!7327 [lite] append onnx parser

Merge pull request !7327 from 徐安越/master

mindspore/lite/schema/model.fbs

@@ -219,6 +219,10 @@ union PrimitiveType {
     Sgd,
     Adam,
     GroupConv2DGradInput,
+    Loop,
+    NonMaxSuppression,
+    InstanceNorm,
+    Identity,
 }
 
 enum QuantType: int {
@@ -250,6 +254,7 @@ table MetaGraph {
     mempoolSize: uint;
     nodes: [CNode];
     allTensors: [Tensor]; // weight + input + output
+    subGraph : [MetaGraph];
 }
 
 root_type MetaGraph;

mindspore/lite/schema/ops.fbs

@@ -18,8 +18,28 @@ namespace mindspore.schema;
 
 enum ResizeMethod: byte {
     UNKNOW = -1,
-    BILINEAR = 0,
-    NEAREST_NEIGHBOR = 1
+    LINEAR = 0,
+    NEAREST = 1,
+    CUBIC = 2
+}
+
+enum CoordinateTransformMode: byte {
+    COMMON = 0,
+    HALF_PIXEL = 1,
+    PYTORCH_HALF_PIXEL = 2,
+    TF_HALF_PIXEL = 3,
+    TF_CROP_AND_RESIZE = 4,
+    ALIGN_CORNERS = 5,
+    ASYMMETRIC = 6,
+    ALIGN_CORNERS_WITH_HALF_PIEXL = 7
+}
+
+enum NearestMode : byte {
+    NORMAL = 0,
+    ROUND_HALF_DOWN = 1,
+    ROUND_HALF_UP = 2,
+    FLOOR = 3,
+    CEIL = 4
 }
 
 enum Format : int {
@@ -376,8 +396,13 @@ table Resize {
     method: ResizeMethod;
     newHeight: long;
     newWidth: long;
-    alignCorners: bool = false;
+    alignCorners: bool = false;  // DEPRECATED IN FUTURE: use 'coordinateTransformMode' instead.
     preserveAspectRatio: bool = false;
+    coordinateTransformMode : CoordinateTransformMode;
+    cubicCoeff : float;
+    excludeOutside : int;
+    extrapolationValue : float = 0;
+    nearestMode : NearestMode;
 }
 
 table DetectionPostProcess {
@@ -1054,3 +1079,21 @@ table FftReal {
 
 table FftImag {
 }
+
+table NonMaxSuppression {
+    maxOutBoxPerClass : int = 0;
+    iouThreshold : float = 0;
+    scoreThreshold : float = 0;
+    centerPointBox : int = 0;
+}
+
+table InstanceNorm {
+    epsilon : float = 0.00001;
+}
+
+table Loop {
+    subGraphIndex : int;
+}
+
+table Identity {
+}

mindspore/lite/src/ops/resize.cc

@@ -51,9 +51,9 @@ int Resize::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inp
   if (this->primitive_->value.value == nullptr) {
     auto attr = new (std::nothrow) schema::ResizeT();
     if (prim.instance_name() == "ResizeNearestNeighbor") {
-      attr->method = schema::ResizeMethod_NEAREST_NEIGHBOR;
+      attr->method = schema::ResizeMethod_NEAREST;
     } else if (prim.instance_name() == "ResizeBilinear") {
-      attr->method = schema::ResizeMethod_BILINEAR;
+      attr->method = schema::ResizeMethod_LINEAR;
     } else {
       MS_LOG(ERROR) << "wrong resize type";
       return RET_ERROR;

mindspore/lite/src/runtime/kernel/arm/base/resize_base.cc

@@ -41,8 +41,8 @@ int ResizeBaseCPUKernel::CheckParameters() {
     return RET_NULL_PTR;
   }
   method_ = parameter->method_;
-  if (method_ != static_cast<int>(schema::ResizeMethod_BILINEAR) &&
-      method_ != static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR)) {
+  if (method_ != static_cast<int>(schema::ResizeMethod_LINEAR) &&
+      method_ != static_cast<int>(schema::ResizeMethod_NEAREST)) {
     MS_LOG(ERROR) << "Resize method should be bilinear or nearest_neighbor, but got " << method_;
     return RET_INVALID_OP_ATTR;
   }

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

@@ -14,11 +14,11 @@
  * limitations under the License.
  */
 
-#include <algorithm>
 #include "src/runtime/kernel/arm/fp32/resize.h"
-#include "schema/model_generated.h"
-#include "nnacl/fp32/resize.h"
+#include <algorithm>
 #include "include/errorcode.h"
+#include "nnacl/fp32/resize.h"
+#include "schema/model_generated.h"
 #include "src/runtime/runtime_api.h"
 
 using mindspore::kernel::KERNEL_ARCH::kCPU;
@@ -41,7 +41,7 @@ int ResizeCPUKernel::Init() {
 
 int ResizeCPUKernel::ReSize() {
   int ret = RET_OK;
-  if (method_ == static_cast<int>(schema::ResizeMethod_BILINEAR)) {
+  if (method_ == static_cast<int>(schema::ResizeMethod_LINEAR)) {
     FreeTmpBuffer();
     ret = MallocTmpBuffer();
     if (ret != RET_OK) {
@@ -162,7 +162,7 @@ int ResizeCPUKernel::RunImpl(int task_id) {
 
   int ret = 0;
   switch (method_) {
-    case static_cast<int>(schema::ResizeMethod_BILINEAR): {
+    case static_cast<int>(schema::ResizeMethod_LINEAR): {
       int n_h_begin, n_h_end;
       int n = out_tensors_.at(0)->shape()[0];
       int h = new_height_;
@@ -178,7 +178,7 @@ int ResizeCPUKernel::RunImpl(int task_id) {
 
       break;
     }
-    case static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR): {
+    case static_cast<int>(schema::ResizeMethod_NEAREST): {
       if (in_tensors_.size() == lite::kDoubleNum && !const_shape_) {
         auto out_shape = in_tensors_.at(1);
         auto data = reinterpret_cast<int32_t *>(out_shape->MutableData());

