ONNX test code cleanup

ci/docker/runtime_functions.sh

@@ -939,11 +939,11 @@ unittest_centos7_gpu() {
 integrationtest_ubuntu_cpu_onnx() {
 	set -ex
 	export PYTHONPATH=./python/
-	pytest tests/python-pytest/onnx/import/mxnet_backend_test.py
-	pytest tests/python-pytest/onnx/import/onnx_import_test.py
-	pytest tests/python-pytest/onnx/import/gluon_backend_test.py
-	pytest tests/python-pytest/onnx/export/onnx_backend_test.py
-	python tests/python-pytest/onnx/export/mxnet_export_test.py
+	pytest tests/python-pytest/onnx/gluon_backend_test.py
+	pytest tests/python-pytest/onnx/mxnet_backend_test.py
+	pytest tests/python-pytest/onnx/mxnet_export_test.py
+	pytest tests/python-pytest/onnx/test_models.py
+	pytest tests/python-pytest/onnx/test_node.py
 }
 
 integrationtest_ubuntu_gpu_python() {

tests/python-pytest/onnx/README.md

@@ -0,0 +1,33 @@
+# ONNX tests
+
+## Directory structure:
+
+```bash
+.
+├── README.md
+├── backend.py
+├── backend_rep.py
+├── backend_test.py
+├── gluon_backend_test.py
+├── mxnet_backend_test.py
+├── mxnet_export_test.py
+├── test_cases.py
+├── test_models.py
+└── test_node.py
+```
+
+* `backend.py` - MXNetBackend. This file contains prepare(). \
+This class can be used for both, MXNet and Gluon backend.
+* `backend_rep.py` - MXNetBackendRep and GluonBackendRep for running inference
+* `backend_test.py` - prepare tests by including tests from `test_cases.py`
+* `gluon_backend_test.py` - Set backend as gluon and execute ONNX tests for ONNX->Gluon import.
+* `mxnet_backend_test.py` - Set backend as gluon and add tests for ONNX->MXNet import/export.
+Since MXNetBackend for export, tests both import and export, the test list in this file is
+a union of tests that execute for import and export, export alone, and import alone.
+* `mxnet_export_test.py` - Execute unit tests for testing MXNet export code - this is not specific to
+any operator.
+* `test_cases.py` - list of test cases for operators/models that are supported
+for "both", import and export, "import" alone, or "export" alone.
+* `test_models.py` - custom tests for models
+* `test_node.py` - custom tests for operators. These tests are written independent of ONNX tests, in case
+ONNX doesn't have tests yet or for MXNet specific operators.

tests/python-pytest/onnx/export/backend.py → tests/python-pytest/onnx/backend.py

@@ -16,51 +16,57 @@
 # under the License.
 
 # coding: utf-8
-"""backend wrapper for onnx test infrastructure"""
-import os
-import sys
-import numpy as np
+"""MXNet/Gluon backend wrapper for onnx test infrastructure"""
+
 from mxnet.contrib.onnx.onnx2mx.import_onnx import GraphProto
 from mxnet.contrib.onnx.mx2onnx.export_onnx import MXNetGraph
+import mxnet as mx
+import numpy as np
+
 try:
     from onnx import helper, TensorProto, mapping
     from onnx.backend.base import Backend
 except ImportError:
-    raise ImportError("Onnx and protobuf need to be installed")
-CURR_PATH = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.insert(0, os.path.join(CURR_PATH, '../'))
-from backend_rep import MXNetBackendRep
+    raise ImportError("Onnx and protobuf need to be installed. Instructions to"
+                      + " install - /~https://github.com/onnx/onnx#installation")
+from backend_rep import MXNetBackendRep, GluonBackendRep
+
 
-# 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
 # MXNetBackend class will take an ONNX model with inputs, perform a computation,
 # and then return the output.
+# Implemented by following onnx docs guide:
+# /~https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md
 
 class MXNetBackend(Backend):
-    """MXNet backend for ONNX"""
+    """MXNet/Gluon backend for ONNX"""
+
+    backend = 'mxnet'
+    operation = 'import'
+
+    @classmethod
+    def set_params(cls, backend, operation):
+        cls.backend = backend
+        cls.operation = operation
 
     @staticmethod
-    def perform_import_export(graph_proto, input_shape):
+    def perform_import_export(sym, arg_params, aux_params, input_shape):
         """ Import ONNX model to mxnet model and then export to ONNX model
             and then import it back to mxnet for verifying the result"""
         graph = GraphProto()
 
-        sym, arg_params, aux_params = graph.from_onnx(graph_proto)
-
         params = {}
         params.update(arg_params)
         params.update(aux_params)
         # exporting to onnx graph proto format
         converter = MXNetGraph()
-        graph_proto = converter.create_onnx_graph_proto(sym, params, in_shape=input_shape, in_type=mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('float32')])
+        graph_proto = converter.create_onnx_graph_proto(sym, params, in_shape=input_shape,
+                                                        in_type=mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('float32')])
 
