def setUp(self):
super(AugmentTest, self).setUp()
ap = augment.AugmentParameters(0.1, 0.05, 0.1)
self.input_augment_lt, self.target_augment_lt = augment.augment(
ap, self.input_lt, self.target_lt)
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 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)