def portrait_multiCVdataloader(cached_data_file,
data_dir,
classes,
batch_size,
scaleIn,
w=180,
h=320,
edge=False,
num_work=4,
Enc=True,
Augset=True):
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"))
trainDataset_main = cvTransforms.Compose([
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(w, h),
cvTransforms.RandomCropResize(32),
cvTransforms.RandomFlip(),
# myTransforms.RandomCrop(64).
cvTransforms.ToTensor(scaleIn),
#
])
trainDataset_main2 = cvTransforms.Compose([
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(224, 224),
cvTransforms.RandomCropResize(16),
cvTransforms.RandomFlip(),
# myTransforms.RandomCrop(64).
cvTransforms.ToTensor(scaleIn),
#
])
trainDataset_main3 = cvTransforms.Compose([
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(int(w * 0.8), int(h * 0.8)),
cvTransforms.RandomCropResize(24),
cvTransforms.RandomFlip(),
# myTransforms.RandomCrop(64).
cvTransforms.ToTensor(scaleIn),
#
])
valDataset = cvTransforms.Compose([
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(224, 224),
cvTransforms.ToTensor(scaleIn),
#
])
print("This stage is Enc" + str(Enc))
print(" Load public Baidu train dataset")
trainLoader = torch.utils.data.DataLoader(myDataLoader.CVDataset(
data['trainIm'],
data['trainAnnot'],
transform=trainDataset_main,
edge=edge,
Enc=Enc),
batch_size=batch_size,
shuffle=True,
num_workers=num_work,
pin_memory=True)
trainLoader2 = torch.utils.data.DataLoader(
myDataLoader.CVDataset(data['trainIm'],
data['trainAnnot'],
transform=trainDataset_main2,
edge=edge,
Enc=Enc),
batch_size=int(1.5 * batch_size),
shuffle=True,
num_workers=num_work,
pin_memory=True)
trainLoader3 = torch.utils.data.DataLoader(
myDataLoader.CVDataset(data['trainIm'],
data['trainAnnot'],
transform=trainDataset_main3,
edge=edge,
Enc=Enc),
batch_size=int(1.8 * batch_size),
shuffle=True,
num_workers=num_work,
pin_memory=True)
print(" Load public val dataset")
valLoader = torch.utils.data.DataLoader(myDataLoader.CVDataset(
data['valIm'],
data['valAnnot'],
transform=valDataset,
edge=True,
Enc=Enc),
batch_size=batch_size,
shuffle=False,
num_workers=num_work,
pin_memory=True)
return trainLoader, trainLoader2, trainLoader3, valLoader, data
def portrait_CVdataloader(cached_data_file,
data_dir,
classes,
batch_size,
scaleIn,
w=180,
h=320,
edge=False,
num_work=4,
Enc=True,
Augset=True):
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"))
trainDataset_main = cvTransforms.Compose([
cvTransforms.Translation(w, h),
# cvTransforms.data_aug_light(),
cvTransforms.data_aug_color(),
cvTransforms.data_aug_blur(),
cvTransforms.data_aug_noise(),
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(w, h),
cvTransforms.ToTensor(scaleIn),
#
])
valDataset = cvTransforms.Compose([
cvTransforms.Normalize(mean=data['mean'], std=data['std']),
cvTransforms.Scale(w, h),
cvTransforms.ToTensor(scaleIn),
#
])
print("This stage is Enc" + str(Enc))
print(" Load public Baidu train dataset")
trainLoader = torch.utils.data.DataLoader(myDataLoader.CVDataset(
data['trainIm'],
data['trainAnnot'],
transform=trainDataset_main,
edge=edge,
Enc=Enc),
batch_size=batch_size,
shuffle=True,
num_workers=num_work,
pin_memory=True)
print(" Load public val dataset")
valLoader = torch.utils.data.DataLoader(myDataLoader.CVDataset(
data['valIm'],
data['valAnnot'],
transform=valDataset,
edge=True,
Enc=Enc),
batch_size=batch_size,
shuffle=False,
num_workers=num_work,
pin_memory=True)
return trainLoader, valLoader, data
