Fix DeDepthwiseConv2D problem

mindspore/lite/tools/converter/legacy_optimizer/node/weight_format_pass.cc

@@ -79,7 +79,7 @@ int WeightFormatPass::ShapeFormatTrans(GraphNode *graphNode) {
     switch (node->quantType) {
       case QuantType_QUANT_NONE: {
         if (opType == schema::PrimitiveType_Conv2D || opType == schema::PrimitiveType_DepthwiseConv2D ||
-            opType == schema::PrimitiveType_DeConv2D) {
+            opType == schema::PrimitiveType_DeConv2D || opType == schema::PrimitiveType_DeDepthwiseConv2D) {
           weightTensor->format = schema::Format_KCHW;
         } else {
           MS_LOG(ERROR) << "Invalid opType: " << schema::EnumNamePrimitiveType(opType)
@@ -240,11 +240,11 @@ int WeightFormatPass::QuantDataFormatTrans(GraphNode *graphNode) {
       }
     } else if (weightTensor->format == schema::Format_KHWC) {  // from onnx
       return RET_OK;
-//      if (weightTensor->dataType == kNumberTypeInt8) {                     // DataType_DT_UINT8) {
-//        status = TransFilterFormat<int8_t>(weightTensor.get(), kKHWC2HWCK);
-//      } else {
-//        status = TransFilterFormat<float>(weightTensor.get(), kKHWC2HWCK);
-//      }
+      //      if (weightTensor->dataType == kNumberTypeInt8) {                     // DataType_DT_UINT8) {
+      //        status = TransFilterFormat<int8_t>(weightTensor.get(), kKHWC2HWCK);
+      //      } else {
+      //        status = TransFilterFormat<float>(weightTensor.get(), kKHWC2HWCK);
+      //      }
     } else if (weightTensor->format == schema::Format_HWCK) {  // from tf
       return 0;
     } else {
@@ -275,7 +275,7 @@ int WeightFormatPass::QuantDataFormatTrans(GraphNode *graphNode) {
     } else if (weightTensor->format == schema::Format_HWCK) {  // from tf
       return 0;
     } else if (weightTensor->format == schema::Format_CHWK) {  // from onnx
-      if (weightTensor->dataType == kNumberTypeInt8) {                     // DataType_DT_UINT8) {
+      if (weightTensor->dataType == kNumberTypeInt8) {         // DataType_DT_UINT8) {
         status = TransFilterFormat<int8_t>(weightTensor.get(), kCHWK2KHWC);
       } else {
         status = TransFilterFormat<float>(weightTensor.get(), kCHWK2HWCK);
@@ -383,9 +383,11 @@ int WeightFormatPass::NonQuantDataFormatTrans(GraphNode *graphNode) {
       MS_LOG(WARNING) << "TransFilter HWKCToKCHW failed, node : " << node->name.c_str();
       // todo(00445839): consider varible weight condition
     }
-  } else if (opType == schema::PrimitiveType_DeDepthwiseConv2D) {  // weight should be CKHW
-    if (weightTensor->format == schema::Format_CKHW) {             // from caffe
+  } else if (opType == schema::PrimitiveType_DeDepthwiseConv2D) {  // weight should be KHWC
+    if (weightTensor->format == schema::Format_KHWC) {
       return 0;
+    } else if (weightTensor->format == schema::Format_KCHW) {  // from caffe
+      status = TransFilterFormat<float>(weightTensor.get(), kKCHW2KHWC);
     } else if (weightTensor->format == schema::Format_HWKC) {  // from tf or onnx
       status = TransFilterFormat<float>(weightTensor.get(), kHWKC2CKHW);
     } else {
@@ -393,7 +395,7 @@ int WeightFormatPass::NonQuantDataFormatTrans(GraphNode *graphNode) {
       return -1;
     }
     if (status == 0) {
-      node->primitive->value.AsDepthwiseConv2D()->format = schema::Format_NHWC;
+      node->primitive->value.AsDeDepthwiseConv2D()->format = schema::Format_NHWC;
       weightTensor->format = schema::Format_CKHW;
     } else {
       MS_LOG(WARNING) << "TransFilter HWKCToCKHW failed, node : " << node->name.c_str();

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

@@ -46,14 +46,13 @@ void CaffeDeconvolutionParser::ParseGroupDeconvolution(schema::CNodeT *op, schem
   deDepthwiseConv2DParam->hasBias = attr->hasBias;
   deDepthwiseConv2DParam->activationType = attr->activationType;
   delete attr;
-  op->primitive = std::make_unique<schema::PrimitiveT>();
   op->primitive->value.type = schema::PrimitiveType_DeDepthwiseConv2D;
   op->primitive->value.value = deDepthwiseConv2DParam.release();
 }
 STATUS CaffeDeconvolutionParser::Parse(const caffe::LayerParameter &proto, const caffe::LayerParameter &weight,
                                        schema::CNodeT *op, std::vector<schema::TensorT *> *weightVec) {
   op->name = proto.name();
-  schema::DeConv2DT *attr = new schema::DeConv2DT();
+  auto *attr = new schema::DeConv2DT();
   attr->format = schema::Format_NCHW;
   const caffe::ConvolutionParameter convParam = proto.convolution_param();