Fashion-MNIST.zip（包括raw和processed文件夹）

from __future__ import print_function import torch.utils.data as data from PIL import Image import os import os.path import errno import numpy as np import torch import codecs import os import gzip root = r'/home/qinhaidong/桌面/书/动手学习深度学习/Dive-into-DL-PyTorch-master/data/Fashion-MNIST' raw_folder='raw' filenames=['train-images-idx3-ubyte.gz','train-labels-idx1-ubyte.gz','t10k-images-idx3-ubyte.gz','t10k-labels-idx1-ubyte.gz'] for filename in filenames: file_path=os.path.join(root, raw_folder, filename) with open(file_path.replace('.gz', ''), 'wb') as out_f, \ gzip.GzipFile(file_path) as zip_f: out_f.write(zip_f.read()) os.unlink(file_path) def get_int(b): return int(codecs.encode(b, 'hex'), 16) def read_label_file(path): with open(path, 'rb') as f: data = f.read() assert get_int(data[:4]) == 2049 length = get_int(data[4:8]) parsed = np.frombuffer(data, dtype=np.uint8, offset=8) return torch.from_numpy(parsed).view(length).long() def read_image_file(path): with open(path, 'rb') as f: data = f.read() assert get_int(data[:4]) == 2051 length = get_int(data[4:8]) num_rows = get_int(data[8:12]) num_cols = get_int(data[12:16]) images = [] parsed = np.frombuffer(data, dtype=np.uint8, offset=16) return torch.from_numpy(parsed).view(length, num_rows, num_cols) splits = ('byclass', 'bymerge', 'balanced', 'letters', 'digits', 'mnist') def _training_file(self, split): return 'training_{}.pt'.format(split) def _test_file(self, split): return 'test_{}.pt'.format(split) raw_folder = os.path.join(root, raw_folder) gzip_folder = os.path.join(raw_folder, 'gzip') raw_folder='raw' processed_folder='processed' training_file = 'training.pt' test_file = 'test.pt' training_set = ( read_image_file(os.path.join(root, raw_folder, 'train-images-idx3-ubyte')), read_label_file(os.path.join(root, raw_folder, 'train-labels-idx1-ubyte')) ) test_set = ( read_image_file(os.path.join(root, raw_folder, 't10k-images-idx3-ubyte')), read_label_file(os.path.join(root, raw_folder, 't10k-labels-idx1-ubyte')) ) with open(os.path.join(root, processed_folder, training_file), 'wb') as f: torch.save(training_set, f) with open(os.path.join(root, processed_folder, test_file), 'wb') as f: torch.save(test_set, f) print('Done!')