!6434 MSLITE fix equal/gather infershape and onnx deconv parser

Merge pull request !6434 from 徐安越/master

mindspore/lite/schema/ops.fbs

@@ -103,9 +103,10 @@ enum EltwiseMode : byte {
 
 enum PadMode : byte {
     NOTSET = 0,
-    SAME = 1,
+    SAME_UPPER = 1,
     VALID = 2,
-    CAFFE = 4
+    CAFFE = 4,
+    SAME_LOWER = 5
 }
 
 enum RoundMode : byte {

mindspore/lite/src/lite_session.cc

@@ -351,7 +351,7 @@ int LiteSession::Init(Context *context) {
     }
   }
 #endif
-  executor = new Executor();
+  executor = new(std::nothrow) Executor();
   if (nullptr == executor) {
     MS_LOG(ERROR) << "New Executor failed";
     is_running_.store(false);

mindspore/lite/src/ops/conv2d.cc

@@ -156,7 +156,7 @@ void Conv2D::PopulaterConv2DMultiGroup(const Primitive &prim, schema::PrimitiveT
   if (pad_mode == "valid") {
     attr->padMode = schema::PadMode_VALID;
   } else if (pad_mode == "same") {
-    attr->padMode = schema::PadMode_SAME;
+    attr->padMode = schema::PadMode_SAME_UPPER;
   } else {
     attr->padMode = schema::PadMode_NOTSET;
   }
@@ -221,7 +221,7 @@ void Conv2D::PopulaterConv2DSingleGroup(const Primitive &prim, schema::Primitive
   if (pad_mode == "valid") {
     attr->padMode = schema::PadMode_VALID;
   } else if (pad_mode == "same") {
-    attr->padMode = schema::PadMode_SAME;
+    attr->padMode = schema::PadMode_SAME_UPPER;
   } else {
     attr->padMode = schema::PadMode_NOTSET;
   }
@@ -233,8 +233,6 @@ void Conv2D::PopulaterConv2DSingleGroup(const Primitive &prim, schema::Primitive
     attr->activationType = schema::ActivationType_NO_ACTIVATION;
   }
 
-  //  attr->padMode = schema::PadMode_SAME;
-  //  attr->activationType = schema::ActivationType_RELU;
   primitive->value.type = schema::PrimitiveType_Conv2D;
   primitive->value.value = attr.release();
 }
@@ -319,7 +317,7 @@ void Conv2D::ConvInferShape(int input_h, int input_w, int *output_h, int *output
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
 
-  if (GetPadMode() == schema::PadMode_SAME) {
+  if (GetPadMode() == schema::PadMode_SAME_UPPER) {
     *output_w = std::ceil(static_cast<float>(input_w) / static_cast<float>(stride_w));
     *output_h = std::ceil(static_cast<float>(input_h) / static_cast<float>(stride_h));
     auto pad_h_all = ((*output_h - 1) * stride_h + (kernel_h - 1) * dilate_h + 1 - input_h);

mindspore/lite/src/ops/conv2d_grad_filter.cc

@@ -119,7 +119,7 @@ int Conv2DGradFilter::UnPackAttr(const Primitive &prim, const std::vector<AnfNod
     if (pad_mode == "valid") {
       attr->padMode = schema::PadMode_VALID;
     } else if (pad_mode == "same") {
-      attr->padMode = schema::PadMode_SAME;
+      attr->padMode = schema::PadMode_SAME_UPPER;
     } else {
       attr->padMode = schema::PadMode_NOTSET;
     }

mindspore/lite/src/ops/conv2d_grad_input.cc

@@ -118,7 +118,7 @@ int Conv2DGradInput::UnPackAttr(const Primitive &prim, const std::vector<AnfNode
     if (pad_mode == "valid") {
       attr->padMode = schema::PadMode_VALID;
     } else if (pad_mode == "same") {
-      attr->padMode = schema::PadMode_SAME;
+      attr->padMode = schema::PadMode_SAME_UPPER;
     } else {
       attr->padMode = schema::PadMode_NOTSET;
     }

mindspore/lite/src/ops/deconv2d.cc

@@ -93,7 +93,7 @@ void DeConv2D::PopulaterDeConv2DSingleGroup(const Primitive &prim, schema::Primi
   if (pad_mode == "valid" || pad_mode == "VALID") {
     attr->padMode = schema::PadMode_VALID;
   } else if (pad_mode == "same" || pad_mode == "SAME") {
-    attr->padMode = schema::PadMode_SAME;
+    attr->padMode = schema::PadMode_SAME_UPPER;
   } else {
     attr->padMode = schema::PadMode_NOTSET;
   }
@@ -105,8 +105,6 @@ void DeConv2D::PopulaterDeConv2DSingleGroup(const Primitive &prim, schema::Primi
     attr->activationType = schema::ActivationType_NO_ACTIVATION;
   }
 
