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# coding: utf-8
# pylint: disable=
"""Dataset container."""
import os
import gzip
import tarfile
import struct
import warnings
import numpy as np
from . import dataset
from ..utils import download, check_sha1
from ... import nd, image, recordio
class _DownloadedDataset(dataset.Dataset):
"""Base class for MNIST, cifar10, etc."""
def __init__(self, root, train, transform):
self._root = os.path.expanduser(root)
self._train = train
self._transform = transform
self._data = None
self._label = None
self._get_data()
def __getitem__(self, idx):
if self._transform is not None:
return self._transform(self._data[idx], self._label[idx])
return self._data[idx], self._label[idx]
def __len__(self):
return len(self._label)
def _get_data(self):
raise NotImplementedError
[docs]class MNIST(_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
Path to temp folder for storing data.
train : bool
Whether to load the training or testing set.
transform : function
A user defined callback that transforms each instance. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root='~/.mxnet/datasets/', train=True,
transform=None):
super(MNIST, self).__init__(root, train, transform)
def _get_data(self):
if not os.path.isdir(self._root):
os.makedirs(self._root)
url = 'http://data.mxnet.io/data/mnist/'
if self._train:
data_file = download(url+'train-images-idx3-ubyte.gz', self._root,
sha1_hash='6c95f4b05d2bf285e1bfb0e7960c31bd3b3f8a7d')
label_file = download(url+'train-labels-idx1-ubyte.gz', self._root,
sha1_hash='2a80914081dc54586dbdf242f9805a6b8d2a15fc')
else:
data_file = download(url+'t10k-images-idx3-ubyte.gz', self._root,
sha1_hash='c3a25af1f52dad7f726cce8cacb138654b760d48')
label_file = download(url+'t10k-labels-idx1-ubyte.gz', self._root,
sha1_hash='763e7fa3757d93b0cdec073cef058b2004252c17')
with gzip.open(label_file, 'rb') as fin:
struct.unpack(">II", fin.read(8))
label = np.fromstring(fin.read(), dtype=np.uint8).astype(np.int32)
with gzip.open(data_file, 'rb') as fin:
struct.unpack(">IIII", fin.read(16))
data = np.fromstring(fin.read(), dtype=np.uint8)
data = data.reshape(len(label), 28, 28, 1)
self._data = [nd.array(x, dtype=x.dtype) for x in data]
self._label = label
[docs]class CIFAR10(_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, 1).
Parameters
----------
root : str
Path to temp folder for storing data.
train : bool
Whether to load the training or testing set.
transform : function
A user defined callback that transforms each instance. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root='~/.mxnet/datasets/', train=True,
transform=None):
self._file_hashes = {'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',
'test_batch.bin': '67eb016db431130d61cd03c7ad570b013799c88c'}
super(CIFAR10, self).__init__(root, train, transform)
def _read_batch(self, filename):
with open(filename, 'rb') as fin:
data = np.fromstring(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 not os.path.isdir(self._root):
os.makedirs(self._root)
file_paths = [(name, os.path.join(self._root, 'cifar-10-batches-bin/', name))
for name in self._file_hashes]
if any(not os.path.exists(path) or not check_sha1(path, self._file_hashes[name])
for name, path in file_paths):
url = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
filename = download(url, self._root,
sha1_hash='e8aa088b9774a44ad217101d2e2569f823d2d491')
with tarfile.open(filename) as tar:
tar.extractall(self._root)
if self._train:
filename = os.path.join(self._root, 'cifar-10-batches-bin/data_batch_%d.bin')
data, label = zip(*[self._read_batch(filename%i) for i in range(1, 6)])
data = np.concatenate(data)
label = np.concatenate(label)
else:
filename = os.path.join(self._root, 'cifar-10-batches-bin/test_batch.bin')
data, label = self._read_batch(filename)
self._data = [nd.array(x, dtype=x.dtype) for x in data]
self._label = label
[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
A user defined callback that transforms each instance. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
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
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.
"""
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_iamges(self._root)
def _list_iamges(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)
