Fixed MobileNetV3 Quantized Model for RKNN (#1390)

* update * update
PaddlePaddle · Sep 20, 2024 · 48fa34e · 48fa34e
1 parent 2f9ad56
commit 48fa34e
Showing 1 changed file with 20 additions and 12 deletions.
diff --git a/paddle2onnx/mapper/quantize/rknn_quantize_processor.cc b/paddle2onnx/mapper/quantize/rknn_quantize_processor.cc
@@ -90,23 +90,33 @@ void RKNNQuantizeProcessor::AddQDQ() {
         if (helper_->quantize_info.find(name) != helper_->quantize_info.end()) {
           continue;
         }
-        std::vector<float> matmul_weight;
-        if (!GetTensorByName(name, &matmul_weight)) {
+        std::vector<float> weight_data;
+        if (!GetTensorByName(name, &weight_data)) {
           continue;
         }
-        std::vector<int64_t> matmul_weight_shape;
-        if (!GetTensorShape(name, &matmul_weight_shape)) {
+        std::vector<int64_t> weight_shape;
+        if (!GetTensorShape(name, &weight_shape)) {
           continue;
         }
+
         int64_t quantize_axis = 1;
         std::vector<float> scale;
         std::vector<int64_t> zeros;
-        GetChannelWiseQuantizeInfo(matmul_weight, matmul_weight_shape,
-                                   quantize_axis, &scale, &zeros);
-        auto scale_node =
-            helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale);
-        auto zero_node =
-            helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, zeros);
+        std::string scale_node, zero_node;
+        if (weight_shape.size() <= 1) {
+          GetTensorWiseQuantizeInfo(weight_data, &scale, &zeros);
+          scale_node = helper_->Constant({}, ONNX_NAMESPACE::TensorProto::FLOAT,
+                                         scale[0]);
+          zero_node = helper_->Constant({}, ONNX_NAMESPACE::TensorProto::INT8,
+                                        zeros[0]);
+        } else {
+          GetChannelWiseQuantizeInfo(weight_data, weight_shape, quantize_axis,
+                                     &scale, &zeros);
+          scale_node =
+              helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale);
+          zero_node =
+              helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, zeros);
+        }
         QuantizeInfo matmul_weight_quantize_info(scale, zeros, scale_node,
                                                  zero_node, quantize_axis);
         helper_->quantize_info[name] = matmul_weight_quantize_info;
@@ -169,13 +179,11 @@ void RKNNQuantizeProcessor::PerchannelToPerlayer() {
 
     auto next_nodes = name2node_dict_[node->output(0)];
     if (next_nodes.size() > 1 || IsGraphOutput(node->output(0))) {
-      P2OLogger() << "Type1" << std::endl;
       continue;
     }
 
     auto add_node = next_nodes[0];
     if (add_node->op_type() != "Add" || IsGraphOutput(add_node->output(0))) {
-      P2OLogger() << "Type2" << std::endl;
       continue;
     }