-  //  attr->padMode = schema::PadMode_SAME;
-  //  attr->activationType = schema::ActivationType_RELU;
   primitive->value.type = schema::PrimitiveType_DeConv2D;
   primitive->value.value = attr.release();
 }
@@ -206,10 +204,10 @@ int DeConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
   auto pad_mode = (schema::PadMode)GetPadMode();
-  if (pad_mode == schema::PadMode_CAFFE) {
+  if (pad_mode == schema::PadMode_CAFFE || pad_mode == schema::PadMode_NOTSET) {
     output_h = (input_h - 1) * stride_h + ((kernel_h - 1) * dilate_h + 1) - pad_u_ - pad_d_;
     output_w = (input_w - 1) * stride_w + ((kernel_w - 1) * dilate_w + 1) - pad_l_ - pad_r_;
-  } else if (pad_mode == schema::PadMode_SAME) {
+  } else if (pad_mode == schema::PadMode_SAME_UPPER) {
     output_h = input_h * stride_h;
     output_w = input_w * stride_w;
   } else if (pad_mode == schema::PadMode_VALID) {
@@ -222,13 +220,13 @@ int DeConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::
   std::vector<int> out_shape = {output_n, output_h, output_w, output_c};
   output->set_shape(out_shape);
 
-  if (pad_mode == schema::PadMode_SAME) {
+  if (pad_mode == schema::PadMode_SAME_UPPER) {
     pad_u_ = ((input_h - 1) * stride_h + (kernel_h - 1) * dilate_h + 1 - output_h) / 2;
     pad_l_ = ((input_w - 1) * stride_w + (kernel_w - 1) * dilate_w + 1 - output_w) / 2;
   } else if (pad_mode == schema::PadMode_VALID) {
     pad_u_ = 0;
     pad_l_ = 0;
-  } else if (pad_mode == schema::PadMode_CAFFE) {
+  } else if (pad_mode == schema::PadMode_CAFFE || pad_mode == schema::PadMode_NOTSET) {
   } else {
     MS_LOG(ERROR) << "unsupported pad mode for deconv";
     return RET_ERROR;

mindspore/lite/src/ops/depthwise_conv2d.cc

@@ -104,7 +104,7 @@ int DepthwiseConv2D::UnPackAttr(const Primitive &prim, const std::vector<AnfNode
   if (pad_mode == "valid") {
     attr->padMode = schema::PadMode_VALID;
   } else if (pad_mode == "same") {
-    attr->padMode = schema::PadMode_SAME;
+    attr->padMode = schema::PadMode_SAME_UPPER;
   } else {
     attr->padMode = schema::PadMode_NOTSET;
   }
@@ -114,8 +114,6 @@ int DepthwiseConv2D::UnPackAttr(const Primitive &prim, const std::vector<AnfNode
   } else {
     attr->activationType = schema::ActivationType_NO_ACTIVATION;
   }
-  //  attr->padMode = schema::PadMode_SAME;
-  //  attr->activationType = schema::ActivationType_RELU;
   auto channel_multiplier = GetValue<int>(prim.GetAttr("channel_multiplier"));
   attr->channelMultiplier = channel_multiplier;
 
@@ -220,7 +218,7 @@ int DepthwiseConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vector
   pad_u_ = GetPadUp();
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
-  if (GetPadMode() == schema::PadMode_SAME) {
+  if (GetPadMode() == schema::PadMode_SAME_UPPER) {
     output_h = std::ceil(static_cast<float>(input_h) / static_cast<float>(GetStrideH()));
     output_w = std::ceil(static_cast<float>(input_w) / static_cast<float>(GetStrideW()));
     auto pad_h_all = ((output_h - 1) * GetStrideH() + (GetKernelH() - 1) * GetDilateH() + 1 - input_h);

mindspore/lite/src/ops/equal.cc

@@ -29,5 +29,15 @@ int Equal::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::
 }
 
