def setUp(self):
super(Base, self).setUp()
rtp = data_provider.ReadTableParameters(
shard_paths=[self.recordio_path()],
is_recordio=True,
bp=util.BatchParameters(2, num_threads=1, capacity=2),
is_deterministic=True,
pad_size=51,
crop_size=110 + 24)
self.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.ap = augment.AugmentParameters(offset_standard_deviation=0.1,
multiplier_standard_deviation=0.05,
noise_standard_deviation=0.1)
self.extract_patch_size = 80
self.stride = 8
self.stitch_patch_size = 8
self.bp = util.BatchParameters(4, 1, 4)
self.core_model = functools.partial(fovea.core, 50)
self.add_head = functools.partial(model_util.add_head,
is_residual_conv=True)
self.num_classes = 16
self.shuffle = False
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 parameters() -> controller.GetInputTargetAndPredictedParameters:
"""Creates the network parameters for the given inputs and flags.
Returns:
A GetInputTargetAndPredictedParameters containing network parameters for the
given mode, metric, and other flags.
"""
if FLAGS.metric == METRIC_LOSS:
stride = FLAGS.loss_patch_stride
shuffle = True
else:
if FLAGS.model == MODEL_CONCORDANCE:
stride = CONCORDANCE_STITCH_STRIDE
else:
raise NotImplementedError('Unsupported model: %s' % FLAGS.model)
# Shuffling breaks stitching.
shuffle = False
if FLAGS.mode == MODE_TRAIN:
is_train = True
else:
is_train = False
if FLAGS.model == MODEL_CONCORDANCE:
core_model = functools.partial(concordance.core, base_depth=FLAGS.base_depth, num_gpus=FLAGS.num_gpus)
add_head = functools.partial(model_util.add_head, is_residual_conv=True)
extract_patch_size = CONCORDANCE_EXTRACT_PATCH_SIZE
stitch_patch_size = CONCORDANCE_STITCH_PATCH_SIZE
else:
raise NotImplementedError('Unsupported model: %s' % FLAGS.model)
dp = data_parameters()
if shuffle:
preprocess_num_threads = FLAGS.preprocess_shuffle_batch_num_threads
else:
# Thread racing is an additional source of shuffling, so we can only
# use 1 thread per queue.
preprocess_num_threads = 1
if is_train or FLAGS.metric == METRIC_JITTER_STITCH:
ap = augment.AugmentParameters(FLAGS.augment_offset_std,
FLAGS.augment_multiplier_std,
FLAGS.augment_noise_std)
else:
ap = None
if FLAGS.metric == METRIC_INFER_FULL:
bp = None
else:
bp = util.BatchParameters(FLAGS.preprocess_batch_size,
preprocess_num_threads,
FLAGS.preprocess_batch_capacity)
if FLAGS.loss == LOSS_CROSS_ENTROPY:
loss = util.softmax_cross_entropy
elif FLAGS.loss == LOSS_RANKED_PROBABILITY_SCORE:
loss = util.ranked_probability_score
else:
logging.fatal('Invalid loss: %s', FLAGS.loss)
return controller.GetInputTargetAndPredictedParameters(
dp, ap, extract_patch_size, stride, stitch_patch_size, bp, core_model,
add_head, shuffle, NUM_CLASSES, loss, is_train)
def data_parameters() -> data_provider.DataParameters:
"""Creates the DataParameters."""
if FLAGS.read_pngs:
if FLAGS.mode == MODE_TRAIN or FLAGS.mode == MODE_EVAL_TRAIN:
directory = FLAGS.dataset_train_directory
else:
directory = FLAGS.dataset_eval_directory
if FLAGS.metric == METRIC_LOSS:
crop_size = FLAGS.loss_crop_size
else:
crop_size = FLAGS.stitch_crop_size
io_parameters = data_provider.ReadPNGsParameters(directory, None, None,
crop_size)
else:
# Use an eighth of the dataset for validation.
if FLAGS.mode == MODE_TRAIN or FLAGS.mode == MODE_EVAL_TRAIN:
dataset = [
FLAGS.dataset_pattern % i
for i in range(FLAGS.dataset_num_shards)
if (i % 8 != 0) or FLAGS.train_on_full_dataset
]
else:
dataset = [
FLAGS.dataset_pattern % i
for i in range(FLAGS.dataset_num_shards)
if i % 8 == 0
]
if FLAGS.metric == METRIC_LOSS:
crop_size = FLAGS.loss_crop_size
else:
crop_size = FLAGS.stitch_crop_size
if FLAGS.model == MODEL_CONCORDANCE:
extract_patch_size = CONCORDANCE_EXTRACT_PATCH_SIZE
stitch_patch_size = CONCORDANCE_STITCH_PATCH_SIZE
else:
raise NotImplementedError('Unsupported model: %s' % FLAGS.model)
if FLAGS.mode == MODE_EXPORT:
# Any padding will be done by the C++ caller.
pad_width = 0
else:
pad_width = (extract_patch_size - stitch_patch_size) // 2
io_parameters = data_provider.ReadTableParameters(
dataset,
FLAGS.is_recordio,
util.BatchParameters(FLAGS.data_batch_size,
FLAGS.data_batch_num_threads,
FLAGS.data_batch_capacity),
# Do non-deterministic data fetching, to increase the variety of what we
# see in the visualizer.
False,
pad_width,
crop_size)
z_values = get_z_values()
return data_provider.DataParameters(io_parameters, z_values,
INPUT_CHANNEL_VALUES, TARGET_Z_VALUES,
TARGET_CHANNEL_VALUES)
def setup_stitch(
gitapp: GetInputTargetAndPredictedParameters,
name=None,
) -> Dict[str, lt.LabeledTensor]:
"""Creates diagnostic images.