mindspore/lite/src/runtime/kernel/arm/int8/resize_int8.cc

@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
+#include "src/runtime/kernel/arm/int8/resize_int8.h"
 #include <vector>
-#include "src/kernel_registry.h"
+#include "include/errorcode.h"
 #include "nnacl/int8/resize.h"
 #include "schema/model_generated.h"
-#include "include/errorcode.h"
-#include "src/runtime/kernel/arm/int8/resize_int8.h"
+#include "src/kernel_registry.h"
 #include "src/runtime/runtime_api.h"
 
 using mindspore::kernel::KERNEL_ARCH::kCPU;
@@ -84,7 +84,7 @@ int ResizeInt8CPUKernel::RunImpl(int task_id) {
 
   int ret = 0;
   switch (method_) {
-    case static_cast<int>(schema::ResizeMethod_BILINEAR): {
+    case static_cast<int>(schema::ResizeMethod_LINEAR): {
       if (quant_in_->zp_ == 0) {
         ret = ResizeBilinearInt8(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
                                  align_corners_, quant_in_, quant_out_, multiplier_, task_id, context_->thread_num_);
@@ -95,7 +95,7 @@ int ResizeInt8CPUKernel::RunImpl(int task_id) {
       }
       break;
     }
-    case static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR): {
+    case static_cast<int>(schema::ResizeMethod_NEAREST): {
       bool same_zp = quant_in_->zp_ == quant_out_->zp_;
       bool same_scale = abs(quant_out_->scale_ - quant_in_->scale_) < 1e-6;
       if (same_zp && same_scale) {

mindspore/lite/src/runtime/kernel/opencl/kernel/resize.cc

@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
+#include "src/runtime/kernel/opencl/kernel/resize.h"
+#include <map>
 #include <set>
 #include <string>
-#include <map>
 #include "include/errorcode.h"
 #include "src/kernel_registry.h"
-#include "src/runtime/kernel/opencl/kernel/resize.h"
 #include "src/runtime/kernel/opencl/cl/resize.cl.inc"
 
 using mindspore::kernel::KERNEL_ARCH::kGPU;
@@ -46,9 +46,9 @@ int ResizeOpenCLKernel::Init() {
     return RET_PARAM_INVALID;
   }
   std::string kernel_name = "resize";
-  if (resize_param->method_ == schema::ResizeMethod_BILINEAR) {
+  if (resize_param->method_ == schema::ResizeMethod_LINEAR) {
     kernel_name += "_bilinear";
-  } else if (resize_param->method_ == schema::ResizeMethod_NEAREST_NEIGHBOR) {
+  } else if (resize_param->method_ == schema::ResizeMethod_NEAREST) {
     kernel_name += "_nearest_neighbor";
   } else {
     MS_LOG(ERROR) << "unsupported resize method:" << resize_param->method_;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_bilinear_fp32_tests.cc

@@ -14,11 +14,11 @@
  * limitations under the License.
  */
 #include <vector>
+#include "common/common_test.h"
+#include "mindspore/lite/src/kernel_registry.h"
 #include "mindspore/lite/src/lite_kernel.h"
 #include "mindspore/lite/src/tensor.h"
-#include "common/common_test.h"
 #include "nnacl/resize_parameter.h"
-#include "mindspore/lite/src/kernel_registry.h"
 #include "schema/ops_generated.h"
 using mindspore::schema::Format_NHWC;
 
@@ -62,7 +62,7 @@ void TestResizeBilinearFp32::Prepare(const std::vector<int> &input_shape, const
   out_tensor_.SetData(output_data);
 
   ResizeParameter param_ = {
-    {}, static_cast<int>(schema::ResizeMethod_BILINEAR), output_shape[1], output_shape[2], align_corners};
+    {}, static_cast<int>(schema::ResizeMethod_LINEAR), output_shape[1], output_shape[2], align_corners};
   desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Resize};
   ctx_ = lite::InnerContext();
   ctx_.thread_num_ = thread_num;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_nearest_neighbor_fp32_tests.cc

@@ -16,7 +16,7 @@
 #include <vector>
 #include "common/common_test.h"
 #include "nnacl/resize_parameter.h"
-#include "mindspore/lite/src/kernel_registry.h"
+#include "src/kernel_registry.h"
 
 namespace mindspore {
 
@@ -57,7 +57,7 @@ void TestResizeNearestNeighborFp32::Prepare(const std::vector<int> &input_shape,
   out_tensor_.SetData(output_data);
 
   ResizeParameter param_ = {
-    {}, static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR), output_shape[1], output_shape[2], align_corners};
+    {}, static_cast<int>(schema::ResizeMethod_NEAREST), output_shape[1], output_shape[2], align_corners};
   desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Resize};
   ctx_ = lite::InnerContext();
   ctx_.thread_num_ = thread_num;

mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc

@@ -19,7 +19,7 @@
 #include "include/context.h"
 #include "src/tensor.h"
 #include "common/common_test.h"
-#include "mindspore/lite/src/kernel_registry.h"
+#include "src/kernel_registry.h"
 #include "nnacl/int8/resize.h"
 
 namespace mindspore {
@@ -68,7 +68,7 @@ void TestResizeBilinearInt8::Prepare(const std::vector<int> &in_shape, const std
   inputs.push_back(&in_tensor);
   outputs.push_back(&out_tensor);
 
-  param_.method_ = static_cast<int>(schema::ResizeMethod_BILINEAR);
+  param_.method_ = static_cast<int>(schema::ResizeMethod_LINEAR);
   param_.new_width_ = out_shape[2];
   param_.new_height_ = out_shape[1];
   param_.align_corners_ = align_corners;

mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_nearest_neighbor_int8_tests.cc

@@ -19,7 +19,7 @@
 #include "include/context.h"
 #include "src/tensor.h"
 #include "common/common_test.h"
-#include "mindspore/lite/src/kernel_registry.h"
+#include "src/kernel_registry.h"
 #include "nnacl/int8/resize.h"
 
 namespace mindspore {
@@ -63,7 +63,7 @@ void TestResizeNearestNeighborInt8::Prepare(const std::vector<int> &in_shape, co
   inputs.push_back(&in_tensor);
   outputs.push_back(&out_tensor);
 
