!791 onnx adapter for MaxPoolWithArgmax

Merge pull request !791 from 梅晓蔚/master

mindspore/ccsrc/onnx/onnx_exporter.cc

@@ -29,11 +29,12 @@
 
 namespace mindspore {
 enum OpMergeMode {
-  OP_MERGE_UNDEFINED = 0,   // undefined behavior
-  OP_MERGE_IGNORE = 1,      // indicate an input op merged into other op in compute node list
-  OP_MERGE_CONV = 2,        // indicate `MindSpore Conv + BiasAdd` --> `ONNX Conv`
-  OP_MERGE_GEMM = 3,        // indicate `MindSpore MatMul + BiasAdd` --> `ONNX Gemm`
-  OP_MERGE_BATCH_NORM = 4,  // indicate `MindSpore BatchNorm(x)[0]` --> `ONNX BatchNormalization`
+  OP_MERGE_UNDEFINED = 0,            // undefined behavior
+  OP_MERGE_IGNORE = 1,               // indicate an input op merged into other op in compute node list
+  OP_MERGE_CONV = 2,                 // indicate `MindSpore Conv + BiasAdd` --> `ONNX Conv`
+  OP_MERGE_GEMM = 3,                 // indicate `MindSpore MatMul + BiasAdd` --> `ONNX Gemm`
+  OP_MERGE_BATCH_NORM = 4,           // indicate `MindSpore BatchNorm(x)[0]` --> `ONNX BatchNormalization`
+  OP_MERGE_MAXPOOL_WITH_ARGMAX = 5,  // indicate `MindSpore MaxPoolWithArgmax(x)[0]` --> `ONNX MaxPool`
 };
 
 struct OpMergedInfo {
@@ -233,6 +234,13 @@ OPERATOR_ONNX_CONVERT_DEFINE(
     .Attr("padding", "auto_pad", onnx::AttributeProto_AttributeType_STRING, SetPoolingPadMode)
     .Attr("strides", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>))
 
+OPERATOR_ONNX_CONVERT_DEFINE(
+  MaxPoolWithArgmax, MaxPool,
+  OpNameInfo()
+    .Attr("ksize", "kernel_shape", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>)
+    .Attr("padding", "auto_pad", onnx::AttributeProto_AttributeType_STRING, SetPoolingPadMode)
+    .Attr("strides", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>))
+
 OPERATOR_ONNX_CONVERT_DEFINE(
   AvgPool, AveragePool,
   OpNameInfo()
@@ -254,6 +262,7 @@ void RegisterOpConverters(const std::function<void(OpNameInfo &&)> &fn) {
 
   fn(OP_CONVERT_FUNCTION_NAME(Flatten)());
   fn(OP_CONVERT_FUNCTION_NAME(MaxPool)());
+  fn(OP_CONVERT_FUNCTION_NAME(MaxPoolWithArgmax)());
   fn(OP_CONVERT_FUNCTION_NAME(AvgPool)());
 
   fn(OP_CONVERT_FUNCTION_NAME(Squeeze)());
@@ -328,6 +337,8 @@ class OnnxExporter {
                        onnx::GraphProto *graph_proto);
   void ExportMergeBatchNorm(const FuncGraphPtr &func_graph, const CNodePtr &node,
                             std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
+  void ExportMergeMaxPoolWithArgmax(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                                    std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
 
   void ExportOutput(const FuncGraphPtr &func_graph, const CNodePtr &node, std::map<AnfNodePtr, size_t> *node_map_ptr,
                     onnx::GraphProto *graph_proto);
@@ -516,6 +527,12 @@ void OnnxExporter::MatchAndMark(const FuncGraphPtr &func_graph, const std::vecto
       op_merged_infos[cnode].mode = OP_MERGE_BATCH_NORM;
       op_merged_infos[cnode->input(1)].mode = OP_MERGE_IGNORE;
       op_merged_infos[cnode->input(1)].referred_count -= 1;
+    } else if (cnode->IsApply(prim::kPrimTupleGetItem) &&
+               IsPrimitiveCNode(cnode->input(1), std::make_shared<Primitive>("MaxPoolWithArgmax")) &&
+               GetInt32Value(cnode->input(2)) == 0) {
+      op_merged_infos[cnode].mode = OP_MERGE_MAXPOOL_WITH_ARGMAX;
+      op_merged_infos[cnode->input(1)].mode = OP_MERGE_IGNORE;
+      op_merged_infos[cnode->input(1)].referred_count -= 1;
     }
   }
 }
@@ -563,6 +580,9 @@ void OnnxExporter::ExportNodes(const FuncGraphPtr &func_graph, std::map<AnfNodeP
       case OP_MERGE_BATCH_NORM:
         ExportMergeBatchNorm(func_graph, cnode, node_map_ptr, graph_proto);
         break;
+      case OP_MERGE_MAXPOOL_WITH_ARGMAX:
+        ExportMergeMaxPoolWithArgmax(func_graph, cnode, node_map_ptr, graph_proto);
+        break;
       default:
         ExportCNode(func_graph, cnode, node_map_ptr, graph_proto);
         break;
@@ -811,6 +831,20 @@ void OnnxExporter::ExportMergeBatchNorm(const FuncGraphPtr &func_graph, const CN
   (*node_map_ptr)[node] = ExportPrimitive(func_graph, node_map_ptr, prim_batch_norm, inputs, graph_proto);
 }
 
+void OnnxExporter::ExportMergeMaxPoolWithArgmax(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                                                std::map<AnfNodePtr, size_t> *node_map_ptr,
+                                                onnx::GraphProto *const graph_proto) {
+  auto maxpool_with_argmax_node = dyn_cast<CNode>(node->input(1));
+
+  PrimitivePtr prim_maxpool_with_argmax =
+    dyn_cast<Primitive>((dyn_cast<ValueNode>(maxpool_with_argmax_node->input(0)))->value());
+  std::vector<AnfNodePtr> inputs;
+  for (size_t i = 1; i < maxpool_with_argmax_node->inputs().size(); i++) {
+    inputs.push_back(maxpool_with_argmax_node->input(i));
+  }
+  (*node_map_ptr)[node] = ExportPrimitive(func_graph, node_map_ptr, prim_maxpool_with_argmax, inputs, graph_proto);
+}
+
 void OnnxExporter::ExportOutput(const FuncGraphPtr & /*func_graph*/, const CNodePtr &node,
                                 std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
   if (node->inputs().size() != 2) {

tests/ut/python/utils/test_serialize.py

@@ -362,6 +362,31 @@ def test_lenet5_onnx_export():
     net = LeNet5()
     export(net, input, file_name='lenet5.onnx', file_format='ONNX')
 
+class DefinedNet(nn.Cell):
+    """simple Net definition with maxpoolwithargmax."""
+    def __init__(self, num_classes=10):
+        super(DefinedNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=2, strides=2)
+        self.flatten = nn.Flatten()
+        self.fc = nn.Dense(int(56*56*64), num_classes)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x, argmax = self.maxpool(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+        return x
+
+def test_net_onnx_maxpoolwithargmax_export():
+    input = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32) * 0.01)
+    net = DefinedNet()
+    export(net, input, file_name='definedNet.onnx', file_format='ONNX')
+
 
 @run_on_onnxruntime
 def test_lenet5_onnx_load_run():