All diagnostic images are registered as summaries.
Args:
gitapp: GetInputTargetAndPredictedParameters.
name: Optional op name.
Returns:
A mapping where the keys are names of summary images and the values
are image tensors.
"""
logging.info('Setting up stitch')
with tf.name_scope(name, 'setup_stitch', []) as scope:
(patch_centers, input_lt, target_lt, predict_input_lt,
predict_target_lt) = get_input_target_and_predicted(gitapp)
predicted_size = len(predict_input_lt.axes['row'])
assert predicted_size == len(predict_input_lt.axes['column'])
input_lt = util.crop_center(predicted_size, input_lt)
target_lt = util.crop_center(predicted_size, target_lt)
# For now, we're not handling overlap or missing data.
assert gitapp.stride == predicted_size
if gitapp.bp is not None:
# Rebatch so a single tensor is all the patches in a single image.
[input_lt, target_lt, predict_input_lt,
predict_target_lt] = util.entry_point_batch(
[input_lt, target_lt, predict_input_lt, predict_target_lt],
bp=util.BatchParameters(size=len(patch_centers),
num_threads=1,
capacity=1),
enqueue_many=True,
entry_point_names=[
'input_stitch', 'target_stitch', 'predict_input_stitch',
'predict_target_stitch'
],
name='stitch')
rc = lt.ReshapeCoder(util.CANONICAL_AXIS_ORDER[3:], ['channel'])
input_lt = rc.decode(
ops.patches_to_image(patch_centers, rc.encode(input_lt)))
rc = lt.ReshapeCoder(util.CANONICAL_AXIS_ORDER[3:], ['channel'])
target_lt = rc.decode(
ops.patches_to_image(patch_centers, rc.encode(target_lt)))
rc = lt.ReshapeCoder(util.CANONICAL_PREDICTION_AXIS_ORDER[3:],
['channel'])
predict_input_lt = rc.decode(
ops.patches_to_image(patch_centers, rc.encode(predict_input_lt)))
rc = lt.ReshapeCoder(util.CANONICAL_PREDICTION_AXIS_ORDER[3:],
['channel'])
predict_target_lt = rc.decode(
ops.patches_to_image(patch_centers, rc.encode(predict_target_lt)))
def get_statistics(t: lt.LabeledTensor) -> lt.LabeledTensor:
t = visualize.to_softmax(t)
rc = lt.ReshapeCoder(list(t.axes.keys())[:-1], ['batch'])
return rc.decode(ops.distribution_statistics(rc.encode(t)))
# C++ entry points .
with tf.name_scope(''):
input_lt = lt.identity(input_lt, name='entry_point_stitched_input')
target_lt = lt.identity(target_lt,
name='entry_point_stitched_target')
# The nodes are used purely to export data to C++.
lt.identity(get_statistics(predict_input_lt),
name='entry_point_stitched_predicted_input')
lt.identity(get_statistics(predict_target_lt),
name='entry_point_stitched_predicted_target')
predict_input_lt = visualize.to_softmax(predict_input_lt)
predict_target_lt = visualize.to_softmax(predict_target_lt)
input_summary_lt = visualize.error_panel(input_lt, predict_input_lt)
target_summary_lt = visualize.error_panel(target_lt, predict_target_lt)
if gitapp.bp is not None:
input_summary_lt, target_summary_lt = lt.batch(
[input_summary_lt, target_summary_lt],
# We'll see 3 images in the visualizer.
batch_size=3,
enqueue_many=True,
num_threads=1,
capacity=1,
name='group')
input_summary_lt = lt.identity(input_summary_lt,
name=scope + 'input_error_panel')
target_summary_lt = lt.identity(target_summary_lt,
name=scope + 'target_error_panel')
visualize_op_dict = {}
visualize_op_dict['input'] = input_lt
visualize_op_dict['predict_input'] = predict_input_lt
visualize_op_dict['target'] = target_lt
visualize_op_dict['predict_target'] = predict_target_lt
def summarize(tag, labeled_tensor):
visualize.summarize_image(labeled_tensor,
name=scope + 'summarize/' + tag)
visualize_op_dict[tag] = labeled_tensor
summarize('input_error_panel', input_summary_lt)
summarize('target_error_panel', target_summary_lt)
return visualize_op_dict
def setUp(self):
super(ReadSerializedExampleTest, self).setUp()
self.example_lt = data_provider.read_serialized_example(
[self.recordio_path()], True,
util.BatchParameters(4, num_threads=1, capacity=2), True)