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| Original file line number | Diff line number | Diff line change |
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| # coding=utf-8 | ||
| # Copyright 2019 The TensorFlow Datasets Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| """MovingMNIST.""" | ||
|
|
||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import numpy as np | ||
| import tensorflow as tf | ||
| import tensorflow_datasets.public_api as tfds | ||
| from tensorflow_datasets.video.moving_sequence import image_as_moving_sequence # pylint: disable=unused-import | ||
|
|
||
| _OUT_RESOLUTION = (64, 64) | ||
| _SEQUENCE_LENGTH = 20 | ||
| _URL = "http://www.cs.toronto.edu/~nitish/unsupervised_video/" | ||
| _CITATION = """\ | ||
| @article{DBLP:journals/corr/SrivastavaMS15, | ||
| author = {Nitish Srivastava and | ||
| Elman Mansimov and | ||
| Ruslan Salakhutdinov}, | ||
| title = {Unsupervised Learning of Video Representations using LSTMs}, | ||
| journal = {CoRR}, | ||
| volume = {abs/1502.04681}, | ||
| year = {2015}, | ||
| url = {http://arxiv.org/abs/1502.04681}, | ||
| archivePrefix = {arXiv}, | ||
| eprint = {1502.04681}, | ||
| timestamp = {Mon, 13 Aug 2018 16:47:05 +0200}, | ||
| biburl = {https://dblp.org/rec/bib/journals/corr/SrivastavaMS15}, | ||
| bibsource = {dblp computer science bibliography, https://dblp.org} | ||
| } | ||
| """ | ||
| _DESCRIPTION = """\ | ||
| Moving variant of MNIST database of handwritten digits. This is the | ||
| data used by the authors for reporting model performance. See | ||
| `tfds.video.moving_mnist.image_as_moving_sequence` | ||
| for generating training/validation data from the MNIST dataset. | ||
| """ | ||
|
|
||
|
|
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| class MovingMnist(tfds.core.GeneratorBasedBuilder): | ||
| """MovingMnist.""" | ||
|
|
||
| VERSION = tfds.core.Version("0.1.0") | ||
|
|
||
| def _info(self): | ||
| return tfds.core.DatasetInfo( | ||
| builder=self, | ||
| description=_DESCRIPTION, | ||
| features=tfds.features.FeaturesDict({ | ||
| "image_sequence": tfds.features.Video( | ||
| shape=(_SEQUENCE_LENGTH,) + _OUT_RESOLUTION + (1,)) | ||
| }), | ||
| urls=[_URL], | ||
| citation=_CITATION, | ||
| ) | ||
|
|
||
| def _split_generators(self, dl_manager): | ||
| data_path = dl_manager.download(_URL + "mnist_test_seq.npy") | ||
|
|
||
| # authors only provide test data. | ||
| # See `tfds.video.moving_mnist.image_as_moving_sequence` for mapping | ||
| # function to create training/validation dataset from MNIST. | ||
| return [ | ||
| tfds.core.SplitGenerator( | ||
| name=tfds.Split.TEST, | ||
| num_shards=5, | ||
| gen_kwargs=dict(data_path=data_path)), | ||
| ] | ||
|
|
||
| def _generate_examples(self, data_path): | ||
| """Generate MovingMnist sequences. | ||
| Args: | ||
| data_path (str): Path to the data file | ||
| Yields: | ||
| 20 x 64 x 64 x 1 uint8 numpy arrays | ||
| """ | ||
| with tf.io.gfile.GFile(data_path, "rb") as fp: | ||
| images = np.load(fp) | ||
| images = np.transpose(images, (1, 0, 2, 3)) | ||
| images = np.expand_dims(images, axis=-1) | ||
| for sequence in images: | ||
| yield dict(image_sequence=sequence) |
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