Table Of Contents
Table Of Contents

Source code for mxnet.gluon.data.vision.datasets

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# coding: utf-8
# pylint: disable=
"""Dataset container."""
__all__ = ['MNIST', 'FashionMNIST', 'CIFAR10', 'CIFAR100',
           'ImageRecordDataset', 'ImageFolderDataset']

import os
import gzip
import tarfile
import struct
import warnings
import numpy as np

from .. import dataset
from ...utils import download, check_sha1, _get_repo_file_url
from .... import nd, image, recordio, base


[docs]class MNIST(dataset._DownloadedDataset): """MNIST handwritten digits dataset from http://yann.lecun.com/exdb/mnist Each sample is an image (in 3D NDArray) with shape (28, 28, 1). Parameters ---------- root : str, default $MXNET_HOME/datasets/mnist Path to temp folder for storing data. train : bool, default True Whether to load the training or testing set. transform : function, default None A user defined callback that transforms each sample. For example:: transform=lambda data, label: (data.astype(np.float32)/255, label) """
[docs] def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'mnist'), train=True, transform=None): self._train = train self._train_data = ('train-images-idx3-ubyte.gz', '6c95f4b05d2bf285e1bfb0e7960c31bd3b3f8a7d') self._train_label = ('train-labels-idx1-ubyte.gz', '2a80914081dc54586dbdf242f9805a6b8d2a15fc') self._test_data = ('t10k-images-idx3-ubyte.gz', 'c3a25af1f52dad7f726cce8cacb138654b760d48') self._test_label = ('t10k-labels-idx1-ubyte.gz', '763e7fa3757d93b0cdec073cef058b2004252c17') self._namespace = 'mnist' super(MNIST, self).__init__(root, transform)
def _get_data(self): if self._train: data, label = self._train_data, self._train_label else: data, label = self._test_data, self._test_label namespace = 'gluon/dataset/'+self._namespace data_file = download(_get_repo_file_url(namespace, data[0]), path=self._root, sha1_hash=data[1]) label_file = download(_get_repo_file_url(namespace, label[0]), path=self._root, sha1_hash=label[1]) with gzip.open(label_file, 'rb') as fin: struct.unpack(">II", fin.read(8)) label = np.frombuffer(fin.read(), dtype=np.uint8).astype(np.int32) with gzip.open(data_file, 'rb') as fin: struct.unpack(">IIII", fin.read(16)) data = np.frombuffer(fin.read(), dtype=np.uint8) data = data.reshape(len(label), 28, 28, 1) self._data = nd.array(data, dtype=data.dtype) self._label = label
[docs]class FashionMNIST(MNIST): """A dataset of Zalando's article images consisting of fashion products, a drop-in replacement of the original MNIST dataset from https://github.com/zalandoresearch/fashion-mnist Each sample is an image (in 3D NDArray) with shape (28, 28, 1). Parameters ---------- root : str, default $MXNET_HOME/datasets/fashion-mnist' Path to temp folder for storing data. train : bool, default True Whether to load the training or testing set. transform : function, default None A user defined callback that transforms each sample. For example:: transform=lambda data, label: (data.astype(np.float32)/255, label) """
[docs] def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'fashion-mnist'), train=True, transform=None): self._train = train self._train_data = ('train-images-idx3-ubyte.gz', '0cf37b0d40ed5169c6b3aba31069a9770ac9043d') self._train_label = ('train-labels-idx1-ubyte.gz', '236021d52f1e40852b06a4c3008d8de8aef1e40b') self._test_data = ('t10k-images-idx3-ubyte.gz', '626ed6a7c06dd17c0eec72fa3be1740f146a2863') self._test_label = ('t10k-labels-idx1-ubyte.gz', '17f9ab60e7257a1620f4ad76bbbaf857c3920701') self._namespace = 'fashion-mnist' super(MNIST, self).__init__(root, transform) # pylint: disable=bad-super-call
[docs]class CIFAR10(dataset._DownloadedDataset): """CIFAR10 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html Each sample is an image (in 3D NDArray) with shape (32, 32, 3). Parameters ---------- root : str, default $MXNET_HOME/datasets/cifar10 Path to temp folder for storing data. train : bool, default True Whether to load the training or testing set. transform : function, default None A user defined callback that transforms each sample. For example:: transform=lambda data, label: (data.astype(np.float32)/255, label) """
[docs] def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'cifar10'), train=True, transform=None): self._train = train self._archive_file = ('cifar-10-binary.tar.gz', 'fab780a1e191a7eda0f345501ccd62d20f7ed891') self._train_data = [('data_batch_1.bin', 'aadd24acce27caa71bf4b10992e9e7b2d74c2540'), ('data_batch_2.bin', 'c0ba65cce70568cd57b4e03e9ac8d2a5367c1795'), ('data_batch_3.bin', '1dd00a74ab1d17a6e7d73e185b69dbf31242f295'), ('data_batch_4.bin', 'aab85764eb3584312d3c7f65fd2fd016e36a258e'), ('data_batch_5.bin', '26e2849e66a845b7f1e4614ae70f4889ae604628')] self._test_data = [('test_batch.bin', '67eb016db431130d61cd03c7ad570b013799c88c')] self._namespace = 'cifar10' super(CIFAR10, self).__init__(root, transform)
