def get_data_loader_list(root,
file_list,
batch_size,
train,
new_size=None,
height=256,
width=128,
num_workers=4,
crop=True):
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
]
transform_list = [transforms.RandomCrop(
(height, width))] + transform_list if crop else transform_list
transform_list = [transforms.Pad(10, padding_mode='edge')
] + transform_list if train else transform_list
transform_list = [transforms.Resize(
(height, width), interpolation=3
)] + transform_list if new_size is not None else transform_list
transform_list = [transforms.RandomHorizontalFlip()
] + transform_list if train else transform_list
transform = transforms.Compose(transform_list)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=num_workers)
return loader
def get_data_loader_list(root,
file_list,
batch_size,
train,
new_size=None,
height=256,
width=256,
num_workers=4,
crop=True):
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
transform_list = [transforms.RandomCrop(
(height, width))] + transform_list if crop else transform_list
transform_list = [transforms.Resize(
new_size)] + transform_list if new_size is not None else transform_list
transform_list = [transforms.RandomHorizontalFlip()
] + transform_list if train else transform_list
transform = transforms.Compose(transform_list)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=train,
drop_last=True,
num_workers=num_workers)
return loader
def get_data_loader_list(root,
file_list,
batch_size,
train,
new_size=None,
height=256,
width=256,
num_workers=4,
crop=True,
horizontal_flip=True,
shuffle=True,
channels=1):
transform = my_transforms(height=height,
width=width,
new_size=new_size,
crop=crop,
horizontal_flip=horizontal_flip,
channels=channels)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
drop_last=True,
num_workers=num_workers)
return loader
def get_dataloader(opt):
if opt.dataroot is None:
raise ValueError(
'`dataroot` parameter is required for dataset \"%s\"' %
opt.dataset)
if opt.dataset == 'folderall':
dataset = ImageFolderAll(root=opt.dataroot,
transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]),
return_paths=not opt.is_train)
nc = 3
elif opt.dataset == 'filelist':
assert opt.datalist != '', 'Please specify `--datalist` if you choose `filelist` dataset mode'
dataset = ImageFilelist(root=opt.dataroot,
flist=opt.datalist,
transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]),
return_paths=not opt.is_train)
nc = 3
elif opt.dataset == 'pairfilelist':
assert opt.datalist != '', 'Please specify `--datalist` if you choose `pairfilelist` dataset mode'
dataset = ImagePairFilelist(root=opt.dataroot,
flist=opt.datalist,
transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]),
transform_matrix=transforms.Compose([
transforms.ToTensor(),
]),
return_paths=not opt.is_train)
nc = 3
else:
raise ValueError('Dataset type is not implemented!')
assert dataset
assert nc > 0
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=opt.batchSize,
drop_last=opt.is_train,
shuffle=opt.is_train,
num_workers=int(opt.workers))
return dataloader, nc
def get_data_loader_list(
root,
file_list,
batch_size,
train,
new_size=None,
height=256,
width=256,
num_workers=4,
crop=True,
):
""" List-based data loader with transformations
(horizontal flip, resizing, random crop, normalization are handled)
Arguments:
root {str} -- path root
file_list {str list} -- list of the file names
batch_size {int} --
train {bool} -- training mode
Keyword Arguments:
new_size {int} -- parameter for resizing (default: {None})
height {int} -- dimension for random cropping (default: {256})
width {int} -- dimension for random cropping (default: {256})
num_workers {int} -- number of workers (default: {4})
crop {bool} -- crop(default: {True})
Returns:
loader -- data loader with transformed dataset
"""
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
transform_list = (
[transforms.RandomCrop((height, width))] + transform_list if crop else transform_list
)
transform_list = (
[transforms.Resize((new_size, new_size))] + transform_list
if new_size is not None
else transform_list
)
transform_list = (
[transforms.RandomHorizontalFlip()] + transform_list if train else transform_list
)
transform = transforms.Compose(transform_list)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=train,
drop_last=True,
num_workers=num_workers,
)
return loader
def get_data_loader_list(root,
file_list,
batch_size,
train,
new_size=None,
height=256,
width=256,
num_workers=4,
crop=True,
datakind=''):
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
transform_list = [transforms.RandomCrop(
(height, width))] + transform_list if crop else transform_list
transform_list = [transforms.Resize(
new_size)] + transform_list if new_size is not None else transform_list
transform_list = [transforms.RandomHorizontalFlip()
] + transform_list if train else transform_list
if datakind == 'selfie2anime':
transform_list = [
transforms.ColorJitter(hue=0.15),
transforms.RandomGrayscale(p=0.25),
transforms.RandomRotation(35),
# transforms.RandomTranslation,
transforms.RandomPerspective(distortion_scale=0.35)
] + transform_list
transform = transforms.Compose(transform_list)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=train,
drop_last=True,
num_workers=num_workers)
return loader
config['gpu']=1
config['trainlist'] = homePath + '/data/WIDER_v0.1/train.lst'
config['testlist'] = homePath + '/data/WIDER_v0.1/test.lst'
config['data_root'] = homePath + '/data'
config['model_name'] = 'twoLinearResnet101.save'
trans = transforms.Compose(
[
transforms.Resize([300,300]),
transforms.RandomCrop([224,224])
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]
)
dataSet = ImageFilelist(root =config['data_root'], flist=config['trainlist'], transform = trans)
testdataSet = ImageFilelist(root =config['data_root'] , flist=config['testlist'], transform = trans)
dataloader = torch.utils.data.DataLoader(dataSet, batch_size = config['batch_size'], shuffle=True,)
testdataloader = torch.utils.data.DataLoader(testdataSet, batch_size = config['batch_size'], shuffle=True,num_workers=8)
cerit = F.cross_entropy
net = TwoLinearModel()
print (net)
net.cuda(config['gpu'])
train_net = nn.ModuleList([net.fc1, net.fc2])
optim = torch.optim.Adam( train_net.parameters(),0.0001, )
optim1 = torch.optim.Adam(nn.ModuleList(list(net.resnet.children())[:-1] ).parameters(),0.000001, )
for epoch in range(25):
# train
net.train()
for idx, (img, label) in enumerate(dataloader):