tests/python-pytest/onnx/export/backend_rep.py

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# coding: utf-8
"""backend rep for onnx test infrastructure"""
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, arg_params, aux_params, device):
        self.symbol = symbol
        self.arg_params = arg_params
        self.aux_params = aux_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
        """
        data_forward = []
        for val in inputs:
            data_forward.append(mx.nd.array(val))
        # create module, passing cpu context
        if self.device == 'CPU':
            ctx = mx.cpu()
        else:
            raise NotImplementedError("ONNX tests are run only for CPU context.")

        # To fetch the data names of the input to the model we list the inputs of the symbol graph
        # and exclude the argument and auxiliary parameters from the list
        data_names = [graph_input for graph_input in self.symbol.list_inputs()
                      if graph_input not in self.arg_params and graph_input not in self.aux_params]

        data_shapes = []
        for idx, input_name in enumerate(data_names):
            data_shapes.append((input_name, inputs[idx].shape))

        mod = mx.mod.Module(symbol=self.symbol, data_names=data_names, context=ctx,
                            label_names=None)
        mod.bind(for_training=False, data_shapes=data_shapes,
                 label_shapes=None)
        mod.set_params(arg_params=self.arg_params, aux_params=self.aux_params)

        # run inference
        mod.forward(mx.io.DataBatch(data_forward))
        result = mod.get_outputs()[0].asnumpy()
        return [result]