add onnx loop if support

mindspore/lite/schema/model.fbs

@@ -264,6 +264,7 @@ union PrimitiveType {
     If,
     GeLU,
     Gru,
+    NonZero,
 }
 
 enum QuantType: int {

mindspore/lite/schema/model_v0.fbs

@@ -236,7 +236,8 @@ union PrimitiveType {
     LpNormalization,
     DropoutGrad,
     MaximumGrad,
-    MinimumGrad
+    MinimumGrad,
+    NonZero,
 }
 
 enum QuantType: int {

mindspore/lite/schema/ops.fbs

@@ -1241,3 +1241,6 @@ table Merge {
 table GeLU {
     approximate : bool = false;
 }
+
+table NonZero {
+}

mindspore/lite/schema/ops_v0.fbs

@@ -1143,3 +1143,6 @@ table LpNormalization {
     axis : int;
     p : int;
 }
+
+table NonZero {
+}

mindspore/lite/src/ops/nonzero.cc

@@ -0,0 +1,124 @@
+/**
+ * Copyright 2019-2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#include "src/ops/nonzero.h"
+#include <algorithm>
+#include "include/errorcode.h"
+#include "src/common/log_adapter.h"
+#include "src/tensor.h"
+#ifndef PRIMITIVE_WRITEABLE
+#include "src/ops/ops_register.h"
+#endif
+
+namespace mindspore {
+namespace lite {
+#ifdef PRIMITIVE_WRITEABLE
+int NonZero::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) {
+  if (this->primitive_ == nullptr) {
+    this->primitive_ = new (std::nothrow) schema::PrimitiveT;
+    if (this->primitive_ == nullptr) {
+      MS_LOG(ERROR) << "new primitiveT failed";
+      return RET_ERROR;
+    }
+    this->primitive_->value.type = schema::PrimitiveType_NonZero;
+  }
+  if (this->primitive_->value.type != schema::PrimitiveType_NonZero) {
+    MS_LOG(ERROR) << "Primitive type is error :" << this->primitive_->value.type;
+    return RET_ERROR;
+  }
+  if (this->primitive_->value.value == nullptr) {
+    this->primitive_->value.value = new (std::nothrow) schema::NonZeroT();
+    if (this->primitive_->value.value == nullptr) {
+      MS_LOG(ERROR) << "new primitiveT value failed";
+      return RET_ERROR;
+    }
+  }
+  PopulaterQuantParam(prim, inputs);
+  return RET_OK;
+}
+#else
+int NonZero::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
+  MS_ASSERT(nullptr != primitive);
+  MS_ASSERT(nullptr != fbb);
+  auto attr = primitive->value_as_NonZero();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "value_as_NonZero return nullptr";
+    return RET_ERROR;
+  }
+  auto val_offset = schema::CreateNonZero(*fbb);
+  auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_NonZero, val_offset.o);
+  fbb->Finish(prim_offset);
+  return RET_OK;
+}
+PrimitiveC *NonZeroCreator(const schema::Primitive *primitive) { return PrimitiveC::NewPrimitiveC<NonZero>(primitive); }
+Registry NonZeroRegistry(schema::PrimitiveType_NonZero, NonZeroCreator);
+#endif
+template <typename T>
+void CalShape(const T *data, const std::vector<Tensor *> &inputs, std::vector<int> *out_shape) {
+  int input_count = inputs[0]->ElementsNum();
+  int input_dim_size = inputs[0]->shape().empty() ? 1 : inputs[0]->shape().size();
+  (*out_shape)[0] = input_dim_size;
+  int nonzero_size = 0;
+  for (int i = 0; i < input_count; i++) {
+    if (static_cast<int>(data[i]) != 0) {
+      nonzero_size++;
+    }
+  }
+  if (nonzero_size == 0) {
+    *out_shape = {};
+  } else {
+    (*out_shape)[1] = nonzero_size / input_dim_size;
+  }
+}
+int NonZero::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outputs_) {
+  MS_ASSERT(this->primitive_ != nullptr);
+  MS_ASSERT(inputs_.size() == 1);
+  auto input = inputs_.front();
+  MS_ASSERT(input != nullptr);
+  auto output = outputs_.front();
+  MS_ASSERT(output != nullptr);
+  output->set_data_type(input->data_type());
+  output->set_format(input->format());
+  if (!infer_flag()) {
+    return RET_INFER_INVALID;
+  }
+
+  std::vector<int> out_shape;
+  if (inputs_.size() == kSingleNum) {
+    auto input_tensor = inputs_.at(0);
+    if (input_tensor->data_c() == nullptr) {
+      MS_LOG(INFO) << "Do infer shape in runtime.";
+      return RET_INFER_INVALID;
+    }
+    switch (input_tensor->data_type()) {
+      case kNumberTypeFloat: {
+        auto data = reinterpret_cast<float *>(input_tensor->MutableData());
+        CalShape<float>(data, inputs_, &out_shape);
+      } break;
+      default: {
+        MS_LOG(ERROR) << "NonZero weight tensor has unsupported dataType: " << input_tensor->data_type();
+        return RET_INFER_ERR;
+      }
+    }
+  } else {
+    MS_LOG(ERROR) << "inputs tensor size invalid.";
+    return RET_INFER_ERR;
+  }
+  output->set_shape(out_shape);
+  return RET_OK;
+}
+}  // namespace lite
+}  // namespace mindspore