         # importing back to MXNET for verifying result.
         sym, arg_params, aux_params = graph.from_onnx(graph_proto)
 
         return sym, arg_params, aux_params
 
-
     @classmethod
     def prepare(cls, model, device='CPU', **kwargs):
         """For running end to end model(used for onnx test backend)
@@ -80,13 +86,31 @@ def prepare(cls, model, device='CPU', **kwargs):
             Returns object of MXNetBackendRep class which will be in turn
             used to run inference on the input model and return the result for comparison.
         """
+        backend = kwargs.get('backend', cls.backend)
+        operation = kwargs.get('operation', cls.operation)
 
         graph = GraphProto()
-        metadata = graph.get_graph_metadata(model.graph)
-        input_data = metadata['input_tensor_data']
-        input_shape = [data[1] for data in input_data]
-        sym, arg_params, aux_params = MXNetBackend.perform_import_export(model.graph, input_shape)
-        return MXNetBackendRep(sym, arg_params, aux_params, device)
+        if device == 'CPU':
+            ctx = mx.cpu()
+        else:
+            raise NotImplementedError("ONNX tests are run only for CPU context.")
+
+        if backend == 'mxnet':
+            sym, arg_params, aux_params = graph.from_onnx(model.graph)
+            if operation == 'export':
+                metadata = graph.get_graph_metadata(model.graph)
+                input_data = metadata['input_tensor_data']
+                input_shape = [data[1] for data in input_data]
+                sym, arg_params, aux_params = MXNetBackend.perform_import_export(sym, arg_params, aux_params,
+                                                                                 input_shape)
+
+            return MXNetBackendRep(sym, arg_params, aux_params, device)
+        elif backend == 'gluon':
+            if operation == 'import':
+                net = graph.graph_to_gluon(model.graph, ctx)
+                return GluonBackendRep(net, device)
+            elif operation == 'export':
+                raise NotImplementedError("Gluon->ONNX export not implemented.")
 
     @classmethod
     def supports_device(cls, device):
@@ -96,6 +120,4 @@ def supports_device(cls, device):
 
 prepare = MXNetBackend.prepare
 
-run_node = MXNetBackend.run_node
-
 supports_device = MXNetBackend.supports_device

tests/python-pytest/onnx/backend_rep.py

@@ -22,7 +22,9 @@
 except ImportError:
     raise ImportError("Onnx and protobuf need to be installed. Instructions to"
                       + " install - /~https://github.com/onnx/onnx#installation")
+import numpy as np
 import mxnet as mx
+from mxnet import nd
 
 # Using these functions for onnx test infrastructure.
 # Implemented by following onnx docs guide:
@@ -82,3 +84,47 @@ def run(self, inputs, **kwargs):
         exe.forward(is_train=False)
         result = exe.outputs[0].asnumpy()
         return [result]
+
+
+# GluonBackendRep object will be returned by GluonBackend'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.
+# Implemented by following onnx docs guide:
+# /~https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md
+
+class GluonBackendRep(BackendRep):
+    """Running model inference on gluon backend and return the result
+     to onnx test infrastructure for comparison."""
+    def __init__(self, net, device):
+        self.net = net
+        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
+        """
+        # create module, passing cpu context
+        if self.device == 'CPU':
+            ctx = mx.cpu()
+        else:
+            raise NotImplementedError("ONNX tests are run only for CPU context.")
+
+        # run inference
+        net_inputs = [nd.array(input_data, ctx=ctx) for input_data in inputs]
+        net_outputs = self.net(*net_inputs)
+        results = []
+        results.extend([o for o in net_outputs.asnumpy()])
+        result = np.array(results)
+
+        return [result]

tests/python-pytest/onnx/backend_test.py

@@ -0,0 +1,55 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""ONNX test backend wrapper"""
+try:
+    import onnx.backend.test
+except ImportError:
+    raise ImportError("Onnx and protobuf need to be installed")
+
+import test_cases
+
+
+def prepare_tests(backend, operation):
+    """
+    Prepare the test list
+    :param backend: mxnet/gluon backend
+    :param operation: str. export or import
+    :return: backend test list
+    """
+    BACKEND_TESTS = onnx.backend.test.BackendTest(backend, __name__)
+    implemented_ops = test_cases.IMPLEMENTED_OPERATORS_TEST.get('both', []) + \
+                      test_cases.IMPLEMENTED_OPERATORS_TEST.get(operation, [])
+
+    for op_test in implemented_ops:
+        BACKEND_TESTS.include(op_test)
+
+    basic_models = test_cases.BASIC_MODEL_TESTS.get('both', []) + \
+                   test_cases.BASIC_MODEL_TESTS.get(operation, [])
+
+    for basic_model_test in basic_models:
+        BACKEND_TESTS.include(basic_model_test)
+
+    std_models = test_cases.STANDARD_MODEL.get('both', []) + \
+                 test_cases.STANDARD_MODEL.get(operation, [])
+
+    for std_model_test in std_models:
+        BACKEND_TESTS.include(std_model_test)
+
+    BACKEND_TESTS.exclude('.*bcast.*')
+
+    return BACKEND_TESTS