def setUp(self):
super(ErrorBase, self).setUp()
rtp = data_provider.ReadTableParameters([self.recordio_path()], True,
util.BatchParameters(2, 1, 2),
True, 0, 768)
dp = data_provider.DataParameters(rtp, self.input_z_values,
self.input_channel_values,
self.target_z_values,
self.target_channel_values)
_, self.target_lt = data_provider.cropped_input_and_target(dp)
self.target_lt_0 = lt.select(self.target_lt, {
'channel': 'DAPI_CONFOCAL',
'mask': False
})
self.target_lt_1 = lt.select(self.target_lt, {
'channel': 'NEURITE_CONFOCAL',
'mask': False
})
self.target_lt_0 = lt.reshape(self.target_lt_0,
self.target_lt_0.axes.keys()[3:],
['channel'])
self.target_lt_1 = lt.reshape(self.target_lt_1,
self.target_lt_1.axes.keys()[3:],
['channel'])
def setUp(self):
super(Base, self).setUp()
self.input_z_values = [round(v, 4) for v in np.linspace(0.0, 1.0, 13)]
self.input_channel_values = ['BRIGHTFIELD', 'PHASE_CONTRAST', 'DIC']
self.target_z_values = ['MAXPROJECT']
self.target_channel_values = [
'DAPI_CONFOCAL',
'DAPI_WIDEFIELD',
'CELLMASK_CONFOCAL',
'TUJ1_WIDEFIELD',
'NFH_CONFOCAL',
'MAP2_CONFOCAL',
'ISLET_WIDEFIELD',
'DEAD_CONFOCAL',
]
rtp = data_provider.ReadTableParameters([self.recordio_path()], True,
util.BatchParameters(2, 1, 2),
True, 0, 256)
dp = data_provider.DataParameters(rtp, self.input_z_values,
self.input_channel_values,
self.target_z_values,
self.target_channel_values)
# pylint: disable=line-too-long
self.input_lt, self.target_lt = data_provider.cropped_input_and_target(
dp)
# pylint: enable=line-too-long
minception_rtp = data_provider.ReadTableParameters(
[self.recordio_path()], True, util.BatchParameters(2, 1, 2), True,
51, 110)
minception_dp = data_provider.DataParameters(
minception_rtp, self.input_z_values, self.input_channel_values,
self.target_z_values, self.target_channel_values)
# pylint: disable=line-too-long
self.minception_input_lt, self.minception_target_lt = data_provider.cropped_input_and_target(
minception_dp)
def setUp(self):
super(CroppedInputAndTargetTest, self).setUp()
batch_size = 2
rtp = data_provider.ReadTableParameters(
shard_paths=[self.recordio_path()],
is_recordio=True,
bp=util.BatchParameters(batch_size, num_threads=1, capacity=2),
is_deterministic=True,
pad_size=0,
crop_size=512)
dp = data_provider.DataParameters(
rtp,
input_z_values=self.input_z_values,
input_channel_values=self.input_channel_values,
target_z_values=self.target_z_values,
target_channel_values=self.target_channel_values)
self.input_lt, self.target_lt = data_provider.cropped_input_and_target(
dp)
def setUp(self):
super(ErrorPanelTest, self).setUp()
rtp = data_provider.ReadTableParameters([self.recordio_path()], True,
util.BatchParameters(2, 1, 2),
True, 0, 768)
dp = data_provider.DataParameters(rtp, self.input_z_values,
self.input_channel_values,
self.target_z_values,
self.target_channel_values)
_, batch_target_lt = data_provider.cropped_input_and_target(dp)
self.prediction_lt = lt.slice(
lt.select(batch_target_lt, {'mask': False}),
{'batch': slice(0, 1)})
self.prediction_lt = util.onehot(16, self.prediction_lt)
self.target_lt = lt.slice(batch_target_lt, {'batch': slice(1, 2)})
self.error_panel_lt = visualize.error_panel(self.target_lt,
self.prediction_lt)
def provide_preprocessed_data(
dp: data_provider.DataParameters,
ap: Optional[augment.AugmentParameters],
extract_patch_size: int,
stride: int,
name: str = None,
) -> Tuple[np.ndarray, lt.LabeledTensor, lt.LabeledTensor]:
"""Provide preprocessed input and target patches.
Args:
dp: DataParameters.
ap: Optional AugmentParameters.
extract_patch_size: Patch size for patch extraction.
stride: Stride for patch extraction.
name: Optional op name.
Returns:
An array containing patch center locations.
The patches are extracted with padding, so that the stitched model outputs
will form an image the same size as the input.
A tensor with model inputs, possibly jittered and corrupted for data
enrichment.
A tensor with model targets.
"""
with tf.name_scope(name, 'provide_preprocessed_data', []) as scope:
input_lt, target_lt = data_provider.cropped_input_and_target(dp)
visualize.summarize_image(
visualize.canonical_image(input_lt, name=scope + 'input'))
visualize.summarize_image(
visualize.canonical_image(target_lt, name=scope + 'target'))
if ap is not None:
input_lt, target_lt = augment.augment(ap, input_lt, target_lt)
visualize.summarize_image(
visualize.canonical_image(input_lt,
name=scope + 'input_jitter'))
visualize.summarize_image(
visualize.canonical_image(target_lt,
name=scope + 'target_jitter'))
rc = lt.ReshapeCoder(['z', 'channel', 'mask'], ['channel'])
patch_centers, input_lt = ops.extract_patches_single_scale(
extract_patch_size, stride, rc.encode(input_lt))
input_lt = rc.decode(input_lt)
input_lt = lt.reshape(input_lt, ['batch', 'patch_row', 'patch_column'],
['batch'],
name=scope + 'input')
rc = lt.ReshapeCoder(['z', 'channel', 'mask'], ['channel'])
target_lt = rc.decode(
ops.extract_patches_single_scale(extract_patch_size, stride,
rc.encode(target_lt))[1])
target_lt = lt.reshape(target_lt,
['batch', 'patch_row', 'patch_column'],
['batch'],
name=scope + 'target')
return patch_centers, input_lt, target_lt