Commit
•
f9e1717
1
Parent(s):
87960e3
Convert dataset to Parquet (#5)
Browse files- Convert dataset to Parquet (72846b13d9300b039c6bbdee948e04736056042a)
- Add 'cropped_digits' config data files (5ee4cad228f7d0894937b5e3020cf85859edc647)
- Delete loading script (f54b2b571d7906c9feb0347a73d0728336af8501)
- README.md +51 -34
- cropped_digits/extra-00000-of-00002.parquet +3 -0
- cropped_digits/extra-00001-of-00002.parquet +3 -0
- cropped_digits/test-00000-of-00001.parquet +3 -0
- cropped_digits/train-00000-of-00001.parquet +3 -0
- full_numbers/extra-00000-of-00004.parquet +3 -0
- full_numbers/extra-00001-of-00004.parquet +3 -0
- full_numbers/extra-00002-of-00004.parquet +3 -0
- full_numbers/extra-00003-of-00004.parquet +3 -0
- full_numbers/test-00000-of-00001.parquet +3 -0
- full_numbers/train-00000-of-00001.parquet +3 -0
- svhn.py +0 -199
README.md
CHANGED
@@ -21,6 +21,36 @@ task_ids: []
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paperswithcode_id: svhn
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pretty_name: Street View House Numbers
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dataset_info:
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- config_name: full_numbers
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features:
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- name: image
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@@ -46,46 +76,33 @@ dataset_info:
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'9': '9'
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splits:
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- name: train
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-
num_bytes:
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num_examples: 33402
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- name: test
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num_bytes:
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num_examples: 13068
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- name: extra
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-
num_bytes:
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num_examples: 202353
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-
download_size:
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dataset_size:
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- config_name: cropped_digits
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'8': '8'
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'9': '9'
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splits:
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- name: train
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num_bytes: 128364360
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num_examples: 73257
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- name: test
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num_bytes: 44464040
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num_examples: 26032
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-
- name: extra
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num_bytes: 967853504
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num_examples: 531131
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-
download_size: 1575594780
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-
dataset_size: 1140681904
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---
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# Dataset Card for Street View House Numbers
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paperswithcode_id: svhn
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pretty_name: Street View House Numbers
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dataset_info:
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+
- config_name: cropped_digits
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features:
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- name: image
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dtype: image
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- name: label
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dtype:
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class_label:
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names:
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'0': '0'
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'1': '1'
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'2': '2'
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'3': '3'
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'4': '4'
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'5': '5'
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'6': '6'
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'7': '7'
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'8': '8'
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'9': '9'
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splits:
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- name: train
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num_bytes: 128062110.875
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num_examples: 73257
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- name: test
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num_bytes: 44356634.0
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num_examples: 26032
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- name: extra
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num_bytes: 965662156.625
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num_examples: 531131
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download_size: 1205637083
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dataset_size: 1138080901.5
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- config_name: full_numbers
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features:
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- name: image
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'9': '9'
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splits:
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- name: train
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num_bytes: 389782132.75
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num_examples: 33402
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- name: test
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num_bytes: 271279491.86
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num_examples: 13068
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- name: extra
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num_bytes: 1864796784.036
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num_examples: 202353
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download_size: 2530154571
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dataset_size: 2525858408.646
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configs:
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- config_name: cropped_digits
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data_files:
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- split: train
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path: cropped_digits/train-*
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- split: test
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path: cropped_digits/test-*
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- split: extra
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path: cropped_digits/extra-*
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- config_name: full_numbers
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data_files:
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- split: train
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path: full_numbers/train-*
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- split: test
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path: full_numbers/test-*
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- split: extra
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path: full_numbers/extra-*
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---
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# Dataset Card for Street View House Numbers
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cropped_digits/extra-00000-of-00002.parquet
ADDED
@@ -0,0 +1,3 @@
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cropped_digits/extra-00001-of-00002.parquet
ADDED
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cropped_digits/test-00000-of-00001.parquet
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cropped_digits/train-00000-of-00001.parquet
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full_numbers/extra-00000-of-00004.parquet
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full_numbers/extra-00002-of-00004.parquet
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full_numbers/extra-00003-of-00004.parquet
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full_numbers/test-00000-of-00001.parquet
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full_numbers/train-00000-of-00001.parquet
ADDED
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svhn.py
DELETED
@@ -1,199 +0,0 @@
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-
# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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-
"""Street View House Numbers (SVHN) dataset."""
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import io
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import os
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import h5py
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import numpy as np
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import scipy.io as sio
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import datasets
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from datasets.tasks import ImageClassification
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@article{netzer2011reading,
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title={Reading digits in natural images with unsupervised feature learning},
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author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
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year={2011}
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}
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"""
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_DESCRIPTION = """\
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-
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting.
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-
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)
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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.
