def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
if not (tuple(data.view_converter.axes) == self.axes):
raise ValueError("Expected axes: %s Actual axes: %s" % (str(data.view_converter.axes), str(self.axes)))
preprocessor.apply(data)
# Do the initial random windowing
randomize_now = self._randomize + self._randomize_once
self._original = dict((data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized)) for data in randomize_now)
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
preprocessor.apply(data)
#
# Do the initial random windowing
#
randomize_now = self._randomize + self._randomize_once
# maps each dataset in randomize_now to a zero-padded topological view
# of its data.
self._original = dict(
(data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized)) for data in randomize_now)
# For each dataset, for each image, extract a randomly positioned and
# potentially horizontal-flipped window
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
preprocessor.apply(data)
#
# Do the initial random windowing
#
randomize_now = self._randomize + self._randomize_once
# maps each dataset in randomize_now to a zero-padded topological view
# of its data.
self._original = dict((data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized))
for data in randomize_now)
# For each dataset, for each image, extract a randomly positioned and
# potentially horizontal-flipped window
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
if not (tuple(data.view_converter.axes) == self.axes):
raise ValueError(
"Expected axes: %s Actual axes: %s" %
(str(data.view_converter.axes), str(self.axes)))
preprocessor.apply(data)
# Do the initial random windowing
randomize_now = self._randomize + self._randomize_once
self._original = dict(
(data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized)) for data in randomize_now)
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._other_datasets:
if not (tuple(data.view_converter.axes) == self.axes):
raise ValueError("Expected axes: %s Actual axes: %s" % (str(data.view_converter.axes), str(self.axes)))
preprocessor.apply(data)
# Do the initial random windowing of the training set.
self._original = dataset.get_topological_view()
self.on_monitor(model, dataset, algorithm)
def setup(self, model, dataset, algorithm):
if self._center_shape is not None:
preprocessor = CentralWindow(self._center_shape)
for data in self._center:
preprocessor.apply(data)
randomize_now = self._randomize + self._randomize_once
self._original = dict(
(data, data.get_topological_view()) for data in randomize_now)
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
if self._center_shape is not None:
preprocessor = CentralWindow(self._center_shape)
for data in self._center:
preprocessor.apply(data)
randomize_now = self._randomize + self._randomize_once
self._original = dict((data,
data.get_topological_view()) for data in randomize_now)
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
preprocessor.apply(data)
# Do the initial random windowing
randomize_now = self._randomize + self._randomize_once
self._original = dict((data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized)) for data in randomize_now)
self.randomize_datasets(randomize_now)
def setup(self, model, dataset, algorithm):
"""
.. todo::
WRITEME
Notes
-----
`dataset` argument is ignored
"""
dataset = None
# Central windowing of auxiliary datasets (e.g. validation sets)
preprocessor = CentralWindow(self._window_shape)
for data in self._center:
preprocessor.apply(data)
# Do the initial random windowing
randomize_now = self._randomize + self._randomize_once
self._original = dict((data,
_zero_pad(data.get_topological_view().astype('float32'),
self._pad_randomized)) for data in randomize_now)
self.randomize_datasets(randomize_now)
