How to use kraken - 10 common examples

elif resize == 'both':
                message('Fitting network exactly to training set ', nl=False)
                logger.info('Resizing network or given codec to {} code sequences'.format(len(gt_set.alphabet)))
                gt_set.encode(None)
                ncodec, del_labels = codec.merge(gt_set.codec)
                logger.info('Deleting {} output classes from network ({} retained)'.format(len(del_labels), len(codec)-len(del_labels)))
                gt_set.encode(ncodec)
                nn.resize_output(ncodec.max_label()+1, del_labels)
                message('\u2713', fg='green')
            else:
                raise click.BadOptionUsage('resize', 'Invalid resize value {}'.format(resize))
    else:
        gt_set.encode(codec)
        logger.info('Creating new model {} with {} outputs'.format(spec, gt_set.codec.max_label()+1))
        spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label()+1)
        nn = vgsl.TorchVGSLModel(spec)
        # initialize weights
        message('Initializing model ', nl=False)
        nn.init_weights()
        nn.add_codec(gt_set.codec)
        # initialize codec
        message('\u2713', fg='green')

    # half the number of data loading processes if device isn't cuda and we haven't enabled preloading
    if device == 'cpu' and not preload:
        loader_threads = threads // 2
    else:
        loader_threads = threads
    train_loader = DataLoader(gt_set, batch_size=1, shuffle=True, num_workers=loader_threads, pin_memory=True)
    threads = max(threads-loader_threads, 1)

    # don't encode validation set as the alphabets may not match causing encoding failures

    @raises(KrakenInputException)
    def test_not_binarize_empty(self):
        """
        Test that mode '1' images aren't binarized again.
        """
        with Image.new('1', (1000,1000)) as im:
            nlbin(im)

    @raises(KrakenInputException)
    def test_rpred_outbounds(self):
        """
        Tests correct handling of invalid line coordinates.
        """
        nn = load_any(os.path.join(resources, 'toy.clstm'))
        pred = rpred(nn, self.im, {'boxes': [[-1, -1, 10000, 10000]], 'text_direction': 'horizontal'}, True)
        next(pred)

def test_load_any_proto(self):
        """
        Test load_any loads protobuf models.
        """
        rnn = models.load_any(os.path.join(resources, 'model.pronn'))
        self.assertIsInstance(rnn, kraken.lib.models.TorchSeqRecognizer)

def test_load_invalid(self):
        """
        Tests correct handling of invalid files.
        """
        models.load_any(self.temp.name)

def test_load_any_pyrnn_py3(self):
        """
        Test load_any doesn't load pickled models on python 3
        """
        rnn = models.load_any(os.path.join(resources, 'model.pyrnn.gz'))

def test_load_clstm(self):
        """
        Tests loading of valid clstm files.
        """
        rnn = models.load_any(os.path.join(resources, 'toy.clstm').encode('utf-8'))
        self.assertIsInstance(rnn, models.TorchSeqRecognizer)

def test_helper_train(self):
        """
        Tests train/eval mode helper methods
        """
        rnn = vgsl.TorchVGSLModel('[1,1,0,48 Lbx10 Do O1c57]')
        rnn.train()
        self.assertTrue(torch.is_grad_enabled())
        self.assertTrue(rnn.nn.training)
        rnn.eval()
        self.assertFalse(torch.is_grad_enabled())
        self.assertFalse(rnn.nn.training)

def test_save_model(self):
        """
        Test model serialization.
        """
        rnn = vgsl.TorchVGSLModel('[1,1,0,48 Lbx10 Do O1c57]')
        with tempfile.TemporaryDirectory() as dir:
            rnn.save_model(dir + '/foo.mlmodel')
            self.assertTrue(os.path.exists(dir + '/foo.mlmodel'))

def test_del_resize(self):
        """
        Tests resizing of output layers with entry deletion.
        """
        rnn = vgsl.TorchVGSLModel('[1,1,0,48 Lbx10 Do O1c57]')
        rnn.resize_output(80, [2, 4, 5, 6, 7, 12, 25])
        self.assertEqual(rnn.nn[-1].lin.out_features, 80)