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):
"""
.. 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 randomize_datasets(self, datasets):
center_shift = np.array(self._window_shape) / 2. - 0.5
tform_center = skimage.transform.SimilarityTransform(
translation=-center_shift)
tform_uncenter = skimage.transform.SimilarityTransform(
translation=center_shift)
if self._preprocess is not None:
pipeline = preprocessing.Pipeline()
#window the rotations to get rid of the uniform background
if self._central_window_shape is not None:
print 'adding window'
pipeline.items.append(CentralWindow(
self._central_window_shape))
for item in self._preprocess:
pipeline.items.append(item)
im_shape = (self._window_shape[0], self._window_shape[1], 1)
for d_idx, dataset in enumerate(datasets):
data = self._original[dataset]
#randomly window data
print data.shape
arr = np.empty((data.shape[0], self._window_shape[0],
self._window_shape[1], data.shape[3]),
dtype=np.float32)
for idx, example in enumerate(data):
scale_x = np.random.uniform(1 - self._scale_diff,
1 + self._scale_diff)
scale_y = np.random.uniform(1 - self._scale_diff,
1 + self._scale_diff)
translation_x = np.random.uniform(1 - self._translation,
1 + self._translation)
translation_y = np.random.uniform(1 - self._translation,
1 + self._translation)
shear = np.random.uniform(0. - self._shear, 0. + self._shear)
rotation = np.random.uniform(0, 360)
tform = AffineTransform(scale=(scale_x, scale_y),
rotation=np.deg2rad(rotation),
translation=(translation_x,
translation_y),
shear=shear)
tform = tform_center + tform + tform_uncenter
img = warp(example, tform, output_shape=self._window_shape)
arr[idx] = img
dataset.set_topological_view(arr, axes=dataset.view_converter.axes)
#assumes self._randomize in in order of [train, valid/test]
if self._preprocess is not None:
can_fit = True
if d_idx == 1:
can_fit = False
dataset.apply_preprocessor(preprocessor=pipeline,
can_fit=can_fit)
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)