 #endif
+int Equal::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outputs_) {
+  auto input = inputs_.front();
+  MS_ASSERT(input != nullptr);
+  auto output = outputs_.front();
+  MS_ASSERT(output != nullptr);
+  output->set_shape(input->shape());
+  output->set_data_type(TypeId::kNumberTypeUInt8);
+  output->SetFormat(input->GetFormat());
+  return RET_OK;
+}
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/ops/equal.h

@@ -36,6 +36,7 @@ class Equal : public Arithmetic {
 
   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

mindspore/lite/src/ops/gather.cc

@@ -91,7 +91,7 @@ int Gather::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outp
   std::vector<int> out_shape{in_shape};
   out_shape.erase(out_shape.begin() + axis);
   for (int i = indices_rank - 1; i >= 0; --i) {
-    out_shape.insert(out_shape.begin() + axis + i, indices_shape[i]);
+    out_shape.insert(out_shape.begin() + axis, indices_shape[i]);
   }
   output->set_shape(out_shape);
   return RET_OK;

mindspore/lite/src/ops/pooling.cc

@@ -101,7 +101,7 @@ int Pooling::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &in
     if (pad_mode == "VALID") {
       attr->padMode = schema::PadMode_VALID;
     } else if (pad_mode == "SAME") {
-      attr->padMode = schema::PadMode_SAME;
+      attr->padMode = schema::PadMode_SAME_UPPER;
     } else {
       attr->padMode = schema::PadMode_NOTSET;
     }
@@ -192,7 +192,7 @@ int Pooling::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> out
   pad_u_ = GetPadUp();
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
-  if (GetPadMode() == schema::PadMode_SAME) {
+  if (GetPadMode() == schema::PadMode_SAME_UPPER) {
     output_w = std::ceil(static_cast<float>(input_w) / static_cast<float>(GetStrideW()));
     output_h = std::ceil(static_cast<float>(input_h) / static_cast<float>(GetStrideH()));
     auto pad_h_all = ((output_h - 1) * GetStrideH() + (window_h - 1) + 1 - input_h);

mindspore/lite/src/ops/pooling_grad.cc

@@ -90,7 +90,7 @@ int PoolingGrad::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr>
     if (pad_mode == "VALID") {
       attr->padMode = schema::PadMode_VALID;
     } else if (pad_mode == "SAME") {
-      attr->padMode = schema::PadMode_SAME;
+      attr->padMode = schema::PadMode_SAME_UPPER;
     } else {
       attr->padMode = schema::PadMode_NOTSET;
     }
@@ -162,7 +162,7 @@ int PoolingGrad::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *>
   pad_u_ = GetPadUp();
   pad_d_ = GetPadDown();
   pad_r_ = GetPadRight();
-  if (GetPadMode() == schema::PadMode_SAME) {
+  if (GetPadMode() == schema::PadMode_SAME_UPPER) {
     int output_w = std::ceil(static_cast<float>(input_w) / static_cast<float>(GetStrideW()));
     int output_h = std::ceil(static_cast<float>(input_h) / static_cast<float>(GetStrideH()));
     auto pad_h_all = ((output_h - 1) * GetStrideH() + (window_h - 1) + 1 - input_h);

