_factor] loss = loss_fn(output, target) prec1, prec5 = accuracy(output, target, topk=(1, 5)) torch.cuda.synchronize() losses_m.update(loss.item(), input.size(0)) prec1_m.update(prec1.item(), output.size(0)) prec5_m.update(prec5.item(), output.size(0)) batch_time_m.update(time.time() - end) end = time.time() if last_batch or batch_idx % args.log_interval == 0: print('Test: [{0}/{1}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) ' 'Loss {loss.val:.4f} ({loss.avg:.4f}) ' 'Prec@1 {top1.val:.4f} ({top1.avg:.4f}) ' 'Prec@5 {top5.val:.4f} ({top5.avg:.4f})'.format( batch_idx, last_idx, batch_time=batch_time_m, loss=losses_m, top1=prec1_m, top5=prec5_m)) metrics = OrderedDict([('eval_loss', losses_m.avg), ('eval_prec1', prec1_m.avg)]) return metrics def create_optimizer(args, parameters): if args.opt.lower() == 'sgd': optimizer = optim.SGD( parameters, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True) elif args.opt.lower() == 'adam': optimizer = optim.Adam( parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps) elif args.opt.lower() == 'nadam': optimizer = Nadam( parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps) elif args.opt.lower() == 'adabound': optimizer = AdaBound( parameters, lr=args.lr / 100, weight_decay=args.weight_decay, eps=args.opt_eps, final_lr=args.lr) elif args.opt.lower() == 'adadelta': optimizer = optim.Adadelta( parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps) elif args.opt.lower() == 'rmsprop': optimizer = optim.RMSprop( parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps, momentum=args.momentum, weight_decay=args.weight_decay) else: assert False and "Invalid optimizer" raise ValueError return optimizer def create_scheduler(args, optimizer): num_epochs = args.epochs if args.sched == 'cosine': lr_scheduler = scheduler.CosineLRScheduler( optimizer, t_initial=num_epochs, t_mul=1.0, lr_min=1e-5, decay_rate=args.decay_rate, warmup_lr_init=1e-4, warmup_t=0, cycle_limit=1, t_in_epochs=True, ) num_epochs = lr_scheduler.get_cycle_length() + 10 elif args.sched == 'tanh': lr_scheduler = scheduler.TanhLRScheduler( optimizer, t_initial=num_epochs, t_mul=1.0, lr_min=1e-5, warmup_lr_init=.001, warmup_t=3, cycle_limit=1, t_in_epochs=True, ) num_epochs = lr_scheduler.get_cycle_length() + 10 else: lr_scheduler = scheduler.StepLRScheduler( optimizer, decay_t=args.decay_epochs, decay_rate=args.decay_rate, ) return lr_scheduler, num_epochs if __name__ == '__main__': main()