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cifar100.py
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cifar100.py
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# Copyright 2019, The TensorFlow Federated 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.
"""Libraries for the federated CIFAR-100 dataset for simulation."""
import collections
import tensorflow as tf
from tensorflow_federated.python.simulation.datasets import download
from tensorflow_federated.python.simulation.datasets import from_tensor_slices_client_data
from tensorflow_federated.python.simulation.datasets import sql_client_data
def _add_proto_parsing(dataset: tf.data.Dataset) -> tf.data.Dataset:
"""Add parsing of the tf.Example proto to the dataset pipeline."""
def parse_proto(tensor_proto):
parse_spec = {
'coarse_label': tf.io.FixedLenFeature(shape=(), dtype=tf.int64),
'image': tf.io.FixedLenFeature(shape=(32, 32, 3), dtype=tf.int64),
'label': tf.io.FixedLenFeature(shape=(), dtype=tf.int64),
}
parsed_features = tf.io.parse_example(tensor_proto, parse_spec)
return collections.OrderedDict(
coarse_label=parsed_features['coarse_label'],
image=tf.cast(parsed_features['image'], tf.uint8),
label=parsed_features['label'],
)
return dataset.map(parse_proto, num_parallel_calls=tf.data.AUTOTUNE)
def load_data(cache_dir=None):
"""Loads a federated version of the CIFAR-100 dataset.
The dataset is downloaded and cached locally. If previously downloaded, it
tries to load the dataset from cache.
The dataset is derived from the [CIFAR-100
dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The training and
testing examples are partitioned across 500 and 100 clients (respectively).
No clients share any data samples, so it is a true partition of CIFAR-100. The
train clients have string client IDs in the range [0-499], while the test
clients have string client IDs in the range [0-99]. The train clients form a
true partition of the CIFAR-100 training split, while the test clients form a
true partition of the CIFAR-100 testing split.
The data partitioning is done using a hierarchical Latent Dirichlet Allocation
(LDA) process, referred to as the [Pachinko Allocation Method]
(https://people.cs.umass.edu/~mccallum/papers/pam-icml06.pdf) (PAM).
This method uses a two-stage LDA process, where each client has an associated
multinomial distribution over the coarse labels of CIFAR-100, and a
coarse-to-fine label multinomial distribution for that coarse label over the
labels under that coarse label. The coarse label multinomial is drawn from a
symmetric Dirichlet with parameter 0.1, and each coarse-to-fine multinomial
distribution is drawn from a symmetric Dirichlet with parameter 10. Each
client has 100 samples. To generate a sample for the client, we first select
a coarse label by drawing from the coarse label multinomial distribution, and
then draw a fine label using the coarse-to-fine multinomial distribution. We
then randomly draw a sample from CIFAR-100 with that label (without
replacement). If this exhausts the set of samples with this label, we
remove the label from the coarse-to-fine multinomial and renormalize the
multinomial distribution.
Data set sizes:
- train: 50,000 examples
- test: 10,000 examples
The `tf.data.Datasets` returned by
`tff.simulation.datasets.ClientData.create_tf_dataset_for_client` will yield
`collections.OrderedDict` objects at each iteration, with the following keys
and values, in lexicographic order by key:
- `'coarse_label'`: a `tf.Tensor` with `dtype=tf.int64` and shape [1] that
corresponds to the coarse label of the associated image. Labels are
in the range [0-19].
- `'image'`: a `tf.Tensor` with `dtype=tf.uint8` and shape [32, 32, 3],
containing the red/blue/green pixels of the image. Each pixel is a value
in the range [0, 255].
- `'label'`: a `tf.Tensor` with `dtype=tf.int64` and shape [1], the class
label of the corresponding image. Labels are in the range [0-99].
Args:
cache_dir: (Optional) directory to cache the downloaded file. If `None`,
caches in Keras' default cache directory.
Returns:
Tuple of (train, test) where the tuple elements are
`tff.simulation.datasets.ClientData` objects.
"""
database_path = download.get_compressed_file(
origin='https://storage.googleapis.com/tff-datasets-public/cifar100.sqlite.lzma',
cache_dir=cache_dir,
)
train_client_data = sql_client_data.SqlClientData(
database_path, 'train'
).preprocess(_add_proto_parsing)
test_client_data = sql_client_data.SqlClientData(
database_path, 'test'
).preprocess(_add_proto_parsing)
return train_client_data, test_client_data
def get_synthetic():
"""Returns a small synthetic dataset for testing.