mindspore/lite/test/ut/src/infer_test.cc

@@ -42,7 +42,7 @@ TEST_F(InferTest, TestConvNode) {
   node->primitive = std::make_unique<schema::PrimitiveT>();
   node->primitive->value.type = schema::PrimitiveType_Conv2D;
   auto primitive = new schema::Conv2DT;
-  primitive->padMode = schema::PadMode_SAME;
+  primitive->padMode = schema::PadMode_SAME_UPPER;
   primitive->channelIn = 3;
   primitive->channelOut = 32;
   primitive->format = schema::Format_NHWC;
@@ -231,18 +231,6 @@ TEST_F(InferTest, TestAddNode) {
   ASSERT_EQ(TypeId::kNumberTypeFloat32, outTensor->data_type());
   auto *outData = reinterpret_cast<float *>(outTensor->MutableData());
   ASSERT_NE(nullptr, outData);
-  // //===================================================
-  // size_t output_size;
-  // std::string output_path = "./convfp32_out_1_28_28_32.bin";
-  // ReadFile(output_path.c_str(), &output_size, buf);
-  // ASSERT_NE(nullptr, buf[0]);
-  // auto output_data = reinterpret_cast<float *>(buf[0]);
-  // ASSERT_NE(nullptr, output_data);
-  // //===================================================
-  // ASSERT_EQ(output_size, outTensor->Size());
-  // for (size_t i = 0; i < outTensor->ElementsNum(); i++) {
-  //   ASSERT_EQ(output_data[i], outData[i]);
-  // }
   MS_LOG(INFO) << "Passed";
 }
 
