[GitHub] sandeep-krishnamurthy commented on a change in pull request #11213: [MXNET-533] MXNet-ONNX export

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sandeep-krishnamurthy commented on a change in pull request #11213: [MXNET-533] MXNet-ONNX export
URL: https://github.com/apache/incubator-mxnet/pull/11213#discussion_r194521974
 
 

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 File path: python/mxnet/contrib/onnx/_export/export_onnx.py
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+# 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.
+#
+# Based on
+# https://github.com/NVIDIA/mxnet_to_onnx/blob/master/mx2onnx_converter/mx2onnx_converter.py#
+#  Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions
+#  are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+#  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+#  PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+#  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+#  EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+#  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+#  PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+#  OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+#  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# coding: utf-8
+# pylint: disable=invalid-name,too-many-locals,no-self-use,too-many-arguments,
+# pylint: disable=maybe-no-member,too-many-nested-blocks
+"""MXNet to ONNX graph converter functions"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import json
+import numpy as np
+
+from .... import context
+from .... import ndarray as nd
+from .... import io
+from .... import module as mod
+
+
+class MXNetGraph(object):
+    """Class to convert MXNet to ONNX graph"""
+    registry_ = {}
+    input_output_maps_ = {}
+
+    def __init__(self):
+        # topologically sorted nodes
+        self.nodes = []
+        self.input_tensors = []
+        self.output_tensors = []
+
+    @staticmethod
+    def register(op_name):
+        """Register operator"""
+        def wrapper(func):
+            """Helper function to map functions"""
+            MXNetGraph.registry_[op_name] = func
+            return func
+
+        return wrapper
+
+    @staticmethod
+    def convert_layer(node, **kwargs):
+        """Convert MXNet layer to ONNX"""
+        op = str(node["op"])
+        if op not in MXNetGraph.registry_:
+            raise AttributeError("No conversion function registered for op type %s yet." % op)
+        convert_fun = MXNetGraph.registry_[op]
+        return convert_fun(node, **kwargs)
+
+    @staticmethod
+    def forward_pass(inputs, sym, arg_params, aux_params):
+        """ Do a forward pass based on the sym and params"""
+        data_names = [graph_input for graph_input in sym.list_inputs()
+                      if graph_input not in arg_params and graph_input not in aux_params
+                      and graph_input != 'softmax_label']
+
+        data_shapes = []
+        # Adding extra dimension of batch_size 1 if the batch_size is different for multiple inputs.
+        for idx, input_name in enumerate(data_names):
+            data_shapes.append((input_name, inputs[idx].shape))
+
+        # create module, passing cpu context
+        ctx = context.cpu()
+        test_mod = mod.Module(symbol=sym, data_names=data_names, context=ctx, label_names=None)
+        test_mod.bind(for_training=False, data_shapes=data_shapes, label_shapes=None)
+
+        # initializing parameters for calculating result of each individual node
+        if arg_params is None and aux_params is None:
+            test_mod.init_params()
+        else:
+            test_mod.set_params(arg_params=arg_params, aux_params=aux_params, allow_missing=True)
+
+        data_forward = []
+        for idx, input_name in enumerate(data_names):
+            val = inputs[idx]
+            data_forward.append(nd.array(val))
+
+        test_mod.forward(io.DataBatch(data_forward))
+        result = test_mod.get_outputs()[0].asnumpy()
+
+        return result.shape
+
+
+    @staticmethod
+    def split_params(sym, params):
+        """splitting params into args and aux params"""
+        arg_params = {}
+        aux_params = {}
+        for args in sym.list_arguments():
+            if args in params:
+                arg_params.update({args: nd.array(params[args])})
+        for aux in sym.list_auxiliary_states():
+            if aux in params:
+                aux_params.update({aux: nd.array(params[aux])})
+        return arg_params, aux_params
+
+
+    @staticmethod
+    def infer_output_shape(sym, params, in_shape):
+        """Infer output shape by doing a forward pass using dummy inputs """
+        #create dummy input
+        inputs = [np.random.randn(*input_shape) for input_shape in in_shape]
+        arg, aux = MXNetGraph.split_params(sym, params)
+        return MXNetGraph.forward_pass(inputs, sym, arg, aux)
+
+
+    @staticmethod
+    def convert_weights_to_numpy(weights_dict):
+        """Convert weights to numpy"""
+        return dict([(k.replace("arg:", "").replace("aux:", ""), v.asnumpy())
+                     for k, v in weights_dict.items()])
+
+    def create_onnx_graph_proto(self, sym, params, in_shape, in_type, log=False):
+        """Convert MXNet graph to ONNX graph"""
+        try:
+            from onnx import (checker, helper, onnx_pb2)
+            from onnx.helper import make_tensor_value_info
+        except ImportError:
+            raise ImportError("Onnx and protobuf need to be installed. "
+                              + "Instructions to install - https://github.com/onnx/onnx")
+
+        # Determine output shape
+        output_shape = MXNetGraph.infer_output_shape(sym, params, in_shape)
+
+        weights = MXNetGraph.convert_weights_to_numpy(params)
+
+        mx_graph = json.loads(sym.tojson())["nodes"]
+
+        initializer = []
+        all_processed_nodes = []
+        onnx_processed_nodes = []
+        onnx_processed_inputs = []
+        onnx_processed_outputs = []
+        index_lookup = []
+
+        graph_input_idx = 0
+        for idx, node in enumerate(mx_graph):
+            op = node["op"]
+            name = node["name"]
+            if log:
+                print("Converting idx: %d, op: %s, name: %s" % (idx, op, name))
+
+            if op == "null" and name not in params:
+                # Handling graph input
+                if name == 'softmax_label':
+                    continue
+                converted = MXNetGraph.convert_layer(
+                    node,
+                    is_input=True,
+                    mx_graph=mx_graph,
+                    weights=weights,
+                    in_shape=in_shape[graph_input_idx],
+                    in_type=in_type,
+                    proc_nodes=all_processed_nodes,
+                    initializer=initializer,
+                    index_lookup=index_lookup)
+                graph_input_idx += 1
+
+            else:
+                converted = MXNetGraph.convert_layer(
+                    node,
+                    is_input=False,
+                    mx_graph=mx_graph,
+                    weights=weights,
+                    in_shape=in_shape,
+                    in_type=in_type,
+                    proc_nodes=all_processed_nodes,
+                    initializer=initializer,
+                    index_lookup=index_lookup,
+                    idx=idx
+                )
+
+            if isinstance(converted, list):
 
 Review comment:
   What if this is not a list?

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