def main():
# add argumentation
parser = argparse.ArgumentParser(
description='MobileNet_v2_DeepLab_v3 Pytorch Implementation')
parser.add_argument(
'--dataset',
default='cityscapes',
choices=['cityscapes', 'other'],
help='Dataset used in training MobileNet v2+DeepLab v3')
parser.add_argument('--root',
default='./data/cityscapes',
help='Path to your dataset')
parser.add_argument('--epoch',
default=None,
help='Total number of training epoch')
parser.add_argument('--lr', default=None, help='Base learning rate')
parser.add_argument('--pretrain',
default=None,
help='Path to a pre-trained backbone model')
parser.add_argument('--resume_from',
default=None,
help='Path to a checkpoint to resume model')
args = parser.parse_args()
params = Params()
# parse args
if not os.path.exists(args.root):
if params.dataset_root is None:
raise ValueError('ERROR: Root %s not exists!' % args.root)
else:
params.dataset_root = args.root
if args.epoch is not None:
params.num_epoch = args.epoch
if args.lr is not None:
params.base_lr = args.lr
if args.pretrain is not None:
params.pre_trained_from = args.pretrain
if args.resume_from is not None:
params.resume_from = args.resume_from
LOG('Network parameters:')
print_config(params)
# create dataset and transformation
LOG('Creating Dataset and Transformation......')
datasets = create_dataset(params)
LOG('Creation Succeed.\n')
# create model
LOG('Initializing MobileNet and DeepLab......')
net = MobileNetv2_DeepLabv3(params, datasets)
LOG('Model Built.\n')
# let's start to train!
net.Train()
net.Test()
def main():
# add argumentation
parser = argparse.ArgumentParser(description='MobileNet_v2_DeepLab_v3 Pytorch Implementation')
#todo maybe make it work with multiple datasets?
#parser.add_argument('--dataset', default='cityscapes', choices=['cityscapes', 'other'],
# help='Dataset used in training MobileNet v2+DeepLab v3')
parser.add_argument('--root', default='./data/cityscapes', help='Path to your dataset')
parser.add_argument('--epoch', default=None, help='Total number of training epoch')
parser.add_argument('--lr', default=None, help='Base learning rate')
parser.add_argument('--pretrain', default=None, help='Path to a pre-trained backbone model')
parser.add_argument('--resume_from', default=None, help='Path to a checkpoint to resume model')
parser.add_argument('--logdir', default=None, help='Directory to save logs for Tensorboard')
parser.add_argument('--batch_size', default=128, help='Batch size for training')
args = parser.parse_args()
params = Params()
# parse args
if not os.path.exists(args.root):
if params.dataset_root is None:
raise ValueError('ERROR: Root %s doesn\'t exist!' % args.root)
else:
params.dataset_root = args.root
if args.epoch is not None:
params.num_epoch = int(args.epoch)
if args.lr is not None:
params.base_lr = args.lr
if args.pretrain is not None:
params.pre_trained_from = args.pretrain
if args.resume_from is not None:
params.resume_from = args.resume_from
if args.logdir is not None:
params.logdir = args.logdir
params.summary_dir, params.ckpt_dir = create_train_dir(params.logdir)
params.train_batch = int(args.batch_size)
LOG('Network parameters:')
print_config(params)
# create dataset and transformation
LOG('Creating Dataset and Transformation......')
datasets = create_dataset(params)
LOG('Creation Succeed.\n')
# create model
LOG('Initializing MobileNet and DeepLab......')
net = MobileNetv2_DeepLabv3(params, datasets)
LOG('Model Built.\n')
# let's start to train!
net.Train()
net.Test()
def main():
parser = argparse.ArgumentParser(
description='MobileNet_V2 Pytorch Implementation')
parser.add_argument('--dataset',
default='cifar10',
choices=['imagenet', 'cifar10', 'cifar100', 'other'],
help='Dataset used in training MobileNet V2')
parser.add_argument('--root',
default='./data/cifar10',
help='Path to your dataset')
args = parser.parse_args()
# parse args
if args.dataset == 'cifar10':
params = CIFAR10_params()
elif args.dataset == 'cifar100':
params = CIFAR100_params()
else:
params = Params()
params.dataset_root = args.root
if not os.path.exists(args.root):
print('ERROR: Root %s not exists!' % args.root)
exit(1)
""" TEST CODE """
# params = CIFAR100_params
# params.dataset_root = '/home/ubuntu/cifar100'
# create model
print('\nInitializing MobileNet......')
net = MobileNetv2(params)
print('Initialization Done.\n')
# create dataset and transformation
print('Loading Data......')
dataset = create_dataset(params)
print('Data Loaded.\n')
# let's start to train!
net.train_n_epoch(dataset)
transforms.Compose([
RandomResizedCrop(params.image_size, scale=(0.5, 2.0)),
RandomHorizontalFlip(),
ToTensor()
]),
'val':
transforms.Compose([
RandomResizedCrop(params.image_size, scale=(0.5, 2.0)),
ToTensor()
]),
'test':
transforms.Compose([ToTensor()])
}
# file_dir = {p: os.path.join(params.dataset_root, p) for p in phase}
# datasets = {Cityscapes(file_dir[p], mode=p, transforms=transform[p]) for p in phase}
datasets = {
p: Cityscapes(params.dataset_root, mode=p, transforms=transform[p])
for p in phase
}
return datasets
if __name__ == '__main__':
from config import Params
pp = Params()
pp.dataset_root = '/media/ubuntu/disk/cityscapes'
datasets = create_datasets(pp)