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train.py
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train.py
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import os
import argparse
import time
import shutil
import torch
import torch.utils.data as data
import torch.backends.cudnn as cudnn
from torchvision import transforms
from data_loader import get_segmentation_dataset
from models.fast_scnn import get_fast_scnn
from utils.loss import MixSoftmaxCrossEntropyLoss, MixSoftmaxCrossEntropyOHEMLoss
from utils.lr_scheduler import LRScheduler
from utils.metric import SegmentationMetric
def parse_args():
"""Training Options for Segmentation Experiments"""
parser = argparse.ArgumentParser(description='Fast-SCNN on PyTorch')
# model and dataset
parser.add_argument('--model', type=str, default='fast_scnn',
help='model name (default: fast_scnn)')
parser.add_argument('--dataset', type=str, default='citys',
help='dataset name (default: citys)')
parser.add_argument('--base-size', type=int, default=1024,
help='base image size')
parser.add_argument('--crop-size', type=int, default=768,
help='crop image size')
parser.add_argument('--train-split', type=str, default='train',
help='dataset train split (default: train)')
# training hyper params
parser.add_argument('--aux', action='store_true', default=False,
help='Auxiliary loss')
parser.add_argument('--aux-weight', type=float, default=0.4,
help='auxiliary loss weight')
parser.add_argument('--epochs', type=int, default=160, metavar='N',
help='number of epochs to train (default: 100)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--batch-size', type=int, default=2,
metavar='N', help='input batch size for training (default: 12)')
parser.add_argument('--lr', type=float, default=1e-2, metavar='LR',
help='learning rate (default: 1e-2)')
parser.add_argument('--momentum', type=float, default=0.9,
metavar='M', help='momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=1e-4,
metavar='M', help='w-decay (default: 1e-4)')
# checking point
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
parser.add_argument('--save-folder', default='./weights',
help='Directory for saving checkpoint models')
# evaluation only
parser.add_argument('--eval', action='store_true', default=False,
help='evaluation only')
parser.add_argument('--no-val', action='store_true', default=True,
help='skip validation during training')
# the parser
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
args.device = device
print(args)
return args
class Trainer(object):
def __init__(self, args):
self.args = args
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
data_kwargs = {'transform': input_transform, 'base_size': args.base_size, 'crop_size': args.crop_size}
train_dataset = get_segmentation_dataset(args.dataset, split=args.train_split, mode='train', **data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_size=1,
shuffle=False)
# create network
self.model = get_fast_scnn(dataset=args.dataset, aux=args.aux)
if torch.cuda.device_count() > 1:
self.model = torch.nn.DataParallel(self.model, device_ids=[0, 1, 2])
self.model.to(args.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(torch.load(args.resume, map_location=lambda storage, loc: storage))
# create criterion
self.criterion = MixSoftmaxCrossEntropyOHEMLoss(aux=args.aux, aux_weight=args.aux_weight,
ignore_index=-1).to(args.device)
# optimizer
self.optimizer = torch.optim.SGD(self.model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = LRScheduler(mode='poly', base_lr=args.lr, nepochs=args.epochs,
iters_per_epoch=len(self.train_loader), power=0.9)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
def train(self):
cur_iters = 0
start_time = time.time()
for epoch in range(self.args.start_epoch, self.args.epochs):
self.model.train()
for i, (images, targets) in enumerate(self.train_loader):
cur_lr = self.lr_scheduler(cur_iters)
for param_group in self.optimizer.param_groups:
param_group['lr'] = cur_lr
images = images.to(self.args.device)
targets = targets.to(self.args.device)
outputs = self.model(images)
loss = self.criterion(outputs, targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
cur_iters += 1
if cur_iters % 10 == 0:
print('Epoch: [%2d/%2d] Iter [%4d/%4d] || Time: %4.4f sec || lr: %.8f || Loss: %.4f' % (
epoch, args.epochs, i + 1, len(self.train_loader),
time.time() - start_time, cur_lr, loss.item()))
if self.args.no_val:
# save every epoch
save_checkpoint(self.model, self.args, is_best=False)
else:
self.validation(epoch)
save_checkpoint(self.model, self.args, is_best=False)
def validation(self, epoch):
is_best = False
self.metric.reset()
self.model.eval()
for i, (image, target) in enumerate(self.val_loader):
image = image.to(self.args.device)
outputs = self.model(image)
pred = torch.argmax(outputs[0], 1)
pred = pred.cpu().data.numpy()
self.metric.update(pred, target.numpy())
pixAcc, mIoU = self.metric.get()
print('Epoch %d, Sample %d, validation pixAcc: %.3f%%, mIoU: %.3f%%' % (
epoch, i + 1, pixAcc * 100, mIoU * 100))
new_pred = (pixAcc + mIoU) / 2
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
save_checkpoint(self.model, self.args, is_best)
def save_checkpoint(model, args, is_best=False):
"""Save Checkpoint"""
directory = os.path.expanduser(args.save_folder)
if not os.path.exists(directory):
os.makedirs(directory)
filename = '{}_{}.pth'.format(args.model, args.dataset)
save_path = os.path.join(directory, filename)
torch.save(model.state_dict(), save_path)
if is_best:
best_filename = '{}_{}_best_model.pth'.format(args.model, args.dataset)
best_filename = os.path.join(directory, best_filename)
shutil.copyfile(filename, best_filename)
if __name__ == '__main__':
args = parse_args()
trainer = Trainer(args)
if args.eval:
print('Evaluation model: ', args.resume)
trainer.validation(args.start_epoch)
else:
print('Starting Epoch: %d, Total Epochs: %d' % (args.start_epoch, args.epochs))
trainer.train()