@tensorleap_input_encoder

code_loader.inner_leap_binder.leapbinder_decorators.tensorleap_preprocess.tensorleap_input_encoder

The tensorleap_input_encoder decorates an Input Encoder

@tensorleap_input_encoder(name='image', channel_dim=-1)
def input_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray
    pass

Args
name	(str) with the given name of the input, e.g. image.
channel_dim	(int, optional) The dimension of the channels in the result. Default is -1 (channel last). Example: If return shape of the function is [H,W,3] -> channel_dim=-1 If return shape of the function is [3,H,W] -> channel_dim=1

Returns:

a np.ndarray typed input (without a batch dimension)

Examples

Basic Usage

import numpy as np
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_input_encoder
...
@tensorleap_input_encoder('image')
def input_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray:
    return preprocess.data['images'][idx].astype('float32')

Usage within the full script can be found at Dataset Script.

Guides

Full examples can be found at the Dataset Integration section of the following guides:

• MNIST Guide

• IMDB Guide