mindspore/lite/src/ops/nonzero.h

@@ -0,0 +1,45 @@
+/**
+ * Copyright 2019-2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_OPS_NONZERO_H_
+#define MINDSPORE_LITE_SRC_OPS_NONZERO_H_
+
+#include <cmath>
+#include <memory>
+#include <set>
+#include <vector>
+
+#include "src/ops/primitive_c.h"
+
+namespace mindspore {
+namespace lite {
+class NonZero : public PrimitiveC {
+ public:
+  NonZero() = default;
+  ~NonZero() = default;
+#ifdef PRIMITIVE_WRITEABLE
+  MS_DECLARE_PARENT(NonZero, PrimitiveC);
+  explicit NonZero(schema::PrimitiveT *primitive) : PrimitiveC(primitive) {}
+  int UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) override;
+#else
+  int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
+#endif
+  int InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::Tensor *> outputs_) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+
+#endif  // MINDSPORE_LITE_SRC_OPS_NONZERO_H_

mindspore/lite/src/runtime/kernel/arm/fp32/nonzero_fp32.cc

@@ -0,0 +1,105 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+#include "src/runtime/kernel/arm/fp32/nonzero_fp32.h"
+#include "include/errorcode.h"
+#include "nnacl/op_base.h"
+#include "schema/model_generated.h"
+#include "src/kernel_registry.h"
+#include "src/runtime/runtime_api.h"
+#include "src/tensor.h"
+
+using mindspore::kernel::KERNEL_ARCH::kCPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_NonZero;
+
+namespace mindspore::kernel {
+int NonZeroCPUKernel::Init() {
+  if (!InferShapeDone()) {
+    return RET_OK;
+  }
+  return ReSize();
+}
+
+int NonZeroCPUKernel::ReSize() { return RET_OK; }
+int NonZeroCPUKernel::Run() {
+  auto in_tensor = in_tensors_.front();
+  auto out_tensor = out_tensors_.front();
+  auto input_data = reinterpret_cast<float *>(in_tensor->MutableData());
+  auto output_data = reinterpret_cast<int *>(out_tensor->MutableData());
+  auto input_dim_size = in_tensor->shape().size();
+  if (out_tensor->shape().size() != 2) {
+    MS_LOG(ERROR) << "out tensor shape size must be equal to 2!";
+    return RET_ERROR;
+  }
+  auto non_zero_nums = out_tensor->shape()[1];
+  int non_zero_count = 0;
+  std::vector coordiate_values(in_tensor->shape().size(), 0);
+  for (int i = 0; i < in_tensor->ElementsNum(); i += 1) {
+    if (input_data[i] != 0) {
+      for (size_t j = 0; j < input_dim_size; j++) {
+        output_data[non_zero_count + j * non_zero_nums] = coordiate_values[j];
+      }
+      non_zero_count++;
+    }
+    for (int idx = input_dim_size - 1; idx >= 0; --idx) {
+      if (coordiate_values[idx] != in_tensor->shape()[idx] - 1) {
+        coordiate_values[idx] = coordiate_values[idx] + 1;
+        break;
+      }
+      coordiate_values[idx] = 0;
+    }
+  }
+  return RET_OK;
+}
+
+kernel::LiteKernel *CpuNonZeroFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
+                                                const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter,
+                                                const lite::InnerContext *ctx, const kernel::KernelKey &desc,
+                                                const mindspore::lite::PrimitiveC *primitive) {
+  if (opParameter == nullptr) {
+    MS_LOG(ERROR) << "Input opParameter is nullptr!";
+    return nullptr;
+  }
+  if (ctx == nullptr) {
+    MS_LOG(ERROR) << "Input context is nullptr!";
+    free(opParameter);
+    return nullptr;
+  }
+  if (ctx->thread_num_ == 0) {
+    MS_LOG(ERROR) << "context thread num is 0!";
+    free(opParameter);
+    return nullptr;
+  }
+  auto *kernel = new (std::nothrow) NonZeroCPUKernel(opParameter, inputs, outputs, ctx, primitive);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "new NonZeroCPUKernel fail!";
+    free(opParameter);
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
+                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_NonZero, CpuNonZeroFp32KernelCreator)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/nonzero_fp32.h