@@ -366,141 +354,4 @@ TEST_F(InferTest, TestModel) {
   auto outputs = session->GetOutputs();
   MS_LOG(INFO) << "Passed";
 }
-
-// TEST_F(TrainTest, TestMultiNode) {
-//  auto msGraph = std::make_shared<schema::GraphDefT>();
-//  msGraph->name = "graph";
-//  auto msSubgraph = std::make_unique<schema::SubGraphDefT>();
-//  msSubgraph->name = "subGraph";
-//
-//  auto conv = std::make_unique<schema::OpDefT>();
-//  conv->inputIndex = {0, 1};
-//  conv->outputIndex = {2};
-//  conv->attr.type = schema::OpT_Conv2D;
-//  auto conv_attr = new schema::Conv2DT;
-//  conv_attr->padMode = schema::PadMode_SAME;
-//  conv_attr->format = schema::Format_NHWC;
-//  conv_attr->strideH = 1;
-//  conv_attr->strideW = 1;
-//  conv_attr->kernelH = 3;
-//  conv_attr->kernelW = 3;
-//  conv_attr->dilateH = 1;
-//  conv_attr->dilateW = 1;
-//
-//  conv->attr.value = conv_attr;
-//  conv->name = "Conv2D";
-//  conv->fmkType = schema::FmkType_CAFFE;
-//  msSubgraph->nodes.emplace_back(std::move(conv));
-//
-//  auto matMul1 = std::make_unique<schema::OpDefT>();
-//  matMul1->inputIndex = {2, 3};
-//  matMul1->outputIndex = {4};
-//  matMul1->attr.type = schema::OpT_MatMul;
-//  auto matMul_attr1 = new schema::MatMulT;
-//  matMul_attr1->transposeA = false;
-//  matMul_attr1->transposeB = true;
-//  matMul1->attr.value = matMul_attr1;
-//  matMul1->name = "matmul1";
-//  matMul1->fmkType = schema::FmkType_CAFFE;
-//  msSubgraph->nodes.emplace_back(std::move(matMul1));
-//
-//  auto matMul2 = std::make_unique<schema::OpDefT>();
-//  matMul2->inputIndex = {4, 5};
-//  matMul2->outputIndex = {6};
-//  matMul2->attr.type = schema::OpT_MatMul;
-//  auto matMul_attr2 = new schema::MatMulT;
-//  matMul_attr2->transposeA = false;
-//  matMul_attr2->transposeB = true;
-//  matMul2->attr.value = matMul_attr2;
-//  matMul2->name = "matmul2";
-//  matMul2->fmkType = schema::FmkType_CAFFE;
-//  msSubgraph->nodes.emplace_back(std::move(matMul2));
-//
-//  msSubgraph->inputIndex = {0};
-//  msSubgraph->outputIndex = {6};
-//
-//  auto input0 = std::make_unique<schema::TensorDefT>();
-//  input0->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
-//  input0->format = schema::Format_NHWC;
-//  input0->dataType = TypeId::kNumberTypeFloat32;
-//  input0->dims = {1, 5, 5, 3};
-//  input0->offset = -1;
-//  msSubgraph->allTensors.emplace_back(std::move(input0));
-//
-//  auto conv_weight = std::make_unique<schema::TensorDefT>();
-//  conv_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
-//  conv_weight->format = schema::Format_KHWC;
-//  conv_weight->dataType = TypeId::kNumberTypeFloat32;
-//  conv_weight->dims = {8, 3, 3, 3};
-//  conv_weight->data.resize(8*3*3*3*sizeof(float));
-//  msSubgraph->allTensors.emplace_back(std::move(conv_weight));
-//
-//  auto conv_output = std::make_unique<schema::TensorDefT>();
-//  conv_output->refCount = 0;
-//  conv_output->format = schema::Format_NHWC;
-//  conv_output->dataType = TypeId::kNumberTypeFloat32;
-//  conv_output->dims = {1, 5, 5, 8};
-//  msSubgraph->allTensors.emplace_back(std::move(conv_output));
-//
-//  auto add_weight = std::make_unique<schema::TensorDefT>();
-//  add_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
-//  add_weight->format = schema::Format_NHWC;
-//  add_weight->dataType = TypeId::kNumberTypeFloat32;
-//  add_weight->dims = {1, 5, 5, 8};
-//  add_weight->data.resize(5*5*8*sizeof(float));
-//  msSubgraph->allTensors.emplace_back(std::move(add_weight));
-//
-//  auto add_output = std::make_unique<schema::TensorDefT>();
-//  add_output->refCount = 0;
-//  add_output->format = schema::Format_NHWC;
-//  add_output->dataType = TypeId::kNumberTypeFloat32;
-//  add_output->dims = {1, 5, 5, 8};
-//  msSubgraph->allTensors.emplace_back(std::move(add_output));
-//
-//  auto mul_weight = std::make_unique<schema::TensorDefT>();
-//  mul_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
-//  mul_weight->format = schema::Format_NHWC;
-//  mul_weight->dataType = TypeId::kNumberTypeFloat32;
-//  mul_weight->dims = {1, 5, 5, 8};
-//  mul_weight->data.resize(5*5*8*sizeof(float));
-//  msSubgraph->allTensors.emplace_back(std::move(mul_weight));
-//
-//  auto mul_output = std::make_unique<schema::TensorDefT>();
-//  mul_output->refCount = 0;
-//  mul_output->format = schema::Format_NHWC;
-//  mul_output->dataType = TypeId::kNumberTypeFloat32;
-//  mul_output->dims = {1, 5, 5, 8};
-//  msSubgraph->allTensors.emplace_back(std::move(mul_output));
-//  msGraph->subgraphs.emplace_back(std::move(msSubgraph));
-//
-//  flatbuffers::FlatBufferBuilder builder(1024);
-//  auto offset = schema::GraphDef::Pack(builder, msGraph.get());
-//  builder.Finish(offset);
-//  size_t size = builder.GetSize();
-//  const char *content = (char *)builder.GetBufferPointer();
-//  const std::string strstub = "";
-//
-//  auto func_graph = inference::LoadModel(content, size, strstub);
-//  ASSERT_NE(nullptr, func_graph);
-//  auto session = inference::MSSession::CreateSession(kCPUDevice, 0);
-//  ASSERT_NE(nullptr, session);
-//  auto graphId = session->CompileGraph(func_graph);
-//
-//  auto inTensor =
-//    std::shared_ptr<inference::MSTensor>(inference::MSTensor::CreateTensor(TypeId::kNumberTypeFloat32, {1, 5, 5, 3}));
-//  ASSERT_NE(nullptr, inTensor);
-//  ASSERT_EQ(sizeof(float) * (5 * 5 * 3), inTensor->Size());
-//  (void)inTensor->MutableData();
-//
-//  std::vector<std::shared_ptr<inference::MSTensor>> inputs;
-//  inputs.emplace_back(inTensor);
-//  auto outputs = session->RunGraph(graphId, inputs);
-//  ASSERT_EQ(1, outputs.size());
-//  ASSERT_EQ(1, outputs.front().size());
-//  auto runOutput = outputs.front().front();
-//  ASSERT_NE(nullptr, runOutput);
-//  ASSERT_EQ(5 * 5 * 8, runOutput->ElementsNum());
-//  ASSERT_EQ(TypeId::kNumberTypeFloat32, runOutput->data_type());
-//  MS_LOG(INFO) << "Passed";
-//}
 }  // namespace mindspore

