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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2022 Intel Corporation
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
#
import tensorflow as tf
from tlt.datasets.tf_dataset import TFDataset
from tlt.datasets.image_classification.image_classification_dataset import ImageClassificationDataset
from downloader.datasets import DataDownloader
class TFDSImageClassificationDataset(ImageClassificationDataset, TFDataset):
"""
An image classification dataset from the TensorFlow datasets catalog
"""
def __init__(self, dataset_dir, dataset_name, split=["train"],
as_supervised=True, shuffle_files=True, seed=None, **kwargs):
"""
Class constructor
"""
if not isinstance(split, list):
raise ValueError("Value of split argument must be a list.")
ImageClassificationDataset.__init__(self, dataset_dir, dataset_name)
self._preprocessed = {}
self._seed = seed
tf.get_logger().setLevel('ERROR')
downloader = DataDownloader(dataset_name, dataset_dir=dataset_dir, catalog='tfds', as_supervised=as_supervised,
shuffle_files=shuffle_files, with_info=True)
data, self._info = downloader.download(split=split)
self._dataset = None
self._train_subset = None
self._validation_subset = None
self._test_subset = None
if len(split) == 1:
self._validation_type = None # Train & evaluate on the whole dataset
self._dataset = data[0]
else:
self._validation_type = 'defined_split' # Defined by user or TFDS
for i, s in enumerate(split):
if s == 'train':
self._train_subset = data[i]
elif s == 'validation':
self._validation_subset = data[i]
elif s == 'test':
self._test_subset = data[i]
self._dataset = data[i] if self._dataset is None else self._dataset.concatenate(data[i])
@property
def class_names(self):
"""Returns the list of class names"""
return self._info.features["label"].names
@property
def info(self):
"""Returns a dictionary of information about the dataset"""
return {'dataset_info': self._info, 'preprocessing_info': self._preprocessed}
@property
def dataset(self):
"""
Returns the framework dataset object (tf.data.Dataset)
"""
return self._dataset
def preprocess(self, image_size, batch_size, add_aug=None, preprocessor=None):
"""
Preprocess the dataset to convert to float32, resize, and batch the images
Args:
image_size (int): desired square image size
batch_size (int): desired batch size
add_aug (None or list[str]): Choice of augmentations (RandomHorizontalandVerticalFlip,
RandomHorizontalFlip, RandomVerticalFlip, RandomZoom, RandomRotation) to
be applied during training
preprocessor (None or preprocess_input function from keras.applications): Should be provided when using
Keras Applications models, which have model-specific preprocessors;
otherwise, use None (the default) to apply generic type conversion and
resizing
Raises:
ValueError: if the dataset is not defined or has already been processed
"""
if self._preprocessed:
raise ValueError("Data has already been preprocessed: {}".format(self._preprocessed))
if not isinstance(batch_size, int) or batch_size < 1:
raise ValueError("batch_size should be a positive integer")
if not isinstance(image_size, int) or image_size < 1:
raise ValueError("image_size should be a positive integer")
if not (self._dataset or self._train_subset or self._validation_subset or self._test_subset):
raise ValueError("Unable to preprocess, because the dataset hasn't been defined.")
if add_aug is not None:
aug_dict = {
'hvflip': tf.keras.layers.RandomFlip("horizontal_and_vertical",
input_shape=(image_size, image_size, 3), seed=self._seed),
'hflip': tf.keras.layers.RandomFlip("horizontal",
input_shape=(image_size, image_size, 3), seed=self._seed),
'vflip': tf.keras.layers.RandomFlip("vertical",
input_shape=(image_size, image_size, 3), seed=self._seed),
'rotate': tf.keras.layers.RandomRotation(0.5, seed=self._seed),
'zoom': tf.keras.layers.RandomZoom(0.3, seed=self._seed)}
aug_list = ['hvflip', 'hflip', 'vflip', 'rotate', 'zoom']
data_augmentation = tf.keras.Sequential()
for option in add_aug:
if option not in aug_list:
raise ValueError("Unsupported augmentation for TensorFlow:{}. \
Supported augmentations are {}".format(option, aug_list))
data_augmentation.add(aug_dict[option])
def preprocess_image(image, label):
if preprocessor is None:
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize_with_pad(image, image_size, image_size)
return (image, label)
# Get the non-None splits
split_list = ['_dataset', '_train_subset', '_validation_subset', '_test_subset']
subsets = [s for s in split_list if getattr(self, s, None)]
for subset in subsets:
setattr(self, subset, getattr(self, subset).map(preprocess_image))
if preprocessor:
setattr(self, subset, getattr(self, subset).map(lambda x, y: (preprocessor(x), y)))
setattr(self, subset, getattr(self, subset).cache())
setattr(self, subset, getattr(self, subset).batch(batch_size))
setattr(self, subset, getattr(self, subset).prefetch(tf.data.AUTOTUNE))
if add_aug is not None and subset in ['_dataset', '_train_subset']:
setattr(self, subset, getattr(self, subset).map(lambda x, y: (data_augmentation(x, training=True), y),
num_parallel_calls=tf.data.AUTOTUNE))
self._preprocessed = {'image_size': image_size, 'batch_size': batch_size}
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