def make_base_pipeline(self, data_provider: Provider) -> Pipeline:
"""Create base pipeline with input data only.
Args:
data_provider: A `Provider` that generates data examples, typically a
`LabelsReader` instance.
Returns:
A `Pipeline` instance configured to produce input examples.
"""
pipeline = Pipeline(providers=data_provider)
pipeline += Normalizer.from_config(self.data_config.preprocessing)
if self.data_config.preprocessing.resize_and_pad_to_target:
pipeline += SizeMatcher.from_config(
config=self.data_config.preprocessing,
provider=data_provider,
)
pipeline += Resizer.from_config(self.data_config.preprocessing)
if self.optimization_config.augmentation_config.random_crop:
pipeline += RandomCropper(
crop_height=self.optimization_config.augmentation_config.
random_crop_height,
crop_width=self.optimization_config.augmentation_config.
random_crop_width,
)
return pipeline
def make_viz_pipeline(self, data_provider: Provider,
keras_model: tf.keras.Model) -> Pipeline:
"""Create visualization pipeline.
Args:
data_provider: A `Provider` that generates data examples, typically a
`LabelsReader` instance.
keras_model: A `tf.keras.Model` that can be used for inference.
Returns:
A `Pipeline` instance configured to fetch data and run inference to generate
predictions useful for visualization during training.
"""
pipeline = self.make_base_pipeline(data_provider=data_provider)
pipeline += Prefetcher()
pipeline += Repeater()
if self.optimization_config.augmentation_config.random_crop:
pipeline += RandomCropper(
crop_height=self.optimization_config.augmentation_config.
random_crop_height,
crop_width=self.optimization_config.augmentation_config.
random_crop_width,
)
pipeline += KerasModelPredictor(
keras_model=keras_model,
model_input_keys="image",
model_output_keys="predicted_centroid_confidence_maps",
)
pipeline += LocalPeakFinder(
confmaps_stride=self.centroid_confmap_head.output_stride,
peak_threshold=0.2,
confmaps_key="predicted_centroid_confidence_maps",
peaks_key="predicted_centroids",
peak_vals_key="predicted_centroid_confidences",
peak_sample_inds_key="predicted_centroid_sample_inds",
peak_channel_inds_key="predicted_centroid_channel_inds",
)
return pipeline
def make_training_pipeline(self, data_provider: Provider) -> Pipeline:
"""Create full training pipeline.
Args:
data_provider: A `Provider` that generates data examples, typically a
`LabelsReader` instance.
Returns:
A `Pipeline` instance configured to produce all data keys required for
training.
Notes:
This does not remap keys to model outputs. Use `KeyMapper` to pull out keys
with the appropriate format for the instantiated `tf.keras.Model`.
"""
pipeline = Pipeline(providers=data_provider)
if self.optimization_config.preload_data:
pipeline += Preloader()
if self.optimization_config.online_shuffling:
pipeline += Shuffler(
shuffle=True,
buffer_size=self.optimization_config.shuffle_buffer_size)
aug_config = self.optimization_config.augmentation_config
if aug_config.random_flip:
pipeline += RandomFlipper.from_skeleton(
self.data_config.labels.skeletons[0],
horizontal=aug_config.flip_horizontal,
)
pipeline += ImgaugAugmenter.from_config(aug_config)
if aug_config.random_crop:
pipeline += RandomCropper(
crop_height=aug_config.random_crop_height,
crop_width=aug_config.random_crop_width,
)
pipeline += Normalizer.from_config(self.data_config.preprocessing)
if self.data_config.preprocessing.resize_and_pad_to_target:
pipeline += SizeMatcher.from_config(
config=self.data_config.preprocessing,
provider=data_provider,
)
pipeline += Resizer.from_config(self.data_config.preprocessing)
pipeline += MultiConfidenceMapGenerator(
sigma=self.confmaps_head.sigma,
output_stride=self.confmaps_head.output_stride,
centroids=False,
with_offsets=self.offsets_head is not None,
offsets_threshold=self.offsets_head.sigma_threshold
if self.offsets_head is not None else 1.0,
)
pipeline += PartAffinityFieldsGenerator(
sigma=self.pafs_head.sigma,
output_stride=self.pafs_head.output_stride,
skeletons=self.data_config.labels.skeletons,
flatten_channels=True,
)
if len(data_provider) >= self.optimization_config.batch_size:
# Batching before repeating is preferred since it preserves epoch boundaries
# such that no sample is repeated within the epoch. But this breaks if there
# are fewer samples than the batch size.
pipeline += Batcher(
batch_size=self.optimization_config.batch_size,
drop_remainder=True,
unrag=True,
)
pipeline += Repeater()
else:
pipeline += Repeater()
pipeline += Batcher(
batch_size=self.optimization_config.batch_size,
drop_remainder=True,
unrag=True,
)
if self.optimization_config.prefetch:
pipeline += Prefetcher()
return pipeline