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"""
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-
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_HOMEPAGE = "http://ufldl.stanford.edu/housenumbers/"
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-
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_LICENSE = "Custom (non-commercial)"
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_URLs = {
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"full_numbers": [
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"http://ufldl.stanford.edu/housenumbers/train.tar.gz",
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"http://ufldl.stanford.edu/housenumbers/test.tar.gz",
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"http://ufldl.stanford.edu/housenumbers/extra.tar.gz",
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],
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"cropped_digits": [
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"http://ufldl.stanford.edu/housenumbers/train_32x32.mat",
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"http://ufldl.stanford.edu/housenumbers/test_32x32.mat",
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"http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
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],
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}
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-
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_DIGIT_LABELS = [str(num) for num in range(10)]
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-
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-
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class SVHN(datasets.GeneratorBasedBuilder):
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"""Street View House Numbers (SVHN) dataset."""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="full_numbers",
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version=VERSION,
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description="Contains the original, variable-resolution, color house-number images with character level bounding boxes.",
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),
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datasets.BuilderConfig(
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name="cropped_digits",
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version=VERSION,
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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.",
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),
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]
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-
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def _info(self):
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if self.config.name == "full_numbers":
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features = datasets.Features(
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{
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"image": datasets.Image(),
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"digits": datasets.Sequence(
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{
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"bbox": datasets.Sequence(datasets.Value("int32"), length=4),
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"label": datasets.ClassLabel(num_classes=10),
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}
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),
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}
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)
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else:
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features = datasets.Features(
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{
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"image": datasets.Image(),
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"label": datasets.ClassLabel(num_classes=10),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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task_templates=[ImageClassification(image_column="image", label_column="label")]
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if self.config.name == "cropped_digits"
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else None,
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)
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-
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def _split_generators(self, dl_manager):
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if self.config.name == "full_numbers":
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train_archive, test_archive, extra_archive = dl_manager.download(_URLs[self.config.name])
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for path, f in dl_manager.iter_archive(train_archive):
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if path.endswith("digitStruct.mat"):
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train_annot_data = f.read()
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break
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for path, f in dl_manager.iter_archive(test_archive):
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if path.endswith("digitStruct.mat"):
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test_annot_data = f.read()
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break
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for path, f in dl_manager.iter_archive(extra_archive):
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if path.endswith("digitStruct.mat"):
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extra_annot_data = f.read()
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break
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train_archive = dl_manager.iter_archive(train_archive)
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test_archive = dl_manager.iter_archive(test_archive)
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extra_archive = dl_manager.iter_archive(extra_archive)
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train_filepath, test_filepath, extra_filepath = None, None, None
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else:
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train_annot_data, test_annot_data, extra_annot_data = None, None, None
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train_archive, test_archive, extra_archive = None, None, None
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train_filepath, test_filepath, extra_filepath = dl_manager.download(_URLs[self.config.name])
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return [
|
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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-
gen_kwargs={
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"annot_data": train_annot_data,
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"files": train_archive,
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"filepath": train_filepath,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"annot_data": test_annot_data,
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"files": test_archive,
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"filepath": test_filepath,
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},
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),
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datasets.SplitGenerator(
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name="extra",
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gen_kwargs={
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"annot_data": extra_annot_data,
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"files": extra_archive,
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"filepath": extra_filepath,
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},
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),
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]
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-
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def _generate_examples(self, annot_data, files, filepath):
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if self.config.name == "full_numbers":
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-
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def _get_digits(bboxes, h5_file):
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def key_to_values(key, bbox):
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if bbox[key].shape[0] == 1:
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return [int(bbox[key][0][0])]
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-
else:
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return [int(h5_file[bbox[key][i][0]][()].item()) for i in range(bbox[key].shape[0])]
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-
|
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bbox = h5_file[bboxes[0]]
|
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assert bbox.keys() == {"height", "left", "top", "width", "label"}
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bbox_columns = [key_to_values(key, bbox) for key in ["left", "top", "width", "height", "label"]]
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return [
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{"bbox": [left, top, width, height], "label": label % 10}
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for left, top, width, height, label in zip(*bbox_columns)
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]
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-
|
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with h5py.File(io.BytesIO(annot_data), "r") as h5_file:
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for path, f in files:
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root, ext = os.path.splitext(path)
|
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-
if ext != ".png":
|
187 |
-
continue
|
188 |
-
img_idx = int(os.path.basename(root)) - 1
|
189 |
-
yield img_idx, {
|
190 |
-
"image": {"path": path, "bytes": f.read()},
|
191 |
-
"digits": _get_digits(h5_file["digitStruct/bbox"][img_idx], h5_file),
|
192 |
-
}
|
193 |
-
else:
|
194 |
-
data = sio.loadmat(filepath)
|
195 |
-
for i, (image_array, label) in enumerate(zip(np.rollaxis(data["X"], -1), data["y"])):
|
196 |
-
yield i, {
|
197 |
-
"image": image_array,
|
198 |
-
"label": label.item() % 10,
|
199 |
-
}
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