def cityPIL_randscalecrop(cached_data_file,
data_dir,
classes,
batch_size,
num_work=6,
scale=(0.5, 2.0),
size=(1024, 512),
scale1=1,
ignore_idx=255):
print("This input size is " + str(size))
if not os.path.isfile(cached_data_file):
dataLoad = ld.LoadData(data_dir, classes, cached_data_file)
data = dataLoad.processData()
if data is None:
print('Error while pickling data. Please check.')
exit(-1)
else:
data = pickle.load(open(cached_data_file, "rb"))
if isinstance(size, tuple):
size = size
else:
size = (size, size)
if isinstance(scale, tuple):
scale = scale
else:
scale = (scale, scale)
train_transforms = pilTransforms.Compose([
pilTransforms.RandomScale(scale=scale),
pilTransforms.RandomCrop(crop_size=size, ignore_idx=ignore_idx),
pilTransforms.RandomFlip(),
pilTransforms.Normalize(scaleIn=scale1)
])
val_transforms = pilTransforms.Compose(
[pilTransforms.Resize(size=size),
pilTransforms.Normalize(scaleIn=1)])
trainLoader = torch.utils.data.DataLoader(myDataLoader.PILDataset(
data['trainIm'],
data['trainAnnot'],
Double=False,
transform=train_transforms),
batch_size=batch_size,
shuffle=True,
num_workers=num_work,
pin_memory=True)
valLoader = torch.utils.data.DataLoader(myDataLoader.PILDataset(
data['valIm'],
data['valAnnot'],
Double=False,
transform=val_transforms),
batch_size=batch_size,
shuffle=False,
num_workers=num_work,
pin_memory=True)
return trainLoader, valLoader, data
def portraitPIL_Doublerandscalecrop(cached_data_file,
data_dir,
classes,
batch_size,
scale=(0.8, 1.0),
size=(1024, 512),
scale1=1,
scale2=2,
ignore_idx=255,
edge=False,
num_work=6,
Augset=True):
print("This input size is " + str(size))
if not os.path.isfile(cached_data_file):
if Augset:
additional_data = []
additional_data.append('/Nukki/baidu_V1/')
additional_data.append('/Nukki/baidu_V2/')
dataLoad = ld.LoadData(data_dir,
classes,
cached_data_file,
additional=additional_data)
data = dataLoad.processDataAug()
else:
dataLoad = ld.LoadData(data_dir, classes, cached_data_file)
data = dataLoad.processData()
if data is None:
print('Error while pickling data. Please check.')
exit(-1)
else:
data = pickle.load(open(cached_data_file, "rb"))
if isinstance(size, tuple):
size = size
else:
size = (size, size)
if isinstance(scale, tuple):
scale = scale
else:
scale = (scale, scale)
train_transforms = pilTransforms.Compose([
# pilTransforms.data_aug_color(),
pilTransforms.RandomScale(scale=scale),
pilTransforms.RandomCrop(crop_size=size, ignore_idx=ignore_idx),
pilTransforms.RandomFlip(),
pilTransforms.DoubleNormalize(scale1=scale1, scale2=scale2)
])
val_transforms = pilTransforms.Compose([
pilTransforms.Resize(size=size),
# pilTransforms.RandomScale(scale=scale),
# pilTransforms.RandomCrop(crop_size=size, ignore_idx=ignore_idx),
# pilTransforms.RandomFlip(),
pilTransforms.DoubleNormalize(scale1=scale2, scale2=1)
])
trainLoader = torch.utils.data.DataLoader(myDataLoader.PILDataset(
data['trainIm'],
data['trainAnnot'],
Double=True,
ignore_idx=ignore_idx,
edge=edge,
transform=train_transforms),
batch_size=batch_size,
shuffle=True,
num_workers=num_work,
pin_memory=True)
valLoader = torch.utils.data.DataLoader(myDataLoader.PILDataset(
data['valIm'],
data['valAnnot'],
Double=True,
ignore_idx=ignore_idx,
edge=True,
transform=val_transforms),
batch_size=batch_size,
shuffle=False,
num_workers=num_work,
pin_memory=True)
return trainLoader, valLoader, data