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

@@ -46,7 +46,7 @@ TEST_F(TestTfliteParserConv, AttrValue) {
   ASSERT_EQ(val->strideW, 1);
   ASSERT_EQ(val->dilateH, 1);
   ASSERT_EQ(val->dilateW, 1);
-  ASSERT_EQ(val->padMode, schema::PadMode_SAME);
+  ASSERT_EQ(val->padMode, schema::PadMode_SAME_UPPER);
   ASSERT_EQ(val->padUp, 1);
   ASSERT_EQ(val->padDown, 1);
   ASSERT_EQ(val->padLeft, 1);

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

@@ -47,7 +47,7 @@ TEST_F(TestTfliteParserDeConv, AttrValue) {
   ASSERT_EQ(val->strideW, 1);
   ASSERT_EQ(val->dilateH, 1);
   ASSERT_EQ(val->dilateW, 1);
-  ASSERT_EQ(val->padMode, schema::PadMode_SAME);
+  ASSERT_EQ(val->padMode, schema::PadMode_SAME_UPPER);
   ASSERT_EQ(val->padUp, 1);
   ASSERT_EQ(val->padDown, 1);
   ASSERT_EQ(val->padLeft, 1);

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

@@ -46,7 +46,7 @@ TEST_F(TestTfliteParserDepthwiseConv1, AttrValue) {
   ASSERT_EQ(val->strideW, 1);
   ASSERT_EQ(val->dilateH, 1);
   ASSERT_EQ(val->dilateW, 1);
-  ASSERT_EQ(val->padMode, schema::PadMode_SAME);
+  ASSERT_EQ(val->padMode, schema::PadMode_SAME_UPPER);
   ASSERT_EQ(val->padUp, 1);
   ASSERT_EQ(val->padDown, 1);
   ASSERT_EQ(val->padLeft, 1);
@@ -80,7 +80,7 @@ TEST_F(TestTfliteParserDepthwiseConv2, AttrValue) {
   ASSERT_EQ(val->strideW, 1);
   ASSERT_EQ(val->dilateH, 1);
   ASSERT_EQ(val->dilateW, 1);
-  ASSERT_EQ(val->padMode, schema::PadMode_SAME);
+  ASSERT_EQ(val->padMode, schema::PadMode_SAME_UPPER);
   ASSERT_EQ(val->padUp, 1);
   ASSERT_EQ(val->padDown, 1);
   ASSERT_EQ(val->padLeft, 1);

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

@@ -73,7 +73,7 @@ TEST_F(TestTfliteParserAvgPooling, AttrValue) {
   ASSERT_EQ(val->windowH, 2);
   ASSERT_EQ(val->strideW, 1);
   ASSERT_EQ(val->strideH, 1);
-  ASSERT_EQ(val->padMode, schema::PadMode_SAME);
+  ASSERT_EQ(val->padMode, schema::PadMode_SAME_UPPER);
   ASSERT_EQ(val->padUp, 0);
   ASSERT_EQ(val->padDown, 1);
   ASSERT_EQ(val->padLeft, 0);

mindspore/lite/test/ut/tools/optimizer/fusion/conv_activation_fusion_test.cc

@@ -42,7 +42,7 @@ CNodeTptr BuildConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
   auto prim1 = new schema::Conv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;
@@ -62,7 +62,7 @@ CNodeTptr BuildDepthwiseConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
   auto prim1 = new schema::DepthwiseConv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;

mindspore/lite/test/ut/tools/optimizer/fusion/conv_biasadd_fusion_test.cc

@@ -42,7 +42,7 @@ CNodeTptr BuildConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
   auto prim1 = new schema::Conv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;
@@ -62,7 +62,7 @@ CNodeTptr BuildDepthwiseConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
   auto prim1 = new schema::DepthwiseConv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;

mindspore/lite/test/ut/tools/optimizer/fusion/conv_bn_fusion_test.cc

@@ -42,7 +42,7 @@ CNodeTptr BuildConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
   auto prim1 = new schema::Conv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;
@@ -62,7 +62,7 @@ CNodeTptr BuildDepthwiseConv2D() {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
   auto prim1 = new schema::DepthwiseConv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;

