torchvision/datasets/mnist.py
def download(self):
        """Download the MNIST data if it doesn't exist in processed_folder already."""

        if self._check_exists():
            return

        makedir_exist_ok(self.raw_folder)
        makedir_exist_ok(self.processed_folder)

        # download files
        for url in self.urls:
            filename = url.rpartition('/')[2]
            file_path = os.path.join(self.raw_folder, filename)
            download_url(url, root=self.raw_folder, filename=filename, md5=None)
            self.extract_gzip(gzip_path=file_path, remove_finished=True)

        # process and save as torch files
        print('Processing...')

        training_set = (
            read_image_file(os.path.join(self.raw_folder, 'train-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 'train-labels-idx1-ubyte'))
        )
        test_set = (
            read_image_file(os.path.join(self.raw_folder, 't10k-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 't10k-labels-idx1-ubyte'))
        )
        with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
            torch.save(training_set, f)
        with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
            torch.save(test_set, f)

        print('Done!')
def download(self):
        if self._check_exists():
            return

        makedir_exist_ok(self.raw_folder)
        makedir_exist_ok(self.processed_folder)

        # download files
        for url in self.urls:
            filename = url.rpartition('/')[2]
            file_path = os.path.join(self.raw_folder, filename)
            download_url(url, root=self.raw_folder, filename=filename, md5=None)
            self.extract_gzip(gzip_path=file_path, remove_finished=True)

        # process and save as torch files
        print('Processing...')

        training_set = (
            read_image_file(os.path.join(self.raw_folder, 'train-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 'train-labels-idx1-ubyte'))
        )
        test_set = (
            read_image_file(os.path.join(self.raw_folder, 't10k-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 't10k-labels-idx1-ubyte'))
        )
        with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
            torch.save(training_set, f)
        with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
            torch.save(test_set, f)

        print('Done!')
def download ( self ) : if self . _check_exists ( ) : return makedir_exist_ok ( self . raw_folder ) makedir_exist_ok ( self . processed_folder ) # download files for url in self . urls : filename = url . rpartition ( '/' ) [ 2 ] file_path = os . path . join ( self . raw_folder , filename ) download_url ( url , root = self . raw_folder , filename = filename , md5 = None ) self . extract_gzip ( gzip_path = file_path , remove_finished = True ) # process and save as torch files print ( 'Processing...' ) training_set = ( read_image_file ( os . path . join ( self . raw_folder , 'train-images-idx3-ubyte' ) ) , read_label_file ( os . path . join ( self . raw_folder , 'train-labels-idx1-ubyte' ) ) ) test_set = ( read_image_file ( os . path . join ( self . raw_folder , 't10k-images-idx3-ubyte' ) ) , read_label_file ( os . path . join ( self . raw_folder , 't10k-labels-idx1-ubyte' ) ) ) with open ( os . path . join ( self . processed_folder , self . training_file ) , 'wb' ) as f : torch . save ( training_set , f ) with open ( os . path . join ( self . processed_folder , self . test_file ) , 'wb' ) as f : torch . save ( test_set , f ) print ( 'Done!' )
Download the MNIST data if it doesn't exist in processed_folder already.
Download the MNIST data if it doesn t exist in processed_folder already .