Module brevettiai.data.tf_types

Classes which are serializable to tuples to allow use in tensorflow datasets

"""
Classes which are serializable to tuples to allow use in tensorflow datasets
"""
from dataclasses import dataclass

import tensorflow as tf


@dataclass(frozen=True, order=True)
class TfRange:
    """
    An object for slicing tensors
    """
    start: int = 0
    end: int = 0

    def slice(self, sequence):
        if self.end == 0:
            return sequence[tf.cast(self.start, tf.int32):]
        else:
            return sequence[tf.cast(self.start, tf.int32):tf.cast(self.end, tf.int32)]

    @classmethod
    def build(cls, x):
        return cls(x[0], x[1])

    @classmethod
    def build_single_frame(cls, frame):
        frame = int(frame)
        return cls(frame, frame+1)

    def __iter__(self):
        yield from (self.start, self.end)

    def __str__(self):
        return f"SequenceRange{self.start, None if self.end == 0 else self.end}"

Classes

class TfRange (start: int = 0, end: int = 0)

An object for slicing tensors

Expand source code

class TfRange:
    """
    An object for slicing tensors
    """
    start: int = 0
    end: int = 0

    def slice(self, sequence):
        if self.end == 0:
            return sequence[tf.cast(self.start, tf.int32):]
        else:
            return sequence[tf.cast(self.start, tf.int32):tf.cast(self.end, tf.int32)]

    @classmethod
    def build(cls, x):
        return cls(x[0], x[1])

    @classmethod
    def build_single_frame(cls, frame):
        frame = int(frame)
        return cls(frame, frame+1)

    def __iter__(self):
        yield from (self.start, self.end)

    def __str__(self):
        return f"SequenceRange{self.start, None if self.end == 0 else self.end}"

Class variables

var end : int

var start : int

Static methods

def build(x)

Expand source code

@classmethod
def build(cls, x):
    return cls(x[0], x[1])

def build_single_frame(frame)

Expand source code

@classmethod
def build_single_frame(cls, frame):
    frame = int(frame)
    return cls(frame, frame+1)

Methods

def slice(self, sequence)

Expand source code

def slice(self, sequence):
    if self.end == 0:
        return sequence[tf.cast(self.start, tf.int32):]
    else:
        return sequence[tf.cast(self.start, tf.int32):tf.cast(self.end, tf.int32)]