jittor.dataset.mnist 源代码

# ***************************************************************
# Copyright(c) 2019
#     Meng-Hao Guo <guomenghao1997@gmail.com>
#     Dun Liang <randonlang@gmail.com>.
# All Rights Reserved.
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************

import os
import string
import numpy as np
import gzip
from PIL import Image
# our lib jittor import
from jittor.dataset.dataset import Dataset, dataset_root
from jittor_utils.misc import ensure_dir, download_url_to_local
import jittor as jt 
import jittor.transform as trans

[文档]class MNIST(Dataset): ''' Jittor's own class for loading MNIST dataset. Args:: [in] data_root(str): your data root. [in] train(bool): choose model train or val. [in] download(bool): Download data automatically if download is Ture. [in] batch_size(int): Data batch size. [in] shuffle(bool): Shuffle data if true. [in] transform(jittor.transform): transform data. Example:: from jittor.dataset.mnist import MNIST train_loader = MNIST(train=True).set_attrs(batch_size=16, shuffle=True) for i, (imgs, target) in enumerate(train_loader): ... ''' def __init__(self, data_root=dataset_root+"/mnist_data/", train=True, download=True, batch_size = 16, shuffle = False, transform=None): # if you want to test resnet etc you should set input_channel = 3, because the net set 3 as the input dimensions super().__init__() self.data_root = data_root self.is_train = train self.transform = transform self.batch_size = batch_size self.shuffle = shuffle if download == True: self.download_url() filesname = [ "train-images-idx3-ubyte.gz", "t10k-images-idx3-ubyte.gz", "train-labels-idx1-ubyte.gz", "t10k-labels-idx1-ubyte.gz" ] self.mnist = {} if self.is_train: with gzip.open(data_root + filesname[0], 'rb') as f: self.mnist["images"] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28, 28) with gzip.open(data_root + filesname[2], 'rb') as f: self.mnist["labels"] = np.frombuffer(f.read(), np.uint8, offset=8) else: with gzip.open(data_root + filesname[1], 'rb') as f: self.mnist["images"] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28, 28) with gzip.open(data_root + filesname[3], 'rb') as f: self.mnist["labels"] = np.frombuffer(f.read(), np.uint8, offset=8) assert(self.mnist["images"].shape[0] == self.mnist["labels"].shape[0]) self.total_len = self.mnist["images"].shape[0] # this function must be called self.set_attrs(total_len = self.total_len) def __getitem__(self, index): img = Image.fromarray(self.mnist['images'][index]).convert('RGB') if self.transform: img = self.transform(img) return trans.to_tensor(img), self.mnist['labels'][index]
[文档] def download_url(self): ''' Download mnist data set function, this function will be called when download is True. ''' resources = [ ("https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"), ("https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"), ("https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"), ("https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c") ] for url, md5 in resources: filename = url.rpartition('/')[2] download_url_to_local(url, filename, self.data_root, md5)
class EMNIST(Dataset): ''' Jittor's own class for loading EMNIST dataset. Args:: [in] data_root(str): your data root. [in] split(str): one of 'byclass', 'bymerge', 'balanced', 'letters', 'digits', 'mnist'. [in] train(bool): choose model train or val. [in] download(bool): Download data automatically if download is Ture. [in] batch_size(int): Data batch size. [in] shuffle(bool): Shuffle data if true. [in] transform(jittor.transform): transform data. Example:: from jittor.dataset.mnist import EMNIST train_loader = EMNIST(train=True).set_attrs(batch_size=16, shuffle=True) for i, (imgs, target) in enumerate(train_loader): ... ''' _merged_classes = {'c', 'i', 'j', 'k', 'l', 'm', 'o', 'p', 's', 'u', 'v', 'w', 'x', 'y', 'z'} _all_classes = set(string.digits + string.ascii_letters) classes_split_dict = { 'byclass': sorted(list(_all_classes)), 'bymerge': sorted(list(_all_classes - _merged_classes)), 'balanced': sorted(list(_all_classes - _merged_classes)), 'letters': ['N/A'] + list(string.ascii_lowercase), 'digits': list(string.digits), 'mnist': list(string.digits), } def __init__(self, data_root=dataset_root+"/emnist_data/", split='byclass', train=True, download=True, batch_size = 16, shuffle = False, transform=None): # if you want to test resnet etc you should set input_channel = 3, because the net set 3 as the input dimensions super().__init__() self.data_root = data_root self.is_train = train self.transform = transform self.batch_size = batch_size self.shuffle = shuffle if download == True: self.download_url() data_root = os.path.join(data_root, "gzip") filesname = [ f"emnist-{split}-train-images-idx3-ubyte.gz", f"emnist-{split}-t10k-images-idx3-ubyte.gz", f"emnist-{split}-train-labels-idx1-ubyte.gz", f"emnist-{split}-t10k-labels-idx1-ubyte.gz" ] for i in range(4): filesname[i] = os.path.join(data_root, filesname[i]) self.mnist = {} if self.is_train: with gzip.open(filesname[0], 'rb') as f: self.mnist["images"] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28, 28).transpose(0,2,1) with gzip.open(filesname[2], 'rb') as f: self.mnist["labels"] = np.frombuffer(f.read(), np.uint8, offset=8) else: with gzip.open(filesname[1], 'rb') as f: self.mnist["images"] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28, 28).transpose(0,2,1) with gzip.open(filesname[3], 'rb') as f: self.mnist["labels"] = np.frombuffer(f.read(), np.uint8, offset=8) assert(self.mnist["images"].shape[0] == self.mnist["labels"].shape[0]) self.total_len = self.mnist["images"].shape[0] # this function must be called self.set_attrs(total_len = self.total_len) def __getitem__(self, index): img = Image.fromarray(self.mnist['images'][index]).convert('RGB') if self.transform: img = self.transform(img) return trans.to_tensor(img), self.mnist['labels'][index] def download_url(self): ''' Download mnist data set function, this function will be called when download is True. ''' resources = [ ("https://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip", "58c8d27c78d21e728a6bc7b3cc06412e"), ] for url, md5 in resources: filename = "emnist.zip" download_url_to_local(url, filename, self.data_root, md5) import zipfile zf = zipfile.ZipFile(os.path.join(self.data_root, filename)) try: zf.extractall(path=self.data_root) except RuntimeError as e: print(e) raise zf.close()