Spaces:
Configuration error
Configuration error
File size: 9,141 Bytes
a01ef8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
#!/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
#
from pydoc import locate
from tlt.utils.types import FrameworkType, UseCaseType
dataset_map = {
FrameworkType.TENSORFLOW: {
UseCaseType.IMAGE_CLASSIFICATION: {
"tf_datasets": {"module": "tlt.datasets.image_classification.tfds_image_classification_dataset",
"class": "TFDSImageClassificationDataset"},
"custom": {"module": "tlt.datasets.image_classification.tf_custom_image_classification_dataset",
"class": "TFCustomImageClassificationDataset"}
},
UseCaseType.TEXT_CLASSIFICATION: {
"tf_datasets": {"module": "tlt.datasets.text_classification.tfds_text_classification_dataset",
"class": "TFDSTextClassificationDataset"},
"custom": {"module": "tlt.datasets.text_classification.tf_custom_text_classification_dataset",
"class": "TFCustomTextClassificationDataset"}
}
},
FrameworkType.PYTORCH: {
UseCaseType.IMAGE_CLASSIFICATION: {
"torchvision": {"module": "tlt.datasets.image_classification.torchvision_image_classification_dataset",
"class": "TorchvisionImageClassificationDataset"},
"custom": {"module": "tlt.datasets.image_classification.pytorch_custom_image_classification_dataset",
"class": "PyTorchCustomImageClassificationDataset"}
},
UseCaseType.TEXT_CLASSIFICATION: {
"huggingface": {"module": "tlt.datasets.text_classification.hf_text_classification_dataset",
"class": "HFTextClassificationDataset"},
"custom": {"module": "tlt.datasets.text_classification.hf_custom_text_classification_dataset",
"class": "HFCustomTextClassificationDataset"}
},
UseCaseType.IMAGE_ANOMALY_DETECTION: {
"custom": {"module": "tlt.datasets.image_anomaly_detection.pytorch_custom_image_anomaly_detection_dataset",
"class": "PyTorchCustomImageAnomalyDetectionDataset"}
},
}
}
def load_dataset(dataset_dir: str, use_case: UseCaseType, framework: FrameworkType, dataset_name=None, **kwargs):
"""A factory method for loading a custom dataset.
Image classification datasets expect a directory of images organized with subfolders for each image class, which
can themselves be in split directories named 'train', 'validation', and/or 'test'. Each class subfolder should
contain .jpg images for the class. The name of the subfolder will be used as the class label.
.. code-block:: text
dataset_dir
├── class_a
├── class_b
└── class_c
Or:
.. code-block:: text
dataset_dir
├── train
| ├── class_a
| ├── class_b
| └── class_c
├── validation
| ├── class_a
| ├── class_b
| └── class_c
└── test
├── class_a
├── class_b
└── class_c
Text classification datasets are expected to be a directory with text/csv file with two columns: the label and the
text/sentence to classify. See the TFCustomTextClassificationDataset documentation for a list of the additional
kwargs that are used for loading the a text classification dataset file.
.. code-block:: text
class_a,<text>
class_b,<text>
class_a,<text>
...
Args:
dataset_dir (str): directory containing the dataset
use_case (str or UseCaseType): use case or task the dataset will be used to model
framework (str or FrameworkType): framework
dataset_name (str): optional; name of the dataset used for informational purposes
kwargs: optional; additional keyword arguments depending on the type of dataset being loaded
Returns:
(dataset)
Raises:
NotImplementedError: if the type of dataset being loaded is not supported
Example:
>>> from tlt.datasets.dataset_factory import load_dataset
>>> data = load_dataset('/tmp/data/flower_photos', 'image_classification', 'tensorflow')
Found 3670 files belonging to 5 classes.
>>> data.class_names
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
"""
if not isinstance(framework, FrameworkType):
framework = FrameworkType.from_str(framework)
if not isinstance(use_case, UseCaseType):
use_case = UseCaseType.from_str(use_case)
dataset_catalog = "custom"
if framework in dataset_map.keys():
if use_case in dataset_map[framework].keys():
if dataset_catalog in dataset_map[framework][use_case]:
dataset_class_str = '{}.{}'.format(dataset_map[framework][use_case][dataset_catalog]['module'],
dataset_map[framework][use_case][dataset_catalog]['class'])
dataset_class = locate(dataset_class_str)
if not dataset_class:
raise NotImplementedError("Unable to find the dataset class:", dataset_class_str)
return dataset_class(dataset_dir, dataset_name, **kwargs)
# If no match was found in the map, then it's not implemented yet
raise NotImplementedError("Custom dataset support for {} {} {} has not been implemented yet".format(
str(framework), str(use_case), dataset_catalog))
def get_dataset(dataset_dir: str, use_case: UseCaseType, framework: FrameworkType,
dataset_name: str = None, dataset_catalog: str = None, **kwargs):
"""
A factory method for using a dataset from a catalog.
Args:
dataset_dir (str): directory containing the dataset or to which the dataset should be downloaded
use_case (str or UseCaseType): use case or task the dataset will be used to model
framework (str or FrameworkType): framework
dataset_name (str): optional; name of the dataset
dataset_catalog (str): optional; catalog from which to download the dataset. If a dataset name is
provided and no dataset catalog is given, it will default to use tf_datasets
for a TensorFlow model, torchvision for PyTorch CV models, and huggingface
datasets for PyTorch NLP models or Hugging Face models.
**kwargs: optional; additional keyword arguments for the framework or dataset_catalog
Returns:
(dataset)
Raises:
NotImplementedError: if the dataset requested is not supported yet
Example:
>>> from tlt.datasets.dataset_factory import get_dataset
>>> data = get_dataset('/tmp/data/', 'image_classification', 'tensorflow', 'tf_flowers', 'tf_datasets')
>>> sorted(data.class_names)
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
"""
if not isinstance(framework, FrameworkType):
framework = FrameworkType.from_str(framework)
if not isinstance(use_case, UseCaseType):
use_case = UseCaseType.from_str(use_case)
if dataset_name and not dataset_catalog:
# Try to assume a dataset catalog based on the other information that we have
if framework is FrameworkType.TENSORFLOW:
dataset_catalog = "tf_datasets"
elif framework is FrameworkType.PYTORCH:
if use_case in [UseCaseType.IMAGE_CLASSIFICATION, UseCaseType.OBJECT_DETECTION]:
dataset_catalog = "torchvision"
elif use_case is UseCaseType.TEXT_CLASSIFICATION:
dataset_catalog = "huggingface"
if framework in dataset_map.keys():
if use_case in dataset_map[framework].keys():
if dataset_catalog and dataset_catalog in dataset_map[framework][use_case]:
dataset_class = locate('{}.{}'.format(dataset_map[framework][use_case][dataset_catalog]['module'],
dataset_map[framework][use_case][dataset_catalog]['class']))
return dataset_class(dataset_dir, dataset_name, **kwargs)
# If no match was found in the map, then it's not implemented yet
raise NotImplementedError("Datasets support for {} {} {} has not been implemented yet".format(
str(framework), str(use_case), dataset_catalog))
|