Delete loading script

svhn.py

@@ -1,199 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""Street View House Numbers (SVHN) dataset."""
-
-import io
-import os
-
-import h5py
-import numpy as np
-import scipy.io as sio
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{netzer2011reading,
-  title={Reading digits in natural images with unsupervised feature learning},
-  author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
-  year={2011}
-}
-"""
-
-_DESCRIPTION = """\
-SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting.
-It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images)
-and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
-"""
-
-_HOMEPAGE = "http://ufldl.stanford.edu/housenumbers/"
-
-_LICENSE = "Custom (non-commercial)"
-
-_URLs = {
-    "full_numbers": [
-        "http://ufldl.stanford.edu/housenumbers/train.tar.gz",
-        "http://ufldl.stanford.edu/housenumbers/test.tar.gz",
-        "http://ufldl.stanford.edu/housenumbers/extra.tar.gz",
-    ],
-    "cropped_digits": [
-        "http://ufldl.stanford.edu/housenumbers/train_32x32.mat",
-        "http://ufldl.stanford.edu/housenumbers/test_32x32.mat",
-        "http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
-    ],
-}
-
-_DIGIT_LABELS = [str(num) for num in range(10)]
-
-
-class SVHN(datasets.GeneratorBasedBuilder):
-    """Street View House Numbers (SVHN) dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="full_numbers",
-            version=VERSION,
-            description="Contains the original, variable-resolution, color house-number images with character level bounding boxes.",
-        ),
-        datasets.BuilderConfig(
-            name="cropped_digits",
-            version=VERSION,
-            description="Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest.",
-        ),
-    ]
-
-    def _info(self):
-        if self.config.name == "full_numbers":
-            features = datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "digits": datasets.Sequence(
-                        {
-                            "bbox": datasets.Sequence(datasets.Value("int32"), length=4),
-                            "label": datasets.ClassLabel(num_classes=10),
-                        }
-                    ),
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.ClassLabel(num_classes=10),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="label")]
-            if self.config.name == "cropped_digits"
-            else None,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "full_numbers":
-            train_archive, test_archive, extra_archive = dl_manager.download(_URLs[self.config.name])
-            for path, f in dl_manager.iter_archive(train_archive):
-                if path.endswith("digitStruct.mat"):
-                    train_annot_data = f.read()
-                    break
-            for path, f in dl_manager.iter_archive(test_archive):
-                if path.endswith("digitStruct.mat"):
-                    test_annot_data = f.read()
-                    break
-            for path, f in dl_manager.iter_archive(extra_archive):
-                if path.endswith("digitStruct.mat"):
-                    extra_annot_data = f.read()
-                    break
-            train_archive = dl_manager.iter_archive(train_archive)
-            test_archive = dl_manager.iter_archive(test_archive)
-            extra_archive = dl_manager.iter_archive(extra_archive)
-            train_filepath, test_filepath, extra_filepath = None, None, None
-        else:
-            train_annot_data, test_annot_data, extra_annot_data = None, None, None
-            train_archive, test_archive, extra_archive = None, None, None
-            train_filepath, test_filepath, extra_filepath = dl_manager.download(_URLs[self.config.name])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annot_data": train_annot_data,
-                    "files": train_archive,
-                    "filepath": train_filepath,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "annot_data": test_annot_data,
-                    "files": test_archive,
-                    "filepath": test_filepath,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="extra",
-                gen_kwargs={
-                    "annot_data": extra_annot_data,
-                    "files": extra_archive,
-                    "filepath": extra_filepath,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, annot_data, files, filepath):
-        if self.config.name == "full_numbers":
-
-            def _get_digits(bboxes, h5_file):
-                def key_to_values(key, bbox):
-                    if bbox[key].shape[0] == 1:
-                        return [int(bbox[key][0][0])]
-                    else:
-                        return [int(h5_file[bbox[key][i][0]][()].item()) for i in range(bbox[key].shape[0])]
-
-                bbox = h5_file[bboxes[0]]
-                assert bbox.keys() == {"height", "left", "top", "width", "label"}
-                bbox_columns = [key_to_values(key, bbox) for key in ["left", "top", "width", "height", "label"]]
-                return [
-                    {"bbox": [left, top, width, height], "label": label % 10}
-                    for left, top, width, height, label in zip(*bbox_columns)
-                ]
-
-            with h5py.File(io.BytesIO(annot_data), "r") as h5_file:
-                for path, f in files:
-                    root, ext = os.path.splitext(path)
-                    if ext != ".png":
-                        continue
-                    img_idx = int(os.path.basename(root)) - 1
-                    yield img_idx, {
-                        "image": {"path": path, "bytes": f.read()},
-                        "digits": _get_digits(h5_file["digitStruct/bbox"][img_idx], h5_file),
-                    }
-        else:
-            data = sio.loadmat(filepath)
-            for i, (image_array, label) in enumerate(zip(np.rollaxis(data["X"], -1), data["y"])):
-                yield i, {
-                    "image": image_array,
-                    "label": label.item() % 10,
-                }