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Nimrod Gileadi
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# Copyright 2020 DeepMind Technologies Limited.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Dataset iterators Mnist, ColorMnist."""
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import enum
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import jax.numpy as jnp
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import numpy as np
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import tensorflow as tf
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import tensorflow_datasets as tfds
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from avae import types
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class Dataset(enum.Enum):
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color_mnist = enum.auto()
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class MnistDataIterator(object):
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"""Mnist data iterator class.
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Data is obtained as dataclass, types.LabelledData.
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"""
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def __init__(self, subset: str, batch_size: int):
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"""Class initializer.
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Args:
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subset: Subset of dataset.
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batch_size: Batch size of the returned dataset iterator.
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"""
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datasets = tfds.load('mnist')
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train_dataset = datasets[subset]
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def _map_fun(x):
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return {'data': tf.cast(x['image'], tf.float32) / 255.0,
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'label': x['label']}
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train_dataset = train_dataset.map(_map_fun)
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train_dataset = train_dataset.batch(batch_size,
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drop_remainder=True).repeat()
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self._iterator = iter(tfds.as_numpy(train_dataset))
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self._batch_size = batch_size
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def __iter__(self):
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return self
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def __next__(self) -> types.LabelledData:
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return types.LabelledData(**next(self._iterator))
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class ColorMnistDataIterator(MnistDataIterator):
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"""Color Mnist data iterator.
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Each ColorMnist image is of shape (28, 28, 3). ColorMnist digit can have 7
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different colors by permution of RGB channels (turning on and off RGB
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channels, except for all channels off permutation).
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Data is obtained as dataclass, util_dataclasses.LabelledData.
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"""
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def __next__(self) -> types.LabelledData:
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mnist_batch = next(self._iterator)
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mnist_image = mnist_batch['data']
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# Colors are supported by turning off and on RGB channels.
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# Thus possible colors are
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# [black (excluded), red, green, yellow, blue, magenta, cyan, white]
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# color_id takes value from [1,8)
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color_id = np.random.randint(7, size=self._batch_size) + 1
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red_channel_bool = np.mod(color_id, 2)
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red_channel_bool = jnp.reshape(red_channel_bool, [-1, 1, 1, 1])
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blue_channel_bool = np.floor_divide(color_id, 4)
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blue_channel_bool = jnp.reshape(blue_channel_bool, [-1, 1, 1, 1])
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green_channel_bool = np.mod(np.floor_divide(color_id, 2), 2)
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green_channel_bool = jnp.reshape(green_channel_bool, [-1, 1, 1, 1])
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color_mnist_image = jnp.stack([
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jnp.multiply(red_channel_bool, mnist_image),
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jnp.multiply(blue_channel_bool, mnist_image),
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jnp.multiply(green_channel_bool, mnist_image)], axis=3)
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color_mnist_image = jnp.reshape(color_mnist_image, [-1, 28, 28, 3])
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# Color id takes value [1, 8)
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# Although to make classification code easier `color` label attached to data
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# takes value in [0, 7) (by subtracting 1 from color id)
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return types.LabelledData(
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data=color_mnist_image, label=mnist_batch['label'])
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