!9780 [MS][LITE]Add DepthwiseConv multiplier support, fix group conv int8

From: @gongdaguo
Reviewed-by: @zhang_xue_tong
Signed-off-by: @zhang_xue_tong

mindspore/lite/src/runtime/kernel/arm/int8/convolution_int8.cc

@@ -307,6 +307,12 @@ kernel::LiteKernel *CpuConvInt8KernelSelect(const std::vector<lite::Tensor *> &i
   return kernel;
 }
 
+void CopyTensorQuantParam(lite::Tensor *dst, lite::Tensor *src) {
+  for (size_t i = 0; i < src->quant_params().size(); i++) {
+    dst->AddQuantParam(src->quant_params().at(i));
+  }
+}
+
 kernel::LiteKernel *CpuGroupConvInt8KernelCreator(const std::vector<lite::Tensor *> &inputs,
                                                   const std::vector<lite::Tensor *> &outputs, OpParameter *op_parameter,
                                                   const InnerContext *ctx, const mindspore::lite::PrimitiveC *primitive,
@@ -359,6 +365,7 @@ kernel::LiteKernel *CpuGroupConvInt8KernelCreator(const std::vector<lite::Tensor
       MS_LOG(ERROR) << "create input tensor failed.";
       return nullptr;
     }
+    CopyTensorQuantParam(in_tensor, inputs[kInputIndex]);
     new_inputs.emplace_back(in_tensor);
 
     // create new weight
@@ -371,6 +378,7 @@ kernel::LiteKernel *CpuGroupConvInt8KernelCreator(const std::vector<lite::Tensor
       MS_LOG(ERROR) << "create filter tensor failed.";
       return nullptr;
     }
+    CopyTensorQuantParam(filter_tensor, inputs[kWeightIndex]);
     new_inputs.emplace_back(filter_tensor);
 
     // if has bias, create new bias
@@ -383,6 +391,7 @@ kernel::LiteKernel *CpuGroupConvInt8KernelCreator(const std::vector<lite::Tensor
         MS_LOG(ERROR) << "create bias_tensor failed.";
         return nullptr;
       }
+      CopyTensorQuantParam(bias_tensor, inputs[kBiasIndex]);
       new_inputs.emplace_back(bias_tensor);
     }
 
@@ -395,6 +404,7 @@ kernel::LiteKernel *CpuGroupConvInt8KernelCreator(const std::vector<lite::Tensor
         MS_LOG(ERROR) << "new out_tensor failed.";
         return nullptr;
       }
+      CopyTensorQuantParam(out_tensor, outputs[j]);
       new_outputs.emplace_back(out_tensor);
     }
     group_convs.emplace_back(CpuConvInt8KernelSelect(
@@ -412,6 +422,15 @@ kernel::LiteKernel *CpuConvInt8KernelCreator(const std::vector<lite::Tensor *> &
   MS_ASSERT(desc.type == schema::PrimitiveType_Conv2D);
   auto conv_param = reinterpret_cast<ConvParameter *>(opParameter);
   kernel::LiteKernel *kernel = nullptr;
+  if (primitive != nullptr && primitive->infer_flag()) {
+    conv_param->input_h_ = inputs.front()->Height();
+    conv_param->input_w_ = inputs.front()->Width();
+    conv_param->input_channel_ = inputs.front()->Channel();
+    conv_param->output_h_ = outputs.front()->Height();
+    conv_param->output_w_ = outputs.front()->Width();
+    conv_param->output_channel_ = outputs.front()->Channel();
+    conv_param->op_parameter_.thread_num_ = ctx->thread_num_;
+  }
   if (conv_param->group_ == 1) {
     kernel = CpuConvInt8KernelSelect(inputs, outputs, opParameter, ctx, primitive);
   } else {

mindspore/lite/test/models_onnx.cfg

@@ -8,6 +8,8 @@ mobilenetv2-7.onnx
 shufflenet-v2-10.onnx
 squeezenet1.1-7.onnx
 densenet-9.onnx
+ml_table_detection_fp32.onnx
+ml_table_segment.onnx
 googlenet-9.onnx
 inception-v1-9.onnx
 inception-v2-9.onnx

mindspore/lite/test/models_onnx_fp16.cfg

@@ -8,6 +8,8 @@ mobilenetv2-7.onnx 8
 shufflenet-v2-10.onnx 5
 squeezenet1.1-7.onnx 1
 densenet-9.onnx 6
+ml_table_detection_fp32.onnx 2
+ml_table_segment.onnx 2
 googlenet-9.onnx 3
 inception-v1-9.onnx 3
 inception-v2-9.onnx 4

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

@@ -59,18 +59,27 @@ bool GroupDepthwiseOpConvertPass::Run(const FuncGraphPtr &graph) {
       MS_LOG(DEBUG) << "the tensor's shape is dynamic.";
       return true;
     }
-    auto conv_attr = std::make_unique<schema::Conv2DT>();
-    if (conv_attr == nullptr) {
-      MS_LOG(ERROR) << "conv_attr is null";
+    auto weight_data_node = depthwise_cnode->input(kConvWeightIndex)->abstract();
+    if (weight_data_node == nullptr) {
+      MS_LOG(ERROR) << "the weight node input is invalid.";
       return false;
     }
-
-    if (data_shape[3] == 1) {
+    auto weight_shape = utils::cast<abstract::ShapePtr>(weight_data_node->GetShapeTrack())->shape();
+    if (weight_shape.empty()) {
+      MS_LOG(DEBUG) << "the weight's shape is dynamic.";
+      return true;
+    }
+    if ((data_shape[3] == 1) || (data_shape[3] != weight_shape[3])) {
+      auto conv_attr = std::make_unique<schema::Conv2DT>();
+      if (conv_attr == nullptr) {
+        MS_LOG(ERROR) << "conv_attr is null";
+        return false;
+      }
       conv_attr->channelIn = data_shape[3];
-      conv_attr->channelOut = conv_attr->channelIn * attr->channelMultiplier;
+      conv_attr->channelOut = weight_shape[3];
 
       // update attr
-      conv_attr->group = 1;
+      conv_attr->group = data_shape[3];
       conv_attr->format = attr->format;
       conv_attr->kernelH = attr->kernelH;
       conv_attr->kernelW = attr->kernelW;