def __init__(self,
tiktorch_net,
filename=None,
HALO_SIZE=32,
BATCH_SIZE=3):
"""
Args:
tiktorch_net (tiktorch): tiktorch object to be loaded into this
classifier object
filename (None, optional): Save file name for future reference
"""
self._filename = filename
if self._filename is None:
self._filename = ""
self.HALO_SIZE = HALO_SIZE
self.BATCH_SIZE = BATCH_SIZE
if tiktorch_net is None:
print(self._filename)
tiktorch_net = TikTorch.unserialize(self._filename)
# print (self._filename)
# assert tiktorch_net.return_hypercolumns == False
# print('blah')
self._tiktorch_net = tiktorch_net
self._opReorderAxes = OpReorderAxes(graph=Graph())
self._opReorderAxes.AxisOrder.setValue("zcyx")
def __init__(self, tiktorch_net, filename=None, HALO_SIZE=32, BATCH_SIZE=3):
"""
Args:
tiktorch_net (tiktorch): tiktorch object to be loaded into this
classifier object
filename (None, optional): Save file name for future reference
"""
self._filename = filename
if self._filename is None:
self._filename = ""
self.HALO_SIZE = HALO_SIZE
self.BATCH_SIZE = BATCH_SIZE
if tiktorch_net is None:
print(self._filename)
tiktorch_net = TikTorch.unserialize(self._filename)
# print (self._filename)
# assert tiktorch_net.return_hypercolumns == False
# print('blah')
self._tiktorch_net = tiktorch_net
self._opReorderAxes = OpReorderAxes(graph=Graph())
self._opReorderAxes.AxisOrder.setValue("zcyx")
def _test_real_serialization(self):
wannabe = TinyConvNet3D()
tik_torch = TikTorch(model=wannabe)
with tempdir() as d:
the_path = '{}/testfile.nn'.format(d)
tik_torch.serialize(to_path=the_path)
new_torch = TikTorch.unserialize(from_path=the_path)
self.assertIsInstance(new_torch, TikTorch)
def _test_gpu_serialization(self):
wannabe = TinyConvNet3D()
tik_torch = TikTorch(model=wannabe)
tik_torch.cuda()
with tempdir() as d:
the_path = "{}/testfile.nn".format(d)
tik_torch.serialize(to_path=the_path)
new_torch = TikTorch.unserialize(from_path=the_path)
self.assertIsInstance(new_torch, TikTorch)
self.assertTrue(new_torch.is_cuda)
def create_and_train_pixelwise(self, feature_images, label_images, axistags=None, feature_names=None):
self._filename = PYTORCH_MODEL_FILE_PATH
logger.debug("Loading pytorch network from {}".format(self._filename))
# Save for future reference
# known_labels = numpy.sort(vigra.analysis.unique(y))
# TODO: check whether loaded network has the same number of classes as specified in ilastik!
self._loaded_pytorch_net = TikTorch.unserialize(self._filename)
logger.info(self.description)
# logger.info("OOB during training: {}".format( oob ))
return TikTorchLazyflowClassifier(self._loaded_pytorch_net, self._filename)
def deserialize_hdf5(cls, h5py_group):
# TODO: load from HDF5 instead of hard coded path!
logger.debug("Deserializing")
# HACK:
# filename = PYTORCH_MODEL_FILE_PATH
filename = h5py_group[cls.HDF5_GROUP_FILENAME]
logger.debug("Deserializing from {}".format(filename))
with tempfile.TemporaryFile() as f:
f.write(h5py_group["classifier"].value)
f.seek(0)
loaded_pytorch_net = TikTorch.unserialize(f)
return TikTorchLazyflowClassifier(loaded_pytorch_net, filename)
def deserialize_hdf5(cls, h5py_group):
# TODO: load from HDF5 instead of hard coded path!
logger.debug("Deserializing")
# HACK:
# filename = PYTORCH_MODEL_FILE_PATH
filename = h5py_group[cls.HDF5_GROUP_FILENAME]
logger.debug("Deserializing from {}".format(filename))
with tempfile.TemporaryFile() as f:
f.write(h5py_group["classifier"].value)
f.seek(0)
loaded_pytorch_net = TikTorch.unserialize(f)
return TikTorchLazyflowClassifier(loaded_pytorch_net, filename)
def create_and_train_pixelwise(self,
feature_images,
label_images,
axistags=None,
feature_names=None):
self._filename = PYTORCH_MODEL_FILE_PATH
logger.debug("Loading pytorch network from {}".format(self._filename))
# Save for future reference
# known_labels = numpy.sort(vigra.analysis.unique(y))
# TODO: check whether loaded network has the same number of classes as specified in ilastik!
self._loaded_pytorch_net = TikTorch.unserialize(self._filename)
logger.info(self.description)
# logger.info("OOB during training: {}".format( oob ))
return TikTorchLazyflowClassifier(self._loaded_pytorch_net,
self._filename)