@@ -0,0 +1,41 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_NONZERO_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_NONZERO_H_
+
+#include <vector>
+#include "src/lite_kernel.h"
+
+namespace mindspore::kernel {
+class NonZeroCPUKernel : public LiteKernel {
+ public:
+  NonZeroCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
+                   const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
+                   const mindspore::lite::PrimitiveC *primitive)
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive) {}
+
+  ~NonZeroCPUKernel() = default;
+
+  int Init() override;
+  int ReSize() override;
+  int Run() override;
+
+ protected:
+  int thread_count_ = 1;
+};
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_NONZERO_H_

mindspore/lite/tools/converter/anf_transform.cc

@@ -100,7 +100,8 @@ FuncGraphPtr AnfTransform::TransformSingleFuncGraph(const FuncGraphPtr &old_grap
     }
   }
 
-  if (config->fmk == lite::converter::FmkType_TFLITE || config->fmk == lite::converter::FmkType_TF) {
+  if (config->fmk == lite::converter::FmkType_TFLITE || config->fmk == lite::converter::FmkType_TF ||
+      config->fmk == lite::converter::FmkType_ONNX) {
     graph_pm->AddPass(std::make_shared<opt::WhilePass>());
     graph_pm->AddPass(std::make_shared<opt::IfPass>());
   }

mindspore/lite/tools/converter/parser/onnx/onnx_if_parser.cc

@@ -0,0 +1,42 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#include "tools/converter/parser/onnx/onnx_if_parser.h"
+#include <memory>
+#include "tools/converter/parser/onnx/onnx_model_parser.h"
+
+namespace mindspore {
+namespace lite {
+lite::PrimitiveC *OnnxIfParser::ParseLitePrimitive(const onnx::GraphProto &onnx_graph,
+                                                   const onnx::NodeProto &onnx_node) {
+  MS_LOG(DEBUG) << "onnx IfParser";
+  auto attr = std::make_unique<schema::IfT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return nullptr;
+  }
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "new primitive failed";
+    return nullptr;
+  }
+  primitive->value.type = schema::PrimitiveType_If;
+  primitive->value.value = attr.release();
+  return PrimitiveC::Create(primitive.release());
+}
+OnnxNodeRegistrar g_onnxIfParser("If", new OnnxIfParser());
+}  // namespace lite
+}  // namespace mindspore

mindspore/lite/tools/converter/parser/onnx/onnx_if_parser.h

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_IF_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_IF_PARSER_H
+
+#include "tools/converter/parser/onnx/onnx_node_parser.h"
+#include "tools/converter/parser/onnx/onnx_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+class OnnxIfParser : public OnnxNodeParser {
+ public:
+  OnnxIfParser() : OnnxNodeParser("If") {}
+  ~OnnxIfParser() override = default;
+
+  lite::PrimitiveC *ParseLitePrimitive(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_IF_PARSER_H