-  param_.method_ = static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR);
+  param_.method_ = static_cast<int>(schema::ResizeMethod_NEAREST);
   param_.new_width_ = out_shape[2];
   param_.new_height_ = out_shape[1];
   param_.align_corners_ = align_corners;

mindspore/lite/test/ut/src/runtime/kernel/opencl/resize_tests.cc

@@ -15,13 +15,13 @@
  */
 #include <iostream>
 #include <memory>
-#include "src/common/log_adapter.h"
 #include "common/common_test.h"
-#include "mindspore/lite/src/common/file_utils.h"
-#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
-#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
-#include "mindspore/lite/src/runtime/kernel/opencl/kernel/resize.h"
-#include "mindspore/lite/test/ut/src/runtime/kernel/opencl/utils_tests.h"
+#include "src/common/file_utils.h"
+#include "src/common/log_adapter.h"
+#include "src/runtime/kernel/opencl/kernel/resize.h"
+#include "src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "test/ut/src/runtime/kernel/opencl/utils_tests.h"
 
 namespace mindspore {
 class TestResizeOpenCL : public mindspore::CommonTest {
@@ -119,7 +119,7 @@ TEST_F(TestResizeOpenCL, ResizeBilinearFp32) {
   std::vector<float> input_data = {0.0f, 1.0f, 2.0f, 3.0f};
   std::vector<float> output_data = {0.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.5f, 2.0f, 2.0f,
                                     2.0f, 2.5f, 3.0f, 3.0f, 2.0f, 2.5f, 3.0f, 3.0f};
-  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_BILINEAR, align_corners);
+  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_LINEAR, align_corners);
 }
 
 TEST_F(TestResizeOpenCL, ResizeBilinearFp16) {
@@ -134,7 +134,7 @@ TEST_F(TestResizeOpenCL, ResizeBilinearFp16) {
   std::vector<float16_t> input_data = {0.0f, 1.0f, 2.0f, 3.0f};
   std::vector<float16_t> output_data = {0.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.5f, 2.0f, 2.0f,
                                         2.0f, 2.5f, 3.0f, 3.0f, 2.0f, 2.5f, 3.0f, 3.0f};
-  RunTestCaseResize(shape, input_data.data(), output_data.data(), true, schema::ResizeMethod_BILINEAR, align_corners);
+  RunTestCaseResize(shape, input_data.data(), output_data.data(), true, schema::ResizeMethod_LINEAR, align_corners);
 }
 
 TEST_F(TestResizeOpenCL, ResizeBilinearAlignFp32) {
@@ -148,7 +148,7 @@ TEST_F(TestResizeOpenCL, ResizeBilinearAlignFp32) {
   std::vector<int> shape = {n, h, w, oh, ow, c};
   std::vector<float> input_data = {0.0f, 1.0f, 2.0f, 3.0f};
   std::vector<float> output_data = {0.0f, 0.5f, 1.0f, 1.0f, 1.5f, 2.0f, 2.0f, 2.5f, 3.0f};
-  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_BILINEAR, align_corners);
+  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_LINEAR, align_corners);
 }
 
 TEST_F(TestResizeOpenCL, ResizeNearestNeighborFp32) {
@@ -163,8 +163,7 @@ TEST_F(TestResizeOpenCL, ResizeNearestNeighborFp32) {
   std::vector<float> input_data = {0.0f, 1.0f, 2.0f, 3.0f};
   std::vector<float> output_data = {0.0f, 0.0f, 1.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f,
                                     2.0f, 2.0f, 3.0f, 3.0f, 2.0f, 2.0f, 3.0f, 3.0f};
-  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_NEAREST_NEIGHBOR,
-                    align_corners);
+  RunTestCaseResize(shape, input_data.data(), output_data.data(), false, schema::ResizeMethod_NEAREST, align_corners);
 }
 
 TEST_F(TestResizeOpenCL, ResizeNearestNeighborFp16) {
@@ -179,7 +178,6 @@ TEST_F(TestResizeOpenCL, ResizeNearestNeighborFp16) {
   std::vector<float16_t> input_data = {0.0f, 1.0f, 2.0f, 3.0f};
   std::vector<float16_t> output_data = {0.0f, 0.0f, 1.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f,
                                         2.0f, 2.0f, 3.0f, 3.0f, 2.0f, 2.0f, 3.0f, 3.0f};
-  RunTestCaseResize(shape, input_data.data(), output_data.data(), true, schema::ResizeMethod_NEAREST_NEIGHBOR,
-                    align_corners);
+  RunTestCaseResize(shape, input_data.data(), output_data.data(), true, schema::ResizeMethod_NEAREST, align_corners);
 }
 }  // namespace mindspore

mindspore/lite/test/ut/tools/converter/parser/tflite/tflite_resize_parser_test.cc

@@ -40,7 +40,7 @@ TEST_F(TestTfliteParserResizeNN, AttrValue) {
   ASSERT_EQ(val->newWidth, 100);
   ASSERT_EQ(val->format, schema::Format_NHWC);
   ASSERT_EQ(val->preserveAspectRatio, false);
-  ASSERT_EQ(val->method, schema::ResizeMethod_NEAREST_NEIGHBOR);
+  ASSERT_EQ(val->method, schema::ResizeMethod_NEAREST);
 }
 
 class TestTfliteParserResizeBilinear : public TestTfliteParser {
@@ -64,7 +64,7 @@ TEST_F(TestTfliteParserResizeBilinear, AttrValue) {
   ASSERT_EQ(val->newWidth, 4);
   ASSERT_EQ(val->format, schema::Format_NHWC);
   ASSERT_EQ(val->preserveAspectRatio, false);
-  ASSERT_EQ(val->method, schema::ResizeMethod_BILINEAR);
+  ASSERT_EQ(val->method, schema::ResizeMethod_LINEAR);
 }
 
 }  // namespace mindspore

mindspore/lite/tools/converter/parser/caffe/caffe_interp_parser.cc

@@ -57,7 +57,7 @@ STATUS CaffeInterpParser::Parse(const caffe::LayerParameter &proto, const caffe:
     attr->newWidth = width;
   }
   attr->alignCorners = true;
-  attr->method = schema::ResizeMethod_BILINEAR;
+  attr->method = schema::ResizeMethod_LINEAR;
 
   op->name = proto.name();
   op->primitive->value.type = schema::PrimitiveType_Resize;

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

@@ -582,6 +582,94 @@ STATUS OnnxSignParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
   return RET_OK;
 }
 