def _read_batch(self, filename): with open(filename, 'rb') as fin: data = np.frombuffer(fin.read(), dtype=np.uint8).reshape(-1, 3072+1) return data[:, 1:].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1), \ data[:, 0].astype(np.int32) def _get_data(self): if any(not os.path.exists(path) or not check_sha1(path, sha1) for path, sha1 in ((os.path.join(self._root, name), sha1) for name, sha1 in self._train_data + self._test_data)): namespace = 'gluon/dataset/'+self._namespace filename = download(_get_repo_file_url(namespace, self._archive_file[0]), path=self._root, sha1_hash=self._archive_file[1]) with tarfile.open(filename) as tar: tar.extractall(self._root) if self._train: data_files = self._train_data else: data_files = self._test_data data, label = zip(*(self._read_batch(os.path.join(self._root, name)) for name, _ in data_files)) data = np.concatenate(data) label = np.concatenate(label) self._data = nd.array(data, dtype=data.dtype) self._label = label
[docs]class CIFAR100(CIFAR10): """CIFAR100 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html Each sample is an image (in 3D NDArray) with shape (32, 32, 3). Parameters ---------- root : str, default $MXNET_HOME/datasets/cifar100 Path to temp folder for storing data. fine_label : bool, default False Whether to load the fine-grained (100 classes) or coarse-grained (20 super-classes) labels. train : bool, default True Whether to load the training or testing set. transform : function, default None A user defined callback that transforms each sample. For example:: transform=lambda data, label: (data.astype(np.float32)/255, label) """
[docs] def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'cifar100'), fine_label=False, train=True, transform=None): self._train = train self._archive_file = ('cifar-100-binary.tar.gz', 'a0bb982c76b83111308126cc779a992fa506b90b') self._train_data = [('train.bin', 'e207cd2e05b73b1393c74c7f5e7bea451d63e08e')] self._test_data = [('test.bin', '8fb6623e830365ff53cf14adec797474f5478006')] self._fine_label = fine_label self._namespace = 'cifar100' super(CIFAR10, self).__init__(root, transform) # pylint: disable=bad-super-call
def _read_batch(self, filename): with open(filename, 'rb') as fin: data = np.frombuffer(fin.read(), dtype=np.uint8).reshape(-1, 3072+2) return data[:, 2:].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1), \ data[:, 0+self._fine_label].astype(np.int32)
[docs]class ImageRecordDataset(dataset.RecordFileDataset): """A dataset wrapping over a RecordIO file containing images. Each sample is an image and its corresponding label. Parameters ---------- filename : str Path to rec file. flag : {0, 1}, default 1 If 0, always convert images to greyscale. \ If 1, always convert images to colored (RGB). transform : function, default None A user defined callback that transforms each sample. For example:: transform=lambda data, label: (data.astype(np.float32)/255, label) """
[docs] def __init__(self, filename, flag=1, transform=None): super(ImageRecordDataset, self).__init__(filename) self._flag = flag self._transform = transform
def __getitem__(self, idx): record = super(ImageRecordDataset, self).__getitem__(idx) header, img = recordio.unpack(record) if self._transform is not None: return self._transform(image.imdecode(img, self._flag), header.label) return image.imdecode(img, self._flag), header.label
[docs]class ImageFolderDataset(dataset.Dataset): """A dataset for loading image files stored in a folder structure. like:: root/car/0001.jpg root/car/xxxa.jpg root/car/yyyb.jpg root/bus/123.jpg root/bus/023.jpg root/bus/wwww.jpg Parameters ---------- root : str Path to root directory. flag : {0, 1}, default 1 If 0, always convert loaded images to greyscale (1 channel). If 1, always convert loaded images to colored (3 channels). transform : callable, default None A function that takes data and label and transforms them:: transform = lambda data, label: (data.astype(np.float32)/255, label) Attributes ---------- synsets : list List of class names. `synsets[i]` is the name for the integer label `i` items : list of tuples List of all images in (filename, label) pairs. """
[docs] def __init__(self, root, flag=1, transform=None): self._root = os.path.expanduser(root) self._flag = flag self._transform = transform self._exts = ['.jpg', '.jpeg', '.png'] self._list_images(self._root)
def _list_images(self, root): self.synsets = [] self.items = [] for folder in sorted(os.listdir(root)): path = os.path.join(root, folder) if not os.path.isdir(path): warnings.warn('Ignoring %s, which is not a directory.'%path, stacklevel=3) continue label = len(self.synsets) self.synsets.append(folder) for filename in sorted(os.listdir(path)): filename = os.path.join(path, filename) ext = os.path.splitext(filename)[1] if ext.lower() not in self._exts: warnings.warn('Ignoring %s of type %s. Only support %s'%( filename, ext, ', '.join(self._exts))) continue self.items.append((filename, label)) def __getitem__(self, idx): img = image.imread(self.items[idx][0], self._flag) label = self.items[idx][1] if self._transform is not None: return self._transform(img, label) return img, label def __len__(self): return len(self.items)