def preprocess()

in container/neo_template_mxnet_byom.py [0:0]

• 48 lines of code
• 16 McCabe index (conditional complexity)

    def preprocess(self, batch_data):
        assert self._batch_size == len(batch_data), \
            'Invalid input batch size: expected {} but got {}'.format(self._batch_size,
                                                                      len(batch_data))
        processed_batch_data = []

        for k in range(len(batch_data)):
            req_body = batch_data[k]
            content_type = self._context.get_request_header(k, 'Content-type')
            if content_type is None:
                content_type = self._context.get_request_header(k, 'Content-Type')
                if content_type is None:
                    raise Exception('Content type could not be deduced')

            payload = batch_data[k].get('data')
            if payload is None:
                payload = batch_data[k].get('body')
            if payload is None:
                raise Exception('Nonexistent payload')

            # For BYOM, any content type is allowed
            print('content_type = {}'.format(content_type))
            try:
                # User is responsible for parsing payload into input(s)
                input_values = self.user_module.neo_preprocess(payload, content_type)
            except Exception as e:
                raise Exception('ClientError: User-defined pre-processing function failed:\n'
                                + str(e))

            # Validate parsed input(s)
            if isinstance(input_values, (np.ndarray, np.generic)):
                # Single input
                if len(self.input_names) != 1:
                    raise Exception('ClientError: User-defined pre-processing function returns ' +
                                    'a single input, but the model has multiple inputs.')
                input_values = {self.input_names[0]: input_values}
            elif isinstance(input_values, dict):
                # Multiple inputs
                given_names = set(input_values.keys())
                expected_names = set(self.input_names)
                if given_names != expected_names:  # Input name(s) mismatch
                    given_missing = expected_names - given_names
                    expected_missing = given_names - expected_names
                    msg = 'ClientError: Input name(s) mismatch: {0} {1}'
                    if given_missing:
                        msg += ('\nExpected ' + ', '.join(str(s) for s in given_missing) + \
                                ' in input data')
                    if expected_missing:
                        msg += ('\nThe model does not accept the following inputs: ' + \
                                ', '.join(str(s) for s in expected_missing))
                    msg = msg.format(given_names, expected_names)
                    raise Exception(msg)
            else:
                raise Exception('ClientError: User-defined pre-processing function must return ' +
                                'either dict type or np.ndarray')

            processed_batch_data.append(input_values)

        return processed_batch_data