+STATUS OnnxAndParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx AndParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::LogicalAndT> attr = std::make_unique<schema::LogicalAndT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_LogicalAnd;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+STATUS OnnxOrParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx OrParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::LogicalOrT> attr = std::make_unique<schema::LogicalOrT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_LogicalOr;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+STATUS OnnxNotParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx NotParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::LogicalNotT> attr = std::make_unique<schema::LogicalNotT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_LogicalNot;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+STATUS OnnxRoundParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
+                              schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx RoundParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::RoundT> attr = std::make_unique<schema::RoundT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_Round;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
 OnnxNodeRegistrar g_onnxAddParser("Add", new OnnxAddParser());
 OnnxNodeRegistrar g_onnxInt8AddParser("Int8Add", new OnnxAddParser());
 OnnxNodeRegistrar g_onnxSubParser("Sub", new OnnxSubParser());
@@ -608,5 +696,9 @@ OnnxNodeRegistrar g_onnxAtanParser("Atan", new OnnxAtanParser());
 OnnxNodeRegistrar g_onnxAsinParser("Asin", new OnnxAsinParser());
 OnnxNodeRegistrar g_onnxTanhParser("Tanh", new OnnxTanhParser());
 OnnxNodeRegistrar g_onnxSignParser("Sign", new OnnxTanhParser());
+OnnxNodeRegistrar g_onnxAndParser("And", new OnnxAndParser());
+OnnxNodeRegistrar g_onnxOrParser("Or", new OnnxOrParser());
+OnnxNodeRegistrar g_onnxNotParser("Not", new OnnxNotParser());
+OnnxNodeRegistrar g_onnxRoundParser("Round", new OnnxRoundParser());
 }  // namespace lite
 }  // namespace mindspore

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

@@ -171,6 +171,30 @@ class OnnxSignParser : public OnnxNodeParser {
   OnnxSignParser() : OnnxNodeParser("Sign") {}
   STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
 };
+
+class OnnxAndParser : public OnnxNodeParser {
+ public:
+  OnnxAndParser() : OnnxNodeParser("And") {}
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+
+class OnnxOrParser : public OnnxNodeParser {
+ public:
+  OnnxOrParser() : OnnxNodeParser("Or") {}
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+
+class OnnxNotParser : public OnnxNodeParser {
+ public:
+  OnnxNotParser() : OnnxNodeParser("Not") {}
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+
+class OnnxRoundParser : public OnnxNodeParser {
+ public:
+  OnnxRoundParser() : OnnxNodeParser("Round") {}
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
 }  // namespace lite
 }  // namespace mindspore
 #endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_ARITHMETIC_OPREATION_PARSER_H

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

@@ -15,9 +15,9 @@
  */
 
 #include "tools/converter/parser/onnx/onnx_conv_parser.h"
-#include <vector>
-#include <memory>
 #include <algorithm>
+#include <memory>
+#include <vector>
 
 namespace mindspore {
 namespace lite {
@@ -176,9 +176,6 @@ STATUS OnnxConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
       MS_LOG(ERROR) << "Convert Convolution to Depthwise failed";
       return RET_ERROR;
     }
-  } else if (attr->group != 1) {
-    MS_LOG(ERROR) << "group conv hasn't supported";
-    return RET_NOT_SUPPORT;
   } else {
     op->primitive->value.type = schema::PrimitiveType_Conv2D;
     op->primitive->value.value = attr.release();

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

@@ -0,0 +1,49 @@
+/**
+ * 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_identity_parser.h"
+#include <memory>
+#include <vector>
+
+namespace mindspore {
+namespace lite {
+STATUS OnnxIdentityParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
+                                 schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx IdentityParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::IdentityT> attr = std::make_unique<schema::IdentityT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+
+  op->primitive->value.type = schema::PrimitiveType_Identity;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+OnnxNodeRegistrar g_onnxIdentityParser("Identity", new OnnxIdentityParser());
+}  // namespace lite
+}  // namespace mindspore

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

@@ -0,0 +1,33 @@
+/**
+ * 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_IDENTITY_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_IDENTITY_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 OnnxIdentityParser : public OnnxNodeParser {
+ public:
+  OnnxIdentityParser() : OnnxNodeParser("Identity") {}
+
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_IDENTITY_PARSER_H

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

@@ -0,0 +1,55 @@
+/**
+ * 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_instance_norm_parser.h"
+#include <memory>
+
+namespace mindspore {
+namespace lite {
+STATUS OnnxInstanceNormParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
+                                     schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx InstanceNormParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::InstanceNormT> attr = std::make_unique<schema::InstanceNormT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+
+  if (!onnx_node.attribute().empty()) {
+    auto onnx_node_attr = onnx_node.attribute().at(0);
+    if (onnx_node_attr.name() == "epsilon") {
+      attr->epsilon = onnx_node_attr.f();
+    }
+  }
+
+  op->primitive->value.type = schema::PrimitiveType_InstanceNorm;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+OnnxNodeRegistrar g_onnxInstanceNormParser("InstanceNormalization", new OnnxInstanceNormParser());
+}  // namespace lite
+}  // namespace mindspore

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

@@ -0,0 +1,33 @@
+/**
+ * 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_INSTANCE_NORM_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_INSTANCE_NORM_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 OnnxInstanceNormParser : public OnnxNodeParser {
+ public:
+  OnnxInstanceNormParser() : OnnxNodeParser("InstanceNorm") {}
+
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_INSTANCE_NORM_PARSER_H

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

@@ -15,12 +15,12 @@
  */
 
 #include "tools/converter/parser/onnx/onnx_model_parser.h"
+#include <algorithm>
 #include <cfloat>
 #include <unordered_map>
-#include <algorithm>
 #include <utility>
-#include "tools/common/graph_util.h"
 #include "src/common/utils.h"
+#include "tools/common/graph_util.h"
 #include "tools/common/protobuf_utils.h"
 
 namespace mindspore {
@@ -36,7 +36,8 @@ static const std::unordered_map<int, mindspore::TypeId> TYPE_MAP = {
   {onnx::TensorProto_DataType_UINT32, mindspore::kNumberTypeUInt32},
   {onnx::TensorProto_DataType_INT64, mindspore::kNumberTypeInt64},
   {onnx::TensorProto_DataType_FLOAT16, mindspore::kNumberTypeFloat16},
-  {onnx::TensorProto_DataType_FLOAT, mindspore::kNumberTypeFloat32}};
+  {onnx::TensorProto_DataType_FLOAT, mindspore::kNumberTypeFloat32},
+  {onnx::TensorProto_DataType_BOOL, mindspore::kNumberTypeBool}};
 
