'poly_train': True
}
# Path.
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
vis_path = os.path.join(ckpt_path, exp_name, 'log')
check_mkdir(vis_path)
log_path = os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt')
writer = SummaryWriter(log_dir=vis_path, comment=exp_name)
# Transform Data.
joint_transform = joint_transforms.Compose([
joint_transforms.RandomRotate(),
joint_transforms.Resize((args['scale'], args['scale']))
])
val_joint_transform = joint_transforms.Compose(
[joint_transforms.Resize((args['scale'], args['scale']))])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]) # maybe can optimized.
])
target_transform = transforms.ToTensor()
# Prepare Data Set.
train_set = ImageFolder(msd_training_root, joint_transform, img_transform,
target_transform)
print("Train set: {}".format(train_set.__len__()))
train_loader = DataLoader(train_set,
'backbone': 'mobilenet', # 'resnet', 'xception', 'drn', 'mobilenet'],
'out_stride': 16, # 8 or 16
'sync_bn': None, # whether to use sync bn (default: auto)
'freeze_bn': False,
'pre_train': True
}
transform = transforms.Compose([
transforms.ToTensor()
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
to_pil = transforms.ToPILImage()
joint_transform = joint_transforms.Compose([
joint_transforms.Resize((args['img_size_h'], args['img_size_w'])),
#joint_transforms.RandomCrop(args['crop_size']),
joint_transforms.RandomHorizontallyFlip()
])
joint_transform_val = joint_transforms.Compose([
joint_transforms.Resize((args['img_size_h'], args['img_size_w'])),
])
train_set = ImageFolder(train_cuhkshadow_path,
transform=transform,
target_transform=transform,
joint_transform=joint_transform,
is_train=True,
batch_size=args['train_batch_size'])
train_loader = DataLoader(train_set,