mindspore/lite/tools/converter/parser/onnx/onnx_loop_parser.cc

@@ -0,0 +1,42 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#include "tools/converter/parser/onnx/onnx_loop_parser.h"
+#include <memory>
+#include "tools/converter/parser/onnx/onnx_model_parser.h"
+
+namespace mindspore {
+namespace lite {
+lite::PrimitiveC *OnnxLoopParser::ParseLitePrimitive(const onnx::GraphProto &onnx_graph,
+                                                     const onnx::NodeProto &onnx_node) {
+  MS_LOG(DEBUG) << "onnx LoopParser";
+  auto attr = std::make_unique<schema::WhileT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return nullptr;
+  }
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "new primitive failed";
+    return nullptr;
+  }
+  primitive->value.type = schema::PrimitiveType_While;
+  primitive->value.value = attr.release();
+  return PrimitiveC::Create(primitive.release());
+}
+OnnxNodeRegistrar g_onnxLoopParser("Loop", new OnnxLoopParser());
+}  // namespace lite
+}  // namespace mindspore

mindspore/lite/tools/converter/parser/onnx/onnx_loop_parser.h

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_LOOP_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_LOOP_PARSER_H
+
+#include "tools/converter/parser/onnx/onnx_node_parser.h"
+#include "tools/converter/parser/onnx/onnx_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+class OnnxLoopParser : public OnnxNodeParser {
+ public:
+  OnnxLoopParser() : OnnxNodeParser("Loop") {}
+  ~OnnxLoopParser() override = default;
+
+  lite::PrimitiveC *ParseLitePrimitive(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_LOOP_PARSER_H

mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.h

@@ -54,29 +54,60 @@ class OnnxModelParser : public ModelParser {
 
  private:
   STATUS InitOriginModel(const std::string &model_file);
-  STATUS ConvertNodes();
-  STATUS ConvertConstTensors();
-  STATUS ConvertGraphInputs();
-  STATUS ConvertGraphOutputs();
-  STATUS BuildReturnNode(const std::vector<AnfNodePtr> &return_inputs);
+  STATUS ConvertNodes(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
+                      std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, std::vector<AnfNodePtr> *graph_inputs,
+                      const std::string &root_node_name);
+  STATUS ConvertOnnxGraph(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
+                          std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map,
+                          std::vector<AnfNodePtr> *graph_inputs, const std::string &root_node_name);
+  STATUS ConvertConstTensors(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
+                             std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map);
+  STATUS ConvertGraphInputs(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
+                            std::unordered_map<std::string, AnfNodePtr> *nodes_map);
+  STATUS ConvertGraphOutputs(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
+                             const std::unordered_map<std::string, AnfNodePtr> &anf_nodes_map);
+  STATUS BuildReturnNode(const FuncGraphPtr &func_graph_ptr, const std::vector<AnfNodePtr> &return_inputs);
   STATUS BuildParameterNode(const ParameterPtr &parameter_node, const onnx::TensorProto &tensor);
   STATUS BuildParameterNodeForQuantParam(void *data, const std::string &name, TypeId type);
-  STATUS BuildCNode(const onnx::NodeProto &onnx_node, lite::PrimitiveC *primitive_c);
-  STATUS BuildOpOutputs(const onnx::NodeProto &onnx_node, const CNodePtr &cnode);
-  STATUS ConvertSpecialOnnxNode(const onnx::NodeProto &onnx_node, lite::PrimitiveC *primitive_c);
-  STATUS ConvertOnnxGemmNode(const onnx::NodeProto &onnx_node, lite::PrimitiveC *primitive_c);
-  STATUS BuildCNodeForGemm(const onnx::NodeProto &onnx_node, lite::PrimitiveC *primitive_c, const std::string &name);
+  STATUS BuildCNode(const onnx::NodeProto &onnx_node, const FuncGraphPtr &func_graph_ptr,
+                    std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, std::vector<AnfNodePtr> *graph_inputs,
+                    lite::PrimitiveC *primitive_c, std::string loop_name);
+  STATUS BuildOpOutputs(const onnx::NodeProto &onnx_node, const FuncGraphPtr &func_graph_ptr,
+                        std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, const CNodePtr &cnode);
+  STATUS ConvertSpecialOnnxNode(const onnx::NodeProto &onnx_node, const FuncGraphPtr &func_graph_ptr,
+                                std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map,
+                                lite::PrimitiveC *primitive_c);
+  STATUS ConvertOnnxGemmNode(const onnx::NodeProto &onnx_node, const FuncGraphPtr &func_graph_ptr,
+                             std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, lite::PrimitiveC *primitive_c);
+  STATUS BuildCNodeForGemm(const onnx::NodeProto &onnx_node, const FuncGraphPtr &func_graph_ptr,
+                           std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, lite::PrimitiveC *primitive_c,
+                           const std::string &name);
   STATUS ConvertOpQuantParams(const onnx::NodeProto &onnx_node, lite::PrimitiveC *primitive_c);
   STATUS ParseQuantParam(const onnx::NodeProto &onnx_node);
   STATUS SetTensorQuantParam(const std::string &tensor_name, std::vector<QuantParamT> *quant_params);
   STATUS SetTensorQuantParamFromNode(const std::string &tensor_name, std::vector<QuantParamT> *quant_params);
   STATUS CopyTensorQuantParam(const std::string &tensor_name, QuantParamT *quant_param, bool scale_or_not);
   bool IsSpecialOnnxNode(const onnx::NodeProto &onnx_node);
-
+  STATUS ConvertLoopOnnxNode(const onnx::NodeProto &onnx_node,
+                             std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map,
+                             const std::string &root_node_name);
+  STATUS ConvertIfOnnxNode(const onnx::NodeProto &onnx_node, std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map,
+                           const std::string &root_node_name);
+  STATUS AddTensorArrayEdge(const FuncGraphPtr &anf_graph, std::vector<AnfNodePtr> *return_new_inputs,
+                            const std::string &loop_node_name, std::vector<AnfNodePtr> *body_graph_inputs,
+                            int act_output_num);
+  STATUS BuildCondGraph(const FuncGraphPtr &cond_graph, const AnfNodePtr &root_while_node, int inputs_num,
+                        const std::string &cond_graph_name);
+  STATUS ConvertIfSubgraph(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &anf_graph,
+                           const std::string &subgrah_name, const std::string &if_node_name,
+                           const std::string &root_node_name);
   onnx::ModelProto onnx_model_;
-  onnx::GraphProto onnx_graph_;
-  std::unordered_map<std::string, AnfNodePtr> nodes_;
-  FuncGraphPtr func_graph_ptr_ = nullptr;
+  onnx::GraphProto onnx_root_graph_;
+  std::vector<FuncGraphPtr> all_subgraphs_;
+  std::unordered_map<std::string, AnfNodePtr> anf_nodes_map_;
+  std::unordered_map<std::string, std::unordered_map<std::string, AnfNodePtr> *> control_nodes_map_;
+  std::unordered_map<std::string, std::string> child_root_map_;  // for nest control flow node
+  FuncGraphPtr anf_root_graph_ = nullptr;
 };
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/parser/onnx/onnx_nonzero_parser.cc

