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

        if self._check_exists():
            return

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

        # download files
        filename = self.url.rpartition('/')[2]
        file_path = os.path.join(self.raw_folder, filename)
        download_url(self.url, root=self.raw_folder, filename=filename, md5=None)

        print('Extracting zip archive')
        with zipfile.ZipFile(file_path) as zip_f:
            zip_f.extractall(self.raw_folder)
        os.unlink(file_path)
        gzip_folder = os.path.join(self.raw_folder, 'gzip')
        for gzip_file in os.listdir(gzip_folder):
            if gzip_file.endswith('.gz'):
                self.extract_gzip(gzip_path=os.path.join(gzip_folder, gzip_file))

        # process and save as torch files
        for split in self.splits:
            print('Processing ' + split)
            training_set = (
                read_image_file(os.path.join(gzip_folder, 'emnist-{}-train-images-idx3-ubyte'.format(split))),
                read_label_file(os.path.join(gzip_folder, 'emnist-{}-train-labels-idx1-ubyte'.format(split)))
            )
            test_set = (
                read_image_file(os.path.join(gzip_folder, 'emnist-{}-test-images-idx3-ubyte'.format(split))),
                read_label_file(os.path.join(gzip_folder, 'emnist-{}-test-labels-idx1-ubyte'.format(split)))
            )
            with open(os.path.join(self.processed_folder, self._training_file(split)), 'wb') as f:
                torch.save(training_set, f)
            with open(os.path.join(self.processed_folder, self._test_file(split)), 'wb') as f:
                torch.save(test_set, f)
        shutil.rmtree(gzip_folder)

        print('Done!')
def download(self):
        import shutil
        import zipfile

        if self._check_exists():
            return

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

        # download files
        filename = self.url.rpartition('/')[2]
        file_path = os.path.join(self.raw_folder, filename)
        download_url(self.url, root=self.raw_folder, filename=filename, md5=None)

        print('Extracting zip archive')
        with zipfile.ZipFile(file_path) as zip_f:
            zip_f.extractall(self.raw_folder)
        os.unlink(file_path)
        gzip_folder = os.path.join(self.raw_folder, 'gzip')
        for gzip_file in os.listdir(gzip_folder):
            if gzip_file.endswith('.gz'):
                self.extract_gzip(gzip_path=os.path.join(gzip_folder, gzip_file))

        # process and save as torch files
        for split in self.splits:
            print('Processing ' + split)
            training_set = (
                read_image_file(os.path.join(gzip_folder, 'emnist-{}-train-images-idx3-ubyte'.format(split))),
                read_label_file(os.path.join(gzip_folder, 'emnist-{}-train-labels-idx1-ubyte'.format(split)))
            )
            test_set = (
                read_image_file(os.path.join(gzip_folder, 'emnist-{}-test-images-idx3-ubyte'.format(split))),
                read_label_file(os.path.join(gzip_folder, 'emnist-{}-test-labels-idx1-ubyte'.format(split)))
            )
            with open(os.path.join(self.processed_folder, self._training_file(split)), 'wb') as f:
                torch.save(training_set, f)
            with open(os.path.join(self.processed_folder, self._test_file(split)), 'wb') as f:
                torch.save(test_set, f)
        shutil.rmtree(gzip_folder)

        print('Done!')
def download ( self ) : import shutil import zipfile if self . _check_exists ( ) : return makedir_exist_ok ( self . raw_folder ) makedir_exist_ok ( self . processed_folder ) # download files filename = self . url . rpartition ( '/' ) [ 2 ] file_path = os . path . join ( self . raw_folder , filename ) download_url ( self . url , root = self . raw_folder , filename = filename , md5 = None ) print ( 'Extracting zip archive' ) with zipfile . ZipFile ( file_path ) as zip_f : zip_f . extractall ( self . raw_folder ) os . unlink ( file_path ) gzip_folder = os . path . join ( self . raw_folder , 'gzip' ) for gzip_file in os . listdir ( gzip_folder ) : if gzip_file . endswith ( '.gz' ) : self . extract_gzip ( gzip_path = os . path . join ( gzip_folder , gzip_file ) ) # process and save as torch files for split in self . splits : print ( 'Processing ' + split ) training_set = ( read_image_file ( os . path . join ( gzip_folder , 'emnist-{}-train-images-idx3-ubyte' . format ( split ) ) ) , read_label_file ( os . path . join ( gzip_folder , 'emnist-{}-train-labels-idx1-ubyte' . format ( split ) ) ) ) test_set = ( read_image_file ( os . path . join ( gzip_folder , 'emnist-{}-test-images-idx3-ubyte' . format ( split ) ) ) , read_label_file ( os . path . join ( gzip_folder , 'emnist-{}-test-labels-idx1-ubyte' . format ( split ) ) ) ) with open ( os . path . join ( self . processed_folder , self . _training_file ( split ) ) , 'wb' ) as f : torch . save ( training_set , f ) with open ( os . path . join ( self . processed_folder , self . _test_file ( split ) ) , 'wb' ) as f : torch . save ( test_set , f ) shutil . rmtree ( gzip_folder ) print ( 'Done!' )
Download the EMNIST data if it doesn't exist in processed_folder already.
Download the EMNIST data if it doesn t exist in processed_folder already .