 TypeId OnnxModelParser::GetDataTypeFromOnnx(onnx::TensorProto_DataType onnx_type) {
   auto iter = TYPE_MAP.find(onnx_type);
@@ -161,9 +162,13 @@ STATUS OnnxModelParser::SetGraphOutputTensor(const onnx::GraphProto &onnx_graph,
                                              TensorCache *tensor_cache) {
   for (const auto &output_value : onnx_graph.output()) {
     int index;
-    const auto status = AddValueInfo(output_value, output_value.name(), OP_OUTPUT, tensor_cache, &index);
-    if (status != RET_OK) {
-      return status;
+    if (tensor_cache->FindTensor(output_value.name()) != -1) {
+      index = tensor_cache->FindTensor(output_value.name());
+    } else {
+      const auto status = AddValueInfo(output_value, output_value.name(), OP_OUTPUT, tensor_cache, &index);
+      if (status != RET_OK) {
+        return status;
+      }
     }
     graph->outputIndex.emplace_back(index);
     MS_LOG(DEBUG) << "output_value name: " << output_value.name() << ", graph output index: " << index;
@@ -250,7 +255,8 @@ STATUS OnnxModelParser::ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node,
 
 STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
                                              schema::CNodeT *dst_op, schema::TensorT *dst_tensor,
-                                             TensorCache *tensor_cache, const QuantType &quantType) {
+                                             TensorCache *tensor_cache, const QuantType &quantType,
+                                             schema::MetaGraphT *dst_graph) {
   // change op_type() to name(), that is unique
   static bool interrupt = false;
   dst_op->name = onnx_node.op_type() + "_" + onnx_node.output(0);
@@ -260,23 +266,34 @@ STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph,
                 << onnx_node.input_size();
   // get the real op type
   SetOpQuantParams(onnx_graph, onnx_node, dst_op, dst_tensor, tensor_cache);
-  auto node_parser = OnnxNodeParserRegistry::GetInstance()->GetNodeParser(onnx_node.op_type());
-  if (node_parser == nullptr || interrupt) {
+  if (onnx_node.op_type() == "Loop") {
+    NoSupportOp::GetInstance()->InsertOp(onnx_node.op_type());
     interrupt = true;
-    if (node_parser == nullptr) {
-      NoSupportOp::GetInstance()->InsertOp(onnx_node.op_type());
-    }
     return RET_NOT_FIND_OP;
-  }
-  auto status = node_parser->Parse(onnx_graph, onnx_node, dst_op);
-  if (status != RET_OK) {
-    interrupt = true;
-    if (status == RET_NOT_SUPPORT) {
-      NoSupportOp::GetInstance()->InsertOp(onnx_node.op_type());
-    } else {
-      MS_LOG(ERROR) << "parser onnx node " << onnx_node.op_type() << " attr failed";
+    int status = ParseLoopAttr(dst_op, onnx_node, quantType, dst_graph);
+    if (status != RET_OK || interrupt) {
+      interrupt = true;
+      return status;
+    }
+  } else {
+    auto node_parser = OnnxNodeParserRegistry::GetInstance()->GetNodeParser(onnx_node.op_type());
+    if (node_parser == nullptr || interrupt) {
+      interrupt = true;
+      if (node_parser == nullptr) {
+        NoSupportOp::GetInstance()->InsertOp(onnx_node.op_type());
+      }
+      return RET_NOT_FIND_OP;
+    }
+    auto status = node_parser->Parse(onnx_graph, onnx_node, dst_op);
+    if (status != RET_OK) {
+      interrupt = true;
+      if (status == RET_NOT_FIND_OP) {
+        NoSupportOp::GetInstance()->InsertOp(onnx_node.op_type());
+      } else {
+        MS_LOG(ERROR) << "parser onnx node " << onnx_node.op_type() << " attr failed";
+      }
+      return status;
     }
-    return status;
   }
   // set op input index
   std::vector<string> node_inputs;
@@ -366,7 +383,7 @@ STATUS OnnxModelParser::SetOpInputIndex(const std::vector<string> &node_inputs,
                                         const onnx::NodeProto &onnx_node, TensorCache *tensor_cache) {
   for (const auto &onnx_node_input : node_inputs) {
     if (onnx_node_input != "") {
-      auto index = tensor_cache->FindTensor(onnx_node_input);
+      int index = tensor_cache->FindTensor(onnx_node_input);
       if (index < 0) {
         MS_LOG(ERROR) << "input " << onnx_node_input << " of node " << onnx_node.name() << " can't be found";
         return RET_ERROR;
@@ -428,6 +445,9 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
       }
       for (size_t i = 0; i < data_count; ++i) {
         if (in_data[i] > static_cast<int64_t>(INT32_MAX) || in_data[i] < static_cast<int64_t>(INT32_MIN)) {
+          if (llabs(in_data[i]) == INT64_MAX || in_data[i] == INT64_MIN) {
+            buffer[i] = in_data[i] > 0 ? INT32_MAX : INT32_MIN;
+          }
           MS_LOG(ERROR) << "int64 data " << in_data[i] << "too big to fit into int32";
           return RET_ERROR;
         } else {
@@ -438,6 +458,7 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
       break;
     case kNumberTypeUInt8:
     case kNumberTypeInt8:
+    case kNumberTypeBool:
       data_size = data_count * sizeof(uint8_t);
       tensor_data = onnx_const_value.raw_data().data();
       break;
@@ -446,7 +467,7 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
       return RET_ERROR;
   }
   tensor->data.resize(data_size);
-  if (memcpy_s(static_cast<void *>(tensor->data.data()), data_size, tensor_data, data_size) != 0) {
+  if (data_size != 0 && memcpy_s(static_cast<void *>(tensor->data.data()), data_size, tensor_data, data_size) != 0) {
     MS_LOG(ERROR) << "memcpy_s failed";
     return RET_ERROR;
   }
@@ -475,30 +496,39 @@ void OnnxModelParser::FindGraphInputAndConst(const onnx::GraphProto &onnx_graph)
   }
 }
 
-schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, const std::string &weightFile,
-                                               const QuantType &quantType) {
-  int status = ValidateFileStr(modelFile, ".onnx");
-  if (status != RET_OK) {
-    MS_LOG(ERROR) << "Input illegal: modelFile must be *.onnx";
-    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
-  }
-
-  onnx::ModelProto onnx_model;
-  status = ReadProtoFromBinaryFile((const char *)modelFile.c_str(), &onnx_model);
-  if (status != RET_OK) {
-    MS_LOG(ERROR) << "Read onnx model file failed, model path: " << modelFile;
-    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
-  }
-  const onnx::GraphProto &onnx_graph = onnx_model.graph();
-  MS_LOG(INFO) << "model producer name: " << onnx_model.producer_name() << ", graph name: " << onnx_graph.name();
+STATUS OnnxModelParser::ParseLoopAttr(schema::CNodeT *dst_op, const onnx::NodeProto &onnx_node,
+                                      const QuantType &quantType, schema::MetaGraphT *dst_graph) {
+  MS_LOG(DEBUG) << "onnx LoopParser";
+  if (dst_op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  dst_op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (dst_op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+  std::unique_ptr<schema::LoopT> attr = std::make_unique<schema::LoopT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  attr->subGraphIndex = subGraphNum;
+  auto sub_graph = std::make_unique<schema::MetaGraphT>();
+  sub_graph.reset(ParseGraph(onnx_node.attribute().at(0).g(), quantType));
+  dst_graph->subGraph.push_back(std::move(sub_graph));
+  subGraphNum += 1;
+  dst_op->primitive->value.type = schema::PrimitiveType_Loop;
+  dst_op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+schema::MetaGraphT *OnnxModelParser::ParseGraph(const onnx::GraphProto &onnx_graph, const QuantType &quantType) {
   TensorCache tensor_cache;
   // dst_graph->name = onnx_graph.name();  // this is not used
   // find out input names and const names
   FindGraphInputAndConst(onnx_graph);
   // set const tensor
-  status = SetGraphConstTensor(onnx_graph, &tensor_cache);
+  int status = SetGraphConstTensor(onnx_graph, &tensor_cache);
   if (status != RET_OK) {
     MS_LOG(ERROR) << "SetGraphConstTensor failed";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
@@ -512,13 +542,7 @@ schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, con
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
     return nullptr;
   }
-  // init onnx model graph output tensor
-  status = SetGraphOutputTensor(onnx_graph, dst_graph.get(), &tensor_cache);
-  if (status != RET_OK) {
-    MS_LOG(ERROR) << "SetGraphOutputTensor failed";
-    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
-  }
+
   // init op node input/output tensor, and dst_op attr
   NoSupportOp::GetInstance()->SetFmkType("ONNX");
   for (const auto &onnx_node : onnx_graph.node()) {
@@ -544,7 +568,8 @@ schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, con
 
     std::unique_ptr<schema::CNodeT> dst_op = std::make_unique<schema::CNodeT>();
     std::unique_ptr<schema::TensorT> dst_tensor = std::make_unique<schema::TensorT>();
-    status_node = ParseOnnxNodeToDstOp(onnx_graph, onnx_node, dst_op.get(), dst_tensor.get(), &tensor_cache, quantType);
+    status_node = ParseOnnxNodeToDstOp(onnx_graph, onnx_node, dst_op.get(), dst_tensor.get(), &tensor_cache, quantType,
+                                       dst_graph.get());
     if (status_node != RET_OK) {
       status = (status == RET_OK ? status_node : status);
       continue;
@@ -558,9 +583,42 @@ schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, con
     }
     return nullptr;
   }
+  // init onnx model graph output tensor
+  status = SetGraphOutputTensor(onnx_graph, dst_graph.get(), &tensor_cache);
+  if (status != RET_OK) {
+    MS_LOG(ERROR) << "SetGraphOutputTensor failed";
+    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
+    return nullptr;
+  }
   SetAllTensors(tensor_cache, dst_graph.get());
-  dst_graph->name = GetModelName(modelFile);
   return dst_graph.release();
 }
+schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, const std::string &weightFile,
+                                               const QuantType &quantType) {
+  int status = ValidateFileStr(modelFile, ".onnx");
+  if (status != RET_OK) {
+    MS_LOG(ERROR) << "Input illegal: modelFile must be *.onnx";
+    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
+    return nullptr;
+  }
+
+  onnx::ModelProto onnx_model;
+  status = ReadProtoFromBinaryFile((const char *)modelFile.c_str(), &onnx_model);
+  if (status != RET_OK) {
+    MS_LOG(ERROR) << "Read onnx model file failed, model path: " << modelFile;
+    ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
+    return nullptr;
+  }
+  const onnx::GraphProto &onnx_graph = onnx_model.graph();
+  MS_LOG(INFO) << "model producer name: " << onnx_model.producer_name() << ", graph name: " << onnx_graph.name();
+
+  schema::MetaGraphT *dst_graph = ParseGraph(onnx_graph, quantType);
+  if (dst_graph == nullptr) {
+    return nullptr;
+  }
+  dst_graph->name = GetModelName(modelFile);
+  return dst_graph;
+}
+
 }  // namespace lite
 }  // namespace mindspore

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

@@ -26,6 +26,7 @@
 #include <vector>
 #include <memory>
 #include <set>
+#include <map>
 #include "securec/include/securec.h"
 #include "tools/converter/model_parser.h"
 #include "tools/converter/parser/onnx/onnx_node_parser_registry.h"
@@ -40,6 +41,7 @@ class OnnxModelParser : public ModelParser {
 
   virtual ~OnnxModelParser();
 
+  schema::MetaGraphT *ParseGraph(const onnx::GraphProto &graph, const QuantType &quantType = QuantType_QUANT_NONE);
   schema::MetaGraphT *ParseToFb(const std::string &modelFile, const std::string &weightFile,
                                 const QuantType &quantType = QuantType_QUANT_NONE) override;
 
