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_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)
pipeline += InstanceCentroidFinder.from_config(
self.data_config.instance_cropping,
skeletons=self.data_config.labels.skeletons,
)
pipeline += InstanceCropper.from_config(
self.data_config.instance_cropping)
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(self.optimization_config.shuffle_buffer_size)
pipeline += ImgaugAugmenter.from_config(
self.optimization_config.augmentation_config)
pipeline += Normalizer.from_config(self.data_config.preprocessing)
pipeline += Resizer.from_config(self.data_config.preprocessing)
pipeline += InstanceCentroidFinder.from_config(
self.data_config.instance_cropping,
skeletons=self.data_config.labels.skeletons,
)
pipeline += InstanceCropper.from_config(
self.data_config.instance_cropping)
pipeline += InstanceConfidenceMapGenerator(
sigma=self.instance_confmap_head.sigma,
output_stride=self.instance_confmap_head.output_stride,
all_instances=False,
)
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)
pipeline += Repeater()
else:
pipeline += Repeater()
pipeline += Batcher(batch_size=self.optimization_config.batch_size,
drop_remainder=True)
if self.optimization_config.prefetch:
pipeline += Prefetcher()
return pipeline
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)
pipeline += Resizer.from_config(self.data_config.preprocessing)
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
def make_datagen_results(reader: LabelsReader, cfg: TrainingJobConfig):
cfg = copy.deepcopy(cfg)
output_keys = dict()
if cfg.data.preprocessing.pad_to_stride is None:
cfg.data.preprocessing.pad_to_stride = (
cfg.model.backbone.which_oneof().max_stride)
pipeline = pipelines.Pipeline(reader)
pipeline += Resizer.from_config(cfg.data.preprocessing)
head_config = cfg.model.heads.which_oneof()
if isinstance(head_config, CentroidsHeadConfig):
pipeline += pipelines.InstanceCentroidFinder.from_config(
cfg.data.instance_cropping, skeletons=reader.labels.skeletons)
pipeline += pipelines.MultiConfidenceMapGenerator(
sigma=cfg.model.heads.centroid.sigma,
output_stride=cfg.model.heads.centroid.output_stride,
centroids=True,
)
output_keys["image"] = "image"
output_keys["confmap"] = "centroid_confidence_maps"
elif isinstance(head_config, CenteredInstanceConfmapsHeadConfig):
if cfg.data.instance_cropping.crop_size is None:
cfg.data.instance_cropping.crop_size = find_instance_crop_size(
labels=reader.labels,
padding=cfg.data.instance_cropping.crop_size_detection_padding,
maximum_stride=cfg.model.backbone.which_oneof().max_stride,
)
pipeline += pipelines.InstanceCentroidFinder.from_config(
cfg.data.instance_cropping, skeletons=reader.labels.skeletons)
pipeline += pipelines.InstanceCropper.from_config(
cfg.data.instance_cropping)
pipeline += pipelines.InstanceConfidenceMapGenerator(
sigma=cfg.model.heads.centered_instance.sigma,
output_stride=cfg.model.heads.centered_instance.output_stride,
)
output_keys["image"] = "instance_image"
output_keys["confmap"] = "instance_confidence_maps"
elif isinstance(head_config, MultiInstanceConfig):
output_keys["image"] = "image"
output_keys["confmap"] = "confidence_maps"
output_keys["paf"] = "confidence_maps"
pipeline += pipelines.MultiConfidenceMapGenerator(
sigma=cfg.model.heads.multi_instance.confmaps.sigma,
output_stride=cfg.model.heads.multi_instance.confmaps.
output_stride,
)
pipeline += pipelines.PartAffinityFieldsGenerator(
sigma=cfg.model.heads.multi_instance.pafs.sigma,
output_stride=cfg.model.heads.multi_instance.pafs.output_stride,
skeletons=reader.labels.skeletons,
)
ds = pipeline.make_dataset()
output_lists = defaultdict(list)
i = 0
for example in ds:
for key, from_key in output_keys.items():
output_lists[key].append(example[from_key])
i += 1
if i == MAX_FRAMES_TO_PREVIEW:
break
outputs = dict()
for key in output_lists.keys():
outputs[key] = np.stack(output_lists[key])
return outputs
def make_datagen_results(reader: LabelsReader,
cfg: TrainingJobConfig) -> np.ndarray:
"""
Gets (subset of) raw images used for training.
TODO: Refactor so we can get this data without digging into details of the
the specific pipelines (e.g., key for confmaps depends on head type).
"""
cfg = copy.deepcopy(cfg)
output_keys = dict()
if cfg.data.preprocessing.pad_to_stride is None:
cfg.data.preprocessing.pad_to_stride = (
cfg.model.backbone.which_oneof().max_stride)
pipeline = pipelines.Pipeline(reader)
pipeline += Resizer.from_config(cfg.data.preprocessing)
head_config = cfg.model.heads.which_oneof()
if isinstance(head_config, CentroidsHeadConfig):
pipeline += pipelines.InstanceCentroidFinder.from_config(
cfg.data.instance_cropping, skeletons=reader.labels.skeletons)
pipeline += pipelines.MultiConfidenceMapGenerator(
sigma=cfg.model.heads.centroid.sigma,
output_stride=cfg.model.heads.centroid.output_stride,
centroids=True,
)
output_keys["image"] = "image"
output_keys["confmap"] = "centroid_confidence_maps"
elif isinstance(head_config, CenteredInstanceConfmapsHeadConfig):
if cfg.data.instance_cropping.crop_size is None:
cfg.data.instance_cropping.crop_size = find_instance_crop_size(
labels=reader.labels,
padding=cfg.data.instance_cropping.crop_size_detection_padding,
maximum_stride=cfg.model.backbone.which_oneof().max_stride,
)
pipeline += pipelines.InstanceCentroidFinder.from_config(
cfg.data.instance_cropping, skeletons=reader.labels.skeletons)
pipeline += pipelines.InstanceCropper.from_config(
cfg.data.instance_cropping)
pipeline += pipelines.InstanceConfidenceMapGenerator(
sigma=cfg.model.heads.centered_instance.sigma,
output_stride=cfg.model.heads.centered_instance.output_stride,
)
output_keys["image"] = "instance_image"
output_keys["confmap"] = "instance_confidence_maps"
elif isinstance(head_config, MultiInstanceConfig):
output_keys["image"] = "image"
output_keys["confmap"] = "confidence_maps"
output_keys["paf"] = "part_affinity_fields"
pipeline += pipelines.MultiConfidenceMapGenerator(
sigma=cfg.model.heads.multi_instance.confmaps.sigma,
output_stride=cfg.model.heads.multi_instance.confmaps.
output_stride,
)
pipeline += pipelines.PartAffinityFieldsGenerator(
sigma=cfg.model.heads.multi_instance.pafs.sigma,
output_stride=cfg.model.heads.multi_instance.pafs.output_stride,
skeletons=reader.labels.skeletons,
flatten_channels=True,
)
ds = pipeline.make_dataset()
output_lists = defaultdict(list)
i = 0
for example in ds:
for key, from_key in output_keys.items():
output_lists[key].append(example[from_key])
i += 1
if i == MAX_FRAMES_TO_PREVIEW:
break
outputs = dict()
for key in output_lists.keys():
outputs[key] = np.stack(output_lists[key])
return outputs