diff --git a/mindspore/lite/src/common/anf_importer/import_from_meta_graphT.cc b/mindspore/lite/src/common/anf_importer/import_from_meta_graphT.cc
index 0b47a8e6362..786f825a651 100644
--- a/mindspore/lite/src/common/anf_importer/import_from_meta_graphT.cc
+++ b/mindspore/lite/src/common/anf_importer/import_from_meta_graphT.cc
@@ -89,7 +89,7 @@ int AnfImporterFromMetaGraphT::ConverterCNode() {
     }
     auto primTValue = std::make_shared<PrimitiveTValue>(cNode->primitive.release());
     // add quant parameter
-    if (cNode->quantType == schema::QuantType_AwareTrainning || cNode->quantType == schema::QuantType_PostTraining) {
+    if (cNode->quantType == schema::QuantType_AwareTrainning) {
       primTValue->SetQuantType(cNode->quantType);
       for (int index : cNode->inputIndex) {
         primTValue->AddInputQuantParam(*(meta_graph_->allTensors[index]->quantParams[0]));
diff --git a/mindspore/lite/tools/converter/converter.cc b/mindspore/lite/tools/converter/converter.cc
index 07dd48268ec..f7a3905c02d 100644
--- a/mindspore/lite/tools/converter/converter.cc
+++ b/mindspore/lite/tools/converter/converter.cc
@@ -141,11 +141,11 @@ void Converter::CreateQuantizer(FuncGraphPtr funcGraph, const converter::Flags *
       //        flags->bitNum));
       //      break;
       //    }
-      //    case mindspore::schema::QuantType_PostTraining: {
-      //      MS_LOG(INFO) << "create PostTrainningQuantizer!";
-      //      mQuantizer.reset(new quant::PostTrainingQuantizer(funcGraph, flags->configFile, 8));
-      //      break;
-      //    }
+    case mindspore::schema::QuantType_PostTraining: {
+      MS_LOG(INFO) << "create PostTrainningQuantizer!";
+      mQuantizer.reset(new quant::PostTrainingQuantizer(funcGraph, flags->configFile, 8));
+      break;
+    }
     case mindspore::schema::QuantType_QUANT_NONE:
       MS_LOG(INFO) << "Not do quantization for model!";
       break;
diff --git a/mindspore/lite/tools/converter/quantizer/quantize_util.cc b/mindspore/lite/tools/converter/quantizer/quantize_util.cc
index 26d5fce2294..e03cbe79e91 100644
--- a/mindspore/lite/tools/converter/quantizer/quantize_util.cc
+++ b/mindspore/lite