@@ -62,7 +64,7 @@ class OnnxModelParser : public ModelParser {
 
   STATUS ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
                               schema::CNodeT *dst_op, schema::TensorT *dst_tensor, TensorCache *tensor_cache,
-                              const QuantType &quantType);
+                              const QuantType &quantType, schema::MetaGraphT *dst_graph);
 
   void ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
                          schema::MetaGraphT *graph, TensorCache *tensor_cache, const QuantType &quant_type);
@@ -86,9 +88,13 @@ class OnnxModelParser : public ModelParser {
 
   void FindGraphInputAndConst(const onnx::GraphProto &onnx_graph);
 
+  STATUS ParseLoopAttr(schema::CNodeT *dst_op, const onnx::NodeProto &onnx_node, const QuantType &quantType,
+                       schema::MetaGraphT *dst_graph);
+
  private:
-  std::vector<string> graphInputNames;
-  std::vector<string> graphConstNames;
+  std::vector<std::string> graphInputNames;
+  std::vector<std::string> graphConstNames;
+  int subGraphNum = 0;
 };
 }  // namespace lite
 }  // namespace mindspore

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

@@ -0,0 +1,80 @@
+/**
+ * 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_non_max_suppression_parser.h"
+#include <memory>
+
+namespace mindspore {
+namespace lite {
+STATUS OnnxNonMaxSuppressionParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
+                                          schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx EluParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::NonMaxSuppressionT> attr = std::make_unique<schema::NonMaxSuppressionT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  if (onnx_node.input_size() > 2) {
+    auto it = std::find_if(onnx_graph.initializer().begin(), onnx_graph.initializer().end(),
+                           [&](const onnx::TensorProto &it) { return it.name() == onnx_node.input(2); });
+    if (it != onnx_graph.initializer().end()) {
+      attr->maxOutBoxPerClass = it->int64_data(0);
+    }
+  }
+
+  if (onnx_node.input_size() > 3) {
+    auto it = std::find_if(onnx_graph.initializer().begin(), onnx_graph.initializer().end(),
+                           [&](const onnx::TensorProto &it) { return it.name() == onnx_node.input(3); });
+    if (it != onnx_graph.initializer().end()) {
+      attr->iouThreshold = it->float_data(0);
+    }
+  }
+
+  if (onnx_node.input_size() > 4) {
+    auto it = std::find_if(onnx_graph.initializer().begin(), onnx_graph.initializer().end(),
+                           [&](const onnx::TensorProto &it) { return it.name() == onnx_node.input(4); });
+    if (it != onnx_graph.initializer().end()) {
+      attr->scoreThreshold = it->float_data(0);
+    }
+  }
+
+  for (const auto &onnx_node_attr : onnx_node.attribute()) {
+    const auto &attribute_name = onnx_node_attr.name();
+    if (attribute_name == "center_point_box") {
+      if (onnx_node_attr.has_i()) {
+        attr->centerPointBox = onnx_node_attr.i();
+      }
+    }
+  }
+
+  op->primitive->value.type = schema::PrimitiveType_Elu;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+OnnxNodeRegistrar g_onnxNonMaxSuppressionParser("NonMaxSuppression", new OnnxNonMaxSuppressionParser());
+}  // namespace lite
+}  // namespace mindspore

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

@@ -0,0 +1,33 @@
+/**
+ * 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_NON_MAX_SUPPRESSION_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_NON_MAX_SUPPRESSION_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 OnnxNonMaxSuppressionParser : public OnnxNodeParser {
+ public:
+  OnnxNonMaxSuppressionParser() : OnnxNodeParser("NonMaxSuppression") {}
+
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_NON_MAX_SUPPRESSION_PARSER_H

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

@@ -0,0 +1,95 @@
+/**
+ * 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_resize_parser.h"
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace mindspore {
+namespace lite {
+STATUS OnnxResizeParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
+                               schema::CNodeT *op) {
+  MS_LOG(DEBUG) << "onnx ResizeParser";
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::ResizeT> attr = std::make_unique<schema::ResizeT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+
+  attr->format = schema::Format_NCHW;
+  attr->nearestMode = schema::NearestMode_ROUND_HALF_DOWN;
+  for (const auto &onnx_node_attr : onnx_node.attribute()) {
+    const auto &attribute_name = onnx_node_attr.name();
+    if (attribute_name == "coordinate_transformation_mode") {
+      attr->coordinateTransformMode = [&]() {
+        std::map<std::string, schema::CoordinateTransformMode> transform_map = {
+          {"half_pixel", schema::CoordinateTransformMode_HALF_PIXEL},
+          {"pytorch_half_pixel", schema::CoordinateTransformMode_PYTORCH_HALF_PIXEL},
+          {"align_corners", schema::CoordinateTransformMode_ALIGN_CORNERS},
+          {"asymmetric", schema::CoordinateTransformMode_ASYMMETRIC},
+          {"tf_half_pixel_for_nn", schema::CoordinateTransformMode_TF_HALF_PIXEL},
+          {"tf_crop_and_resize", schema::CoordinateTransformMode_TF_CROP_AND_RESIZE},
+        };
+        return transform_map[onnx_node_attr.strings(0)];
+      }();
+    } else if (attribute_name == "cubic_coeff_a") {
+      attr->cubicCoeff = onnx_node_attr.f();
+    } else if (attribute_name == "exclude_outside") {
+      attr->excludeOutside = onnx_node_attr.i();
+    } else if (attribute_name == "extrapolation_value") {
+      attr->extrapolationValue = onnx_node_attr.f();
+    } else if (attribute_name == "mode") {
+      attr->method = [&]() {
+        std::map<std::string, schema::ResizeMethod> resize_mode = {
+          {"nearest", schema::ResizeMethod_NEAREST},
+          {"linear", schema::ResizeMethod_LINEAR},
+          {"cubic", schema::ResizeMethod_CUBIC},
+        };
+        return resize_mode[onnx_node_attr.strings(0)];
+      }();
+    } else if (attribute_name == "nearest_mode") {
+      attr->nearestMode = [&]() {
+        std::map<std::string, schema::NearestMode> nearest_mode = {
+          {"round_prefer_floor", schema::NearestMode_ROUND_HALF_DOWN},
+          {"round_prefer_ceil", schema::NearestMode_ROUND_HALF_UP},
+          {"floor", schema::NearestMode_FLOOR},
+          {"ceil", schema::NearestMode_CEIL},
+        };
+        return nearest_mode[onnx_node_attr.strings(0)];
+      }();
+    }
+  }
+
+  op->primitive->value.type = schema::PrimitiveType_Resize;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+OnnxNodeRegistrar g_onnxResizeParser("Resize", new OnnxResizeParser());
+}  // namespace lite
+}  // namespace mindspore