@@ -0,0 +1,42 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#include "tools/converter/parser/onnx/onnx_nonzero_parser.h"
+#include <memory>
+#include "tools/converter/parser/onnx/onnx_model_parser.h"
+
+namespace mindspore {
+namespace lite {
+lite::PrimitiveC *OnnxNonZeroParser::ParseLitePrimitive(const onnx::GraphProto &onnx_graph,
+                                                        const onnx::NodeProto &onnx_node) {
+  MS_LOG(DEBUG) << "onnx NonZeroParser";
+  auto attr = std::make_unique<schema::NonZeroT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return nullptr;
+  }
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "new primitive failed";
+    return nullptr;
+  }
+  primitive->value.type = schema::PrimitiveType_NonZero;
+  primitive->value.value = attr.release();
+  return PrimitiveC::Create(primitive.release());
+}
+OnnxNodeRegistrar g_onnxNonZeroParser("NonZero", new OnnxNonZeroParser());
+}  // namespace lite
+}  // namespace mindspore

mindspore/lite/tools/converter/parser/onnx/onnx_nonzero_parser.h

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed 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.
+ */
+
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_NONZERO_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_NONZERO_PARSER_H
+
+#include "tools/converter/parser/onnx/onnx_node_parser.h"
+#include "tools/converter/parser/onnx/onnx_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+class OnnxNonZeroParser : public OnnxNodeParser {
+ public:
+  OnnxNonZeroParser() : OnnxNodeParser("NonZero") {}
+  ~OnnxNonZeroParser() override = default;
+
+  lite::PrimitiveC *ParseLitePrimitive(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_NONZERO_PARSER_H

