🤼 Triko

Simplifies the process of encoding/decoding data using TFRecord framework.

Getting Started

I was a bit overwhelmed after using TFRecord framework for the first time. I don't find its interface very appealing, so the idea was to encapsulate all the nitty-gritty in this library.

Note: I'm not an expert in TFRecord. I just found my approach very helpful in my workflow.

TrikoFeature

For each feature you want to serialize ( images, numbers, strings, labels ), you should use a separate TrikoFeature subclass. Each TrikoFeature subclass must be initialized with a unique key ( see init method ). Those keys are used to serialize data in TFRecord.

TrikoFeature utilizes generic. Each subclass must provide three types for itself.

An abstract example:

class DemoFeature(TrikoFeature[RAW_TYPE, ENCODED_TYPE, DECODED_TYPE])

• RAW_TYPE - an original type of your data that you want to encode
• ENCODED_TYPE - a type your data will be in after encoding (TFRecord supports only a few types)
• DECODED_TYPE - a type your data will be in after decoding

A specific example: Let's say we want to encode an image. We read it, transform it the way we like, and then it's time to serialize it to a TFRecord dataset.

class DemoImageFeature(TrikoFeature[np.ndarray, bytes, np.ndarray])

• np.ndarray ( RAW_TYPE ) - our image data is initially a numpy matrix
• bytes ( ENCODED_TYPE ) - we can't serialize raw numpy arrays using TFRecord ( it won't be a good idea anyway ), so we will convert them to bytes
• np.ndarray ( DECODED_TYPE ) - when reading TFRecord dataset, bytes are useless to us, so we will decoded it back to np.ndarray

How does Triko encode/decode data?

You must tell it how by overriding either _encode_raw or _decode_value methods.

Continuing our example:

class DemoImageFeature(TrikoFeature[np.ndarray, bytes, np.ndarray]):
	def _encode_raw(self, raw_value: np.ndarray) -> bytes:
		# convert numpy array to bytes and return
		pass

	def _decode_value(self, encoded_value: bytes) -> np.ndarray:
		# read bytes and return numpy array
		pass

A simple built-in raw data validation

Before encoding raw data, you can validate its value by overriding _validate_raw_value.

TrikoFeature in action

Encoding

Consider a pseudocode:

with TFRecordWriter as writer:
	# you read an image and perform transformations
	img_array: np.ndarray = ...
	# label for the image
	label: str = ...

	# list of your TrikoFeature subclasses
	features: List[TrikoFeature] = ...

	def raw_value_getter(feature: TrikoFeature) -> Any:
		"""
		Maps a feature to a raw data
		"""

		# 'image' is a key you used for your TrikoFeature subclass
		# that represents an image
		if feature.key == 'image':
			return img_array

		return label

	serialized_features = TrikoFeature.encode_features_to_string(
		features=features, raw_value_getter=raw_value_getter,
	)
	writer.write(serialized_features)

Decoding

Consider a pseudocode:

# list of your TrikoFeature subclasses
features: List[TrikoFeature] = ...

dataset = tf.data.TFRecordDataset().map(TrikoFeature.decoder(features=features))

The lib is cool, but pseudocode is not

See documented real-world example here

Limitations

Only FixedLenFeature are now supported.

Prerequisites

``` python 3.7 tensorflow numpy ```

Installing

``` pip install triko ```