The two clients produced have exactly 5 examples apiece. The images and
labels are derived from a fixed set of hard-coded images.
Returns:
A `tff.simulation.datasets.ClientData` object that matches the
characteristics (other than size) of those provided by
`tff.simulation.datasets.cifar100.load_data`.
"""
return from_tensor_slices_client_data.TestClientData({
'synthetic1': _get_synthetic_digits_data(),
'synthetic2': _get_synthetic_digits_data(),
})
def _get_synthetic_digits_data():
"""Returns a dictionary suitable for `tf.data.Dataset.from_tensor_slices`.
Returns:
A dictionary that matches the structure of the data produced by
`tff.simulation.datasets.cifar100.load_data`, with keys (in lexicographic
order) `coarse_label`, `image` and `label`.
"""
data = _SYNTHETIC_IMAGE_DATA
images = []
for img_array in data:
reshaped_image = tf.image.resize(img_array, (32, 32))
images.append(tf.cast(reshaped_image, dtype=tf.uint8))
images = tf.stack(images, axis=0)
coarse_labels = tf.constant([4, 4, 4, 8, 10], dtype=tf.int64)
labels = tf.constant([0, 51, 51, 88, 71], dtype=tf.int64)
return collections.OrderedDict(
coarse_label=coarse_labels, image=images, label=labels
)
# This consists of 5 CIFAR-like that have been downsampled to images of shape
# (8, 8, 3), and have been converted to float values. To re-convert to
# CIFAR-like images, we upsample to (32, 32, 3) and recast to `tf.uint8`.
_SYNTHETIC_IMAGE_DATA = [
[
[
[159, 149.5, 109],
[117.75, 123, 116.75],
[132.5, 138, 119.5],
[159, 171.75, 107.25],
[131.75, 143.25, 100.25],
[142.75, 149.5, 133],
[137, 140, 146],
[134.5, 138.25, 143],
],
[
[125.75, 129, 136],
[125.25, 138.25, 67.25],
[164.5, 186.75, 73.75],
[159.25, 185.25, 58.5],
[123.5, 146, 49.5],
[139.75, 170, 37.5],
[174, 200.25, 68.25],
[143, 147.75, 149.5],
],
[
[137, 140.25, 140.75],
[129.25, 151, 24.5],
[163.5, 183.75, 42.75],
[180.75, 204, 60.5],
[187.5, 211.5, 85.75],
[183.5, 210.75, 93.5],
[187.5, 206.5, 70.75],
[158.25, 165, 113.5],
],
[
[104, 109.75, 87],
[113.5, 128.75, 4.75],
[154.25, 173, 27.5],
[171, 195.5, 48.75],
[198, 215.5, 106.25],
[212.25, 226.75, 143.75],
[189.5, 206.25, 69.5],
[163.25, 174.25, 85],
],
[
[47.5, 50.75, 43.5],
[102, 114.75, 3],
[134.75, 154, 18.25],
[155, 176.25, 32],
[176.25, 197.75, 57.25],
[177, 201.25, 61.5],
[182.5, 203.25, 58.5],
[150.75, 158.5, 106.25],
],
[
[15, 15.75, 23.5],
[67.5, 77.5, 5.5],
[112, 127.75, 3.25],
[146.25, 163.5, 26],
[157.25, 178.75, 36.75],
[166.25, 188, 46.5],
[169, 188, 46.5],
[132.5, 137.75, 129],
],
[
[13.25, 15.75, 21.5],
[66.5, 72, 34],
[84.75, 97, 5.75],
[128.5, 146.25, 17.5],
[135.75, 156.75, 22.75],
[143.75, 165, 31.75],
[134, 144.75, 72.25],
[119.25, 121.75, 124.25],
],
[
[26.5, 29.5, 34.5],
[11.75, 11, 16],
[60, 64, 31.5],
[85.75, 93.5, 38.5],
[84.25, 93.5, 36],
[125.5, 130, 101.5],
