def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True,
mean=None,
stdev=None):
rs = np.random.RandomState()
if mean is None or stdev is None:
mean = 0
std = 1
print("No mean and std given!")
trsfm = [t3d.Normalize(mean, stdev), t3d.ToTensor(True)]
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm)
super(hdf5_3d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)
def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True,
mean=None,
std=None):
if mean is None or std is None:
mean = 0
std = 1
print("No mean and std given!")
#mean = 8.71
#std = 22.02
trsfm_train = transforms.Compose([
transforms.Resize(90),
transforms.RandomHorizontalFlip(),
#transforms.RandomAffine(45, translate=(.05,.05), scale=(0.95,1.05), shear=None, resample=False, fillcolor=0),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize(mean=[mean], std=[std]),
])
trsfm_test = transforms.Compose([
transforms.Resize(90),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize(mean=[mean], std=[std]),
])
'''
means and std for IMPRS dataset
allDAPI_volume 141.42 18.85
mask_avgproj 128.16, 0.54
mask_maxproj 128.51, 1.23
mask_sumproj 126.145, 31.49
mask_volume 128.23, 0.84
'''
if training == True:
trsfm = trsfm_train
else:
trsfm = trsfm_test
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm)
super(hdf5_2d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)
def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True):
rs = np.random.RandomState()
mean = 143.68
stdev = 23.53
trsfm_train = [ #t3d.shotNoise(rs, alpha = 0.7, execution_prob = 0.2),
#t3d.Downsample(rs, factor = 4.0, order=2),
#t3d.RandomFlip(rs),
t3d.RandomRotate90(rs),
#t3d.RandomContrast(rs, factor = 0.8, execution_probability=0.2),
#t3d.ElasticDeformation(rs, 3, alpha=20, sigma=3, execution_probability=0.2),
#t3d.GaussianNoise(rs, 3),
t3d.Normalize(mean, stdev),
t3d.ToTensor(True)
]
trsfm_test = [t3d.Normalize(mean, stdev), t3d.ToTensor(True)]
'''
means and std for IMPRS dataset
allDAPI_volume 141.42 18.85
mask_avgproj 128.16, 0.54
mask_maxproj 128.51, 1.23
mask_sumproj 126.145, 31.49
mask_volume 128.23, 0.84
allDAPI_volume0701 140.61 17.60
'''
if training == True:
trsfm = trsfm_train
else:
trsfm = trsfm_test
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm)
super(hdf5_3d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)
def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True,
dataset=None,
mean=0,
stdev=1):
rs = np.random.RandomState()
trsfm_train = [
t3d.RandomRotate90(rs),
t3d.Normalize(mean, stdev),
t3d.ToTensor(True)
]
trsfm_test = [t3d.Normalize(mean, stdev), t3d.ToTensor(True)]
if training == True:
trsfm = trsfm_train
else:
trsfm = trsfm_test
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
if dataset == None:
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm)
else:
self.dataset = dataset
super(hdf5_3d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)
def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True,
projected=False):
if projected:
rs = 256
cc = 224
else:
rs = 90
cc = 64
trsfm_train = transforms.Compose([
transforms.Resize(rs),
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(5,
translate=(.05, .05),
scale=(0.95, 1.05),
shear=None,
resample=False,
fillcolor=0),
transforms.CenterCrop(cc),
transforms.ToTensor(),
transforms.Normalize(mean=[8.18], std=[19.91]),
])
trsfm_test = transforms.Compose([
transforms.Resize(rs),
transforms.CenterCrop(cc),
transforms.ToTensor(),
transforms.Normalize(mean=[8.18], std=[19.91]),
])
'''
means and std for IMPRS dataset
allDAPI_volume 141.42 18.85
mask_avgproj 128.16, 0.54
mask_maxproj 128.51, 1.23
mask_sumproj 126.145, 31.49
mask_volume 128.23, 0.84
'''
if training == True:
trsfm = trsfm_train
else:
trsfm = trsfm_test
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm,
projection=projected)
super(hdf5_2d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)
def __init__(self,
hdf5_path,
batch_size,
shuffle=True,
shape=[7, 32, 32],
validation_split=0.0,
num_workers=1,
training=True,
mean=None,
stdev=None):
rs = np.random.RandomState()
if mean is None or stdev is None:
mean = 0
std = 1
print("No mean and std given!")
#mean = 15.26
#stdev = 18.21
trsfm_train = [
t3d.shotNoise(rs, alpha=0.8, execution_prob=0.3),
t3d.Downsample(rs, factor=999, order=0, execution_prob=0.2),
t3d.RandomFlip(rs),
t3d.RandomRotate90(rs),
t3d.RandomRotate(rs,
angle_spectrum=35,
axes=[(1, 2)],
mode='constant',
order=0),
t3d.Translate(rs, pixels=8, execution_prob=0.3),
t3d.RandomContrast(rs, factor=0.8, execution_probability=0.3),
#t3d.ElasticDeformation(rs, 3, alpha=20, sigma=3, execution_probability=0.2),
#t3d.GaussianNoise(rs, 3),
t3d.Normalize(mean, stdev),
t3d.ToTensor(True)
]
trsfm_test = [ #t3d.shotNoise(rs, alpha = 0.4, execution_prob = 1.0),
#t3d.Downsample(rs, factor = 4.0, order=0, execution_prob = 1.0),
t3d.Normalize(mean, stdev),
t3d.ToTensor(True)
]
'''
means and std for IMPRS dataset
allDAPI_volume 141.42 18.85
mask_avgproj 128.16, 0.54
mask_maxproj 128.51, 1.23
mask_sumproj 126.145, 31.49
mask_volume 128.23, 0.84
allDAPI_volume0701 140.61 17.60
'''
if training == True:
trsfm = trsfm_train
else:
trsfm = trsfm_test
self.hdf5_path = hdf5_path
self.batch_size = batch_size
self.shape = shape
importlib.reload(
databases
) #used to get load any recent changes from database class
self.dataset = databases.hdf5dataset(hdf5_path,
shape=self.shape,
training=training,
transforms=trsfm)
super(hdf5_3d_dataloader,
self).__init__(self.dataset, batch_size, shuffle,
validation_split, num_workers)