def transform_test_2():
return Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 228)),
normalize,
#T.CenterCrop((112, 112))
])
def transform_train_optical_flow():
return Compose([
T.transform_optical_flow_raw,
T.Resize((128, 228)),
T.RandomHorizontalFlip(),
T.RandomCrop((112, 112))
])
def transform_train_reference():
return Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 112))
])
def transform_train_3():
return Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 228)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 160))
])
'''
Here we assume video have been resized to value appearing in T.Resize.
'''
import torchvision
import video_yyz.transforms as T
from torchvision import get_video_backend
video_backend = get_video_backend()
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
transform_train = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 228)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 112))
])
transform_test = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 228)), normalize,
T.CenterCrop((112, 112))
])
def transform_test_optical_flow():
return Compose([
T.transform_optical_flow_raw,
T.Resize((128, 228)),
T.CenterCrop((112, 112))
])
def transform_test_1_right():
return Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 228)), normalize,
T.RightCrop((112, 112))
])
def transform_test_reference():
return Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)), normalize,
T.CenterCrop((112, 112))
])
'''
Provide some reference "objects"
Code in this folder should not be "imported". Copy & paste is preferred
until proper abstraction for them are found.
'''
import torchvision
import video_yyz.transforms as T
from torchvision import get_video_backend
video_backend = get_video_backend()
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
transform_train = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 112))
])
transform_test = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)), normalize,
T.CenterCrop((112, 112))
])