mindspore/lite/test/ut/tools/optimizer/fusion/conv_scale_fusion_test.cc

@@ -48,7 +48,7 @@ CNodeTptr BuildConv2D(int with_bias_flag) {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_Conv2D;
   auto prim1 = new schema::Conv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;
@@ -74,7 +74,7 @@ CNodeTptr BuildDepthwiseConv2D(int with_bias_flag) {
   convNode->primitive = std::make_unique<schema::PrimitiveT>();
   convNode->primitive->value.type = schema::PrimitiveType_DepthwiseConv2D;
   auto prim1 = new schema::DepthwiseConv2DT;
-  prim1->padMode = schema::PadMode_SAME;
+  prim1->padMode = schema::PadMode_SAME_UPPER;
   prim1->format = schema::Format_NHWC;
   prim1->strideH = 1;
   prim1->strideW = 1;

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

@@ -71,6 +71,13 @@ STATUS OnnxDeConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::N
     return RET_NULL_PTR;
   }
 
+  // set default params
+  attr->padMode = schema::PadMode_NOTSET;
+  attr->group = 1;
+  attr->strideW = 1;
+  attr->strideH = 1;
+  attr->dilateW = 1;
+  attr->dilateH = 1;
   // set opdef each attr params
   for (const auto &onnx_node_attr : onnx_node.attribute()) {
     if (onnx_node_attr.name() == "group") {
@@ -121,6 +128,9 @@ STATUS OnnxDeConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::N
         MS_LOG(ERROR) << "Unsupported format: " << onnx_node_attr.s().c_str();
         return RET_ERROR;
       }
+    } else if (onnx_node_attr.name() == "output_padding") {
+      MS_LOG(ERROR) << "output_padding param hasn't been supported";
+      return RET_NOT_SUPPORT;
     }
   }
 

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

@@ -22,8 +22,10 @@ namespace lite {
 schema::PadMode OnnxNodeParser::GetOnnxPadMode(const onnx::AttributeProto &onnx_node_attr) {
   if (onnx_node_attr.s() == "NOTSET") {
     return schema::PadMode_NOTSET;
-  } else if (onnx_node_attr.s() == "SAME_UPPER" || onnx_node_attr.s() == "SAME_LOWER") {
-    return schema::PadMode_SAME;
+  } else if (onnx_node_attr.s() == "SAME_UPPER") {
+    return schema::PadMode_SAME_UPPER;
+  } else if (onnx_node_attr.s() == "SAME_LOWER") {
+    return schema::PadMode_SAME_LOWER;
   } else if (onnx_node_attr.s() == "VALID") {
     return schema::PadMode_VALID;
   } else {

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

@@ -49,6 +49,9 @@ STATUS TfliteCastParser::Parse(const std::unique_ptr<tflite::OperatorT> &tflite_
     return RET_NULL_PTR;
   }
   attr->srcT = GetTfliteDataType(in_tensor->type);
+  if (attr->srcT == TypeId::kNumberTypeBool) {
+    attr->srcT = TypeId::kNumberTypeUInt8;
+  }
   const auto &out_tensor = tflite_tensors[tflite_op->outputs[0]];
   if (out_tensor == nullptr) {
     MS_LOG(ERROR) << "tensor is null";

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

@@ -18,6 +18,7 @@
 #include <vector>
 #include <memory>
 #include <map>
+#include <string>
 
 namespace mindspore {
 namespace lite {

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

@@ -157,7 +157,7 @@ TypeId GetTfliteDataType(const tflite::TensorType &tflite_data_type) {
 
 schema::PadMode GetPadMode(tflite::Padding tflite_padmode) {
   if (tflite_padmode == tflite::Padding_SAME) {
-    return schema::PadMode_SAME;
+    return schema::PadMode_SAME_UPPER;
   } else if (tflite_padmode == tflite::Padding_VALID) {
     return schema::PadMode_VALID;
   } else {
@@ -198,7 +198,7 @@ STATUS getPaddingParam(const std::unique_ptr<tflite::TensorT> &tensor, schema::P
   int padDown = 0;
   int padLeft = 0;
   int padRight = 0;
-  if (pad_mode == schema::PadMode_SAME) {
+  if (pad_mode == schema::PadMode_SAME_UPPER) {
     auto shape = tensor->shape;
     int H_input = shape.at(1);
     int W_input = shape.at(2);