Source code for tensorpack.dataflow.dataset.svhn

# -*- coding: utf-8 -*-
# File:

import numpy as np
import os

from ...utils import logger
from ...utils.fs import download, get_dataset_path
from ..base import RNGDataFlow

__all__ = ['SVHNDigit']


[docs]class SVHNDigit(RNGDataFlow): """ `SVHN <>`_ Cropped Digit Dataset. Produces [img, label], img of 32x32x3 in range [0,255], label of 0-9 """ _Cache = {}
[docs] def __init__(self, name, data_dir=None, shuffle=True): """ Args: name (str): 'train', 'test', or 'extra'. data_dir (str): a directory containing the original {train,test,extra}_32x32.mat. shuffle (bool): shuffle the dataset. """ self.shuffle = shuffle if name in SVHNDigit._Cache: self.X, self.Y = SVHNDigit._Cache[name] return if data_dir is None: data_dir = get_dataset_path('svhn_data') assert name in ['train', 'test', 'extra'], name filename = os.path.join(data_dir, name + '_32x32.mat') if not os.path.isfile(filename): url = SVHN_URL + os.path.basename(filename)"File {} not found!".format(filename))"Downloading from {} ...".format(url)) download(url, os.path.dirname(filename))"Loading {} ...".format(filename)) data = self.X = data['X'].transpose(3, 0, 1, 2) self.Y = data['y'].reshape((-1)) self.Y[self.Y == 10] = 0 SVHNDigit._Cache[name] = (self.X, self.Y)
def __len__(self): return self.X.shape[0] def __iter__(self): n = self.X.shape[0] idxs = np.arange(n) if self.shuffle: self.rng.shuffle(idxs) for k in idxs: # since svhn is quite small, just do it for safety yield [self.X[k], self.Y[k]]
[docs] @staticmethod def get_per_pixel_mean(names=('train', 'test', 'extra')): """ Args: names (tuple[str]): names of the dataset split Returns: a 32x32x3 image, the mean of all images in the given datasets """ for name in names: assert name in ['train', 'test', 'extra'], name images = [SVHNDigit(x).X for x in names] return np.concatenate(tuple(images)).mean(axis=0)
try: import except ImportError: from ...utils.develop import create_dummy_class SVHNDigit = create_dummy_class('SVHNDigit', '') # noqa if __name__ == '__main__': a = SVHNDigit('train') b = SVHNDigit.get_per_pixel_mean()