# -*- coding: utf-8 -*-
from .. import to_cpu
from ..core import *
from .dataset import *
from .transforms import Compose, ToTensor, Normalize
import matplotlib.pyplot as plt
import gzip
[docs]class MNIST(Dataset):
'''MNIST Dataset\n
Args:
normalize (bool): If true, the intensity value of a specific pixel in a specific image will be rescaled from [0, 255] to [0, 1]. Default: True
flatten (bool): If true, data will have a shape of [N, 28*28]. Default: False
Shape:
- data: [N, 1, 28, 28]
'''
def __init__(self, train=True,
transforms=Compose([ToTensor(), Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])]),
target_transforms=None):
super().__init__(train, transforms, target_transforms)
def __len__(self):
if self.train:
return 60000
else:
return 10000
[docs] def state_dict(self):
return {
'label_map': {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9'}
}
[docs] def prepare(self):
url = 'http://yann.lecun.com/exdb/mnist/'
files = {
'train_data.gz':'train-images-idx3-ubyte.gz',
'train_labels.gz':'train-labels-idx1-ubyte.gz',
'test_data.gz':'t10k-images-idx3-ubyte.gz',
'test_labels.gz':'t10k-labels-idx1-ubyte.gz'
}
for filename, value in files.items():
self._download(url+value, filename)
if self.train:
data_path = self.root+'/train_data.gz'
label_path = self.root+'/train_labels.gz'
else:
data_path = self.root+'/test_data.gz'
label_path = self.root+'/test_labels.gz'
self.data = self._load_data(data_path)
self.label = MNIST.to_one_hot(self._load_label(label_path), 10)
def _load_data(self, filename):
with gzip.open(filename, 'rb') as file:
if gpu:
import numpy
data = np.asarray(numpy.frombuffer(file.read(), np.uint8, offset=16))
else:
data = np.frombuffer(file.read(), np.uint8, offset=16)
return data.reshape(-1,1,28,28)
def _load_label(self, filename):
with gzip.open(filename, 'rb') as file:
if gpu:
import numpy
labels = np.asarray(numpy.frombuffer(file.read(), np.uint8, offset=8))
else:
labels = np.frombuffer(file.read(), np.uint8, offset=8)
return labels
[docs] def show(self, row=10, col=10):
H, W = 28, 28
img = np.zeros((H*row, W*col))
for r in range(row):
for c in range(col):
img[r*H:(r+1)*H, c*W:(c+1)*W] = self.data[random.randint(0, len(self.data)-1)].reshape(H,W)
plt.imshow(to_cpu(img), cmap='gray', interpolation='nearest')
plt.axis('off')
plt.show()