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pytorch-image-models/train.py

402 lines
15 KiB

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)
dataset_train = Dataset(
os.path.join(args.data, 'train'),
transform=transforms_imagenet_train())
loader_train = data.DataLoader(
dataset_train,
batch_size=batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=fast_collate
)
loader_train = PrefetchLoader(
loader_train, random_erasing=True,
)
dataset_eval = Dataset(
os.path.join(args.data, 'validation'),
transform=transforms_imagenet_eval())
loader_eval = data.DataLoader(
dataset_eval,
batch_size=4 * args.batch_size,
shuffle=False,
num_workers=args.workers,
collate_fn=fast_collate,
)
loader_eval = PrefetchLoader(
loader_eval, random_erasing=False,
)
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()