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

@@ -0,0 +1,33 @@
+/**
+ * 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_RESIZE_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_RESIZE_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 OnnxResizeParser : public OnnxNodeParser {
+ public:
+  OnnxResizeParser() : OnnxNodeParser("Resize") {}
+
+  STATUS Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *op) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_ONNX_RESIZE_PARSER_H

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

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#include <memory>
 #include "tools/converter/parser/onnx/onnx_upsample_parser.h"
+#include <memory>
 
 namespace mindspore {
 namespace lite {
@@ -42,9 +42,9 @@ STATUS OnnxUpsampleParser::Parse(const onnx::GraphProto &onnx_graph, const onnx:
     const auto &attribute_name = onnx_node_attr.name();
     if (attribute_name == "mode") {
       if ("nearest" == onnx_node_attr.s()) {
-        attr->method = schema::ResizeMethod_NEAREST_NEIGHBOR;
+        attr->method = schema::ResizeMethod_NEAREST;
       } else if ("bilinear" == onnx_node_attr.s()) {
-        attr->method = schema::ResizeMethod_BILINEAR;
+        attr->method = schema::ResizeMethod_LINEAR;
       } else {
         MS_LOG(ERROR) << "Resize do not support upsample mode";
         return RET_ERROR;

mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.cc

@@ -15,9 +15,9 @@
  */
 
 #include "tools/converter/parser/tflite/tflite_custom_parser.h"
-#include <vector>
-#include <memory>
 #include <map>
+#include <memory>
+#include <vector>
 #include "flatbuffers/flatbuffers.h"
 #include "flatbuffers/flexbuffers.h"
 
@@ -206,6 +206,8 @@ STATUS TfliteCustomParser::Parse(TfliteTensorsInfo *tensors_info, const std::uni
     status = ExtractFeatures(custom_attr, op, tflite_op);
   } else if (custom_type == "AudioSpectrogram") {
     status = AudioSpectrogram(custom_attr, op, tflite_op);
+  } else if (custom_type == "Mfcc") {
+    status = Mfcc(custom_attr, op, tflite_op);
   } else if (custom_type == "FlexRFFT") {
     status = Rfft(custom_attr, op, tflite_op, tflite_model);
   } else if (custom_type == "FlexReal") {

mindspore/lite/tools/converter/parser/tflite/tflite_resize_parser.cc

@@ -15,10 +15,10 @@
  */
 
 #include "tools/converter/parser/tflite/tflite_resize_parser.h"
-#include <vector>
+#include <map>
 #include <memory>
 #include <string>
-#include <map>
+#include <vector>
 
 namespace mindspore {
 namespace lite {
@@ -39,7 +39,7 @@ STATUS TfliteResizeParser::Parse(TfliteTensorsInfo *tensors_info, const std::uni
     MS_LOG(ERROR) << "new op failed";
     return RET_NULL_PTR;
   }
-
+  attr->coordinateTransformMode = schema::CoordinateTransformMode_COMMON;
   std::vector<std::string> node_name_str;
   Split(op->name.data(), &node_name_str, "-");
   const char *node_name = node_name_str.data()->c_str();
@@ -50,8 +50,16 @@ STATUS TfliteResizeParser::Parse(TfliteTensorsInfo *tensors_info, const std::uni
       MS_LOG(ERROR) << "get op: " << op->name.c_str() << " attr failed";
       return RET_NULL_PTR;
     }
-    attr->alignCorners = tfliteAttr->align_corners;
-    attr->method = schema::ResizeMethod_BILINEAR;
+    if (tfliteAttr->align_corners) {
+      attr->alignCorners = tfliteAttr->align_corners;
+      attr->coordinateTransformMode = schema::CoordinateTransformMode_ALIGN_CORNERS;
+    }
+    if (tfliteAttr->half_pixel_centers) {
+      attr->coordinateTransformMode = (attr->coordinateTransformMode == schema::CoordinateTransformMode_COMMON
+                                         ? schema::CoordinateTransformMode_TF_HALF_PIXEL
+                                         : schema::CoordinateTransformMode_ALIGN_CORNERS_WITH_HALF_PIEXL);
+    }
+    attr->method = schema::ResizeMethod_LINEAR;
   } else if (std::strcmp(node_name, "NearestNeighbor") == 0) {
     MS_LOG(DEBUG) << "parse TfliteResizeNearestNeighborParser";
     const auto &tfliteAttr = tflite_op->builtin_options.AsResizeNearestNeighborOptions();
@@ -59,8 +67,17 @@ STATUS TfliteResizeParser::Parse(TfliteTensorsInfo *tensors_info, const std::uni
       MS_LOG(ERROR) << "get op: " << op->name.c_str() << " attr failed";
       return RET_NULL_PTR;
     }
-    attr->alignCorners = tfliteAttr->align_corners;
-    attr->method = schema::ResizeMethod_NEAREST_NEIGHBOR;
+    if (tfliteAttr->align_corners) {
+      attr->alignCorners = tfliteAttr->align_corners;
+      attr->coordinateTransformMode = schema::CoordinateTransformMode_ALIGN_CORNERS;
+    }
+    if (tfliteAttr->half_pixel_centers) {
+      attr->coordinateTransformMode = (attr->coordinateTransformMode == schema::CoordinateTransformMode_COMMON
+                                         ? schema::CoordinateTransformMode_TF_HALF_PIXEL
+                                         : schema::CoordinateTransformMode_ALIGN_CORNERS_WITH_HALF_PIEXL);
+    }
+    attr->method = schema::ResizeMethod_NEAREST;
+    attr->nearestMode = schema::NearestMode_NORMAL;
   } else {
     MS_LOG(ERROR) << "wrong resize type";
     return RET_ERROR;