[123.75, 124.25, 127.75],
[113.75, 115.75, 116],
],
],
[
[
[97.5, 117.25, 130.25],
[85.25, 78.5, 88.75],
[55.25, 49.75, 57.75],
[42.75, 52, 48.25],
[125.5, 138, 136.75],
[252.5, 252.75, 252.75],
[254.5, 254.5, 254.5],
[254.5, 254.5, 254.5],
],
[
[98.25, 114, 125.5],
[91.75, 82, 92.5],
[95.75, 84.75, 94.25],
[89.25, 75, 86],
[80.75, 76.5, 86.5],
[158.25, 165.25, 160],
[255, 255, 255],
[255, 255, 255],
],
[
[69.25, 74.5, 85.5],
[91.5, 79.25, 89.75],
[112, 96.5, 102.75],
[102.75, 89, 99.75],
[165, 153.5, 159.25],
[240, 240.25, 240.75],
[255, 255, 255],
[255, 255, 255],
],
[
[52.25, 86.5, 35.75],
[37.5, 43.25, 38.75],
[10, 9.5, 19.5],
[8.25, 8.75, 19.25],
[11.25, 11.5, 23.75],
[108.75, 110.5, 115],
[252.75, 253.5, 253.5],
[252.5, 253, 252.5],
],
[
[72.75, 107, 70.5],
[47, 90.25, 39.5],
[38, 62.25, 34],
[33.25, 52.25, 34.5],
[94.25, 91.75, 88],
[124.5, 123, 123.5],
[133.75, 147.75, 144],
[63.25, 76.25, 65.75],
],
[
[40.5, 74.75, 36.75],
[73.75, 109, 70],
[60.5, 97.75, 50.5],
[54.25, 97.25, 38.5],
[48.75, 77, 48.75],
[65.5, 95.25, 63],
[105.75, 131.5, 95.25],
[83.75, 124.5, 58],
],
[
[44.5, 90.5, 33.75],
[59, 99, 54.5],
[51.5, 103.75, 32.5],
[63.5, 111, 39.5],
[67.5, 109, 42.75],
[53.25, 92, 44],
[39.75, 80, 28.75],
[52.5, 91, 53.25],
],
[
[32.25, 69.5, 23.25],
[42, 75, 38.75],
[65.5, 106.25, 57.25],
[58, 97.5, 47.5],
[52.5, 77.25, 45],
[61.25, 97, 44.25],
[62.75, 100.75, 55.25],
[46, 92.5, 30.75],
],
],
[
[
[60.25, 49.75, 36],
[72, 78.5, 35.5],
[134.5, 125.75, 91],
[214.5, 196.75, 163.5],
[207.75, 174, 133.5],
[151.75, 125.75, 85.5],
[75.5, 91, 28],
[54.25, 67, 25],
],
[
[54.75, 58.5, 30.5],
[77.75, 64.25, 41.5],
[114, 104.5, 67.25],
[203.25, 183.5, 144],
[229.5, 173.25, 113.75],
[240, 182.5, 109],
[146, 110.5, 61.75],
[75.5, 75.25, 43],
],
[
[88.25, 99, 35.5],
[168, 215, 68],
[158, 194.75, 61],
[160, 137.25, 99.25],
[186.75, 138, 91],
[214.5, 168.75, 110],
[82.5, 65.25, 36.25],
[75.5, 86.25, 36.25],
],
[
[120.5, 152.25, 53],
[112.75, 150.5, 58],
[179.75, 205, 107.75],
[141, 110.5, 76],
[164.25, 117.5, 78],
[152.5, 110.5, 72],
[128.5, 102, 64.25],
[101, 107, 50.5],
],
[
[118.25, 144.25, 56],
[115.75, 130.25, 55],
[76, 71.25, 32.5],
[231, 154.25, 87.25],
[185.25, 127, 73.5],
[140, 101.5, 61.75],
[131.75, 111.75, 61],
[83.5, 87, 44.5],
],
[
[96.25, 103, 56.5],
[153, 155.5, 91],
[92.25, 69.5, 32.75],
[236, 169.75, 109.5],
[178.5, 138.25, 71.75],
[122.25, 101, 59.5],
[139.75, 114.5, 73.5],
[96.5, 79, 51.5],
],
[
[159.5, 210, 90],
[194.75, 244.25, 101.75],
[130, 101.75, 74.75],
[226.25, 148, 81],
[154, 183.5, 86.75],
[120.25, 98, 62],
[119.5, 102.75, 71],
[151, 136.75, 116],
],
[
[111.5, 145.75, 66],
[193, 245.25, 121.25],
[118.75, 111, 65.75],
[149.75, 166, 91.25],
[161, 137.5, 95.25],
[129.25, 123.5, 73.75],
[95.5, 89.5, 60.25],
[140, 137.5, 110.25],
