tests/python-pytest/onnx/backend_rep.py

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# coding: utf-8
"""backend rep for onnx test infrastructure"""
from collections import namedtuple
import numpy as np
try:
    from onnx.backend.base import BackendRep
except ImportError:
    raise ImportError("Onnx and protobuf need to be installed")
import mxnet as mx

# Using these functions for onnx test infrastructure.
# Implemented by following onnx docs guide:
# https://github.com/onnx/onnx/blob/master/docs/Implementing%20an%20ONNX%20backend.md
# MXNetBackendRep object will be returned by MXNetBackend's prepare method which is used to
# execute a model repeatedly.
# Inputs will be passed to the run method of MXNetBackendRep class, it will perform computation and
# retrieve the corresponding results for comparison to the onnx backend.
# https://github.com/onnx/onnx/blob/master/onnx/backend/test/runner/__init__.py.

class MXNetBackendRep(BackendRep):
    """Running model inference on mxnet engine and return the result
     to onnx test infrastructure for comparison."""
    def __init__(self, symbol, params, device):
        self.symbol = symbol
        self.params = params
        self.device = device

    def run(self, inputs, **kwargs):
        """Run model inference and return the result

        Parameters
        ----------
        inputs : numpy array
            input to run a layer on

        Returns
        -------
        params : numpy array
            result obtained after running the inference on mxnet
        """
        input_data = np.asarray(inputs[0], dtype='f')

        # create module, passing cpu context
        if self.device == 'CPU':
            ctx = mx.cpu()
        else:
            raise NotImplementedError("Only CPU context is supported for now")

        mod = mx.mod.Module(symbol=self.symbol, data_names=['input_0'], context=ctx,
                            label_names=None)
        mod.bind(for_training=False, data_shapes=[('input_0', input_data.shape)],
                 label_shapes=None)
        mod.set_params(arg_params=self.params, aux_params=None)

        # run inference
        batch = namedtuple('Batch', ['data'])

        mod.forward(batch([mx.nd.array(input_data)]))
        result = mod.get_outputs()[0].asnumpy()
        return [result]