mindspore/lite/tools/optimizer/graph/identity_remove_pass.cc

@@ -96,6 +96,19 @@ bool RemoveIdentityOpPass::Run(const FuncGraphPtr &func_graph) {
       status = ReplaceIdentity(node, manager);
     } else if (type == schema::PrimitiveType_TupleGetItem) {
       status = ReplaceTupleGetItem(node, manager);
+    } else if (type == schema::PrimitiveType_If || type == schema::PrimitiveType_While) {
+      auto sub_func_graph = GetValueNode<FuncGraphPtr>(node->cast<CNodePtr>()->input(1));
+      if (sub_func_graph == nullptr) {
+        lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
+        return false;
+      }
+      (void)Run(sub_func_graph);
+      sub_func_graph = GetValueNode<FuncGraphPtr>(node->cast<CNodePtr>()->input(2));
+      if (sub_func_graph == nullptr) {
+        lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
+        return false;
+      }
+      (void)Run(sub_func_graph);
     }
     if (status != lite::RET_OK && status != lite::RET_NO_CHANGE) {
       MS_LOG(ERROR) << "remove identity pass is failed.";

mindspore/lite/tools/optimizer/graph/onnx_inputs_adjust_pass.cc

@@ -296,6 +296,45 @@ STATUS OnnxInputAdjustOpPass::AdjustStridedSlice(const FuncGraphPtr &func_graph,
   return lite::RET_OK;
 }
 
+STATUS OnnxInputAdjustOpPass::AdjustResize(const CNodePtr &cnode) {
+  MS_ASSERT(cnode != nullptr);
+  auto node = cnode->input(0);
+  MS_ASSERT(value_node != nullptr);
+  auto value_node = node->cast<ValueNodePtr>();
+  if (value_node == nullptr) {
+    MS_LOG(ERROR) << "cnode input0 is not a valuenode.";
+    return lite::RET_ERROR;
+  }
+  MS_ASSERT(value_node->value() != nullptr);
+  auto primitive_c = value_node->value()->cast<PrimitiveCPtr>();
+  if (primitive_c == nullptr) {
+    MS_LOG(ERROR) << "cnode has no primitive_c.";
+    return lite::RET_ERROR;
+  }
+  auto primitive = primitive_c->primitiveT();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "cnode has no schema::primitive.";
+    return lite::RET_ERROR;
+  }
+  if (primitive->value.type != schema::PrimitiveType_Resize) {
+    MS_LOG(DEBUG) << "cnode is not cast node.";
+    return RET_OK;
+  }
+  auto value = primitive->value.value;
+  if (value == nullptr) {
+    MS_LOG(ERROR) << "value is nullptr.";
+    return lite::RET_ERROR;
+  }
+  auto attr = reinterpret_cast<schema::ResizeT *>(value);
+  if (cnode->inputs().size() > 3 &&
+      attr->coordinateTransformMode == schema::CoordinateTransformMode_TF_CROP_AND_RESIZE) {
+    auto new_resize_inputs = cnode->inputs();
+    new_resize_inputs.erase(new_resize_inputs.begin() + 1);
+    cnode->set_inputs(new_resize_inputs);
+  }
+  return lite::RET_OK;
+}
+
 STATUS OnnxInputAdjustOpPass::AdjustConvOrDeConv(const CNodePtr &cnode) {
   MS_ASSERT(cnode != nullptr);
   if (!CheckInputs(cnode)) {

mindspore/lite/tools/optimizer/graph/onnx_inputs_adjust_pass.h

@@ -40,6 +40,7 @@ class OnnxInputAdjustOpPass : public Pass {
   STATUS AdjustConvOrDeConv(const CNodePtr &cnode);
   STATUS AdjustTile(const CNodePtr &cnode);
   STATUS AdjustCast(const CNodePtr &cnode);
+  STATUS AdjustResize(const CNodePtr &cnode);
   STATUS ReplaceConstant(const FuncGraphPtr &func_graph, const CNodePtr &cnode);
   STATUS ReplaceTransposeWithGraphInput(const FuncGraphPtr &func_graph, const CNodePtr &cnode);
   bool Run(const FuncGraphPtr &func_graph) override;