],
],
[
[
[62.75, 95.5, 63.5],
[37.75, 60.75, 32.25],
[78, 91.75, 75.5],
[48.25, 59.5, 47],
[56.25, 55, 41],
[54.25, 61, 43],
[33.25, 53, 45],
[33.25, 40.75, 40.5],
],
[
[48.25, 65.25, 55.25],
[40.25, 55, 42.75],
[87, 76.5, 66.75],
[47.75, 73, 49],
[69.5, 66.5, 60.75],
[144, 144, 134.75],
[32.75, 43.75, 34.25],
[39.5, 48.25, 45],
],
[
[47.75, 59.5, 52.5],
[48, 53.75, 43.75],
[48.25, 48.5, 45.75],
[45, 52.5, 40.5],
[101.5, 82.25, 61],
[137.25, 127.75, 105.5],
[90.5, 93, 81.75],
[34.75, 46, 41.25],
],
[
[87.5, 102.25, 99.25],
[84.75, 94.5, 86.75],
[102.5, 107.5, 110.5],
[128, 116, 96],
[102.5, 82.25, 60],
[124.25, 119, 109.5],
[141.25, 138.75, 110],
[50.75, 65.75, 59],
],
[
[49.25, 60.5, 54.25],
[78.75, 82.75, 78.25],
[99, 108.25, 117.25],
[77.5, 66.5, 50.25],
[124, 108, 88],
[130.25, 138.25, 140],
[158, 145.75, 120.25],
[103, 116.75, 114],
],
[
[101.25, 126.25, 123.25],
[107, 119.5, 124.5],
[138.75, 143.5, 143],
[108, 110, 101.5],
[70.5, 70.25, 61.25],
[142, 124, 88.75],
[134.5, 138.5, 116.75],
[155.25, 181, 181.5],
],
[
[175.5, 199.5, 195.75],
[206.75, 221, 214.75],
[168.5, 183.75, 170],
[166.5, 186.25, 176.5],
[179.5, 189, 185.25],
[141.5, 144.75, 133.75],
[169.25, 190.5, 185],
[165.25, 156.25, 143.25],
],
[
[144.25, 165, 147.5],
[163.25, 183, 163],
[162, 162, 132.75],
[165.5, 151, 127],
[153.5, 138.25, 116.75],
[185.75, 185.5, 166.5],
[148.75, 137, 117.5],
[149.5, 121, 105],
],
],
[
[
[95.5, 103.25, 121],
[127.5, 134.75, 151.75],
[133, 141.75, 161.5],
[97.75, 115.5, 149.25],
[87, 107.75, 148],
[95.75, 112.75, 149],
[88, 107.25, 142.5],
[79.5, 97.5, 128.5],
],
[
[68, 88, 120.75],
[75.25, 100.75, 136.25],
[85.5, 109.5, 148.25],
[96.5, 119.25, 159],
[143.25, 157, 180],
[209.75, 210.75, 217.75],
[177.75, 179, 186.25],
[125.75, 131, 144.75],
],
[
[73.25, 77, 88.75],
[103.25, 102.75, 105.75],
[113.75, 112.5, 116.5],
[133, 129.75, 127.75],
[147.5, 142.5, 137],
[219.25, 219.5, 218.75],
[95.25, 92.25, 95.25],
[56.5, 53.75, 59.5],
],
[
[119.75, 112.25, 95],
[125.5, 110.25, 91],
[109.5, 96.25, 83.5],
[146.5, 125.25, 101.5],
[171, 147, 119],
[185, 164.25, 133.75],
[171, 149, 118],
[136, 122.5, 95.75],
],
[
[52, 49.25, 45.75],
[48, 46.75, 47.25],
[59.5, 55, 54.5],
[83.5, 70.5, 61.75],
[104.25, 83.25, 66.75],
[124.25, 98.75, 75.75],
[79, 65.25, 56],
[60, 51, 47.5],
],
[
[24, 23.5, 22.5],
[25.25, 24.75, 25],
[35.25, 33.5, 31],
[48, 41.25, 34.5],
[156, 118.5, 74.25],
[236.25, 226, 197.25],
[133.75, 96.25, 62.75],
[39.5, 32.75, 27.5],
],
[
[7.75, 7.75, 9.75],
[13, 12, 12.5],
[17.75, 17.75, 15.75],
[34.75, 27.75, 22.25],
[111.75, 76.5, 43.5],
[133.25, 92.75, 52],
[67.25, 45.5, 32.5],
[34.25, 27.25, 23.25],
],
[
[4, 4, 4],
[9, 7, 6.5],
[11.5, 10.5, 8.75],
[18, 14.25, 11],
[35, 23, 14.25],
[60.5, 36, 19],
[30.5, 21, 12.5],
[14.5, 11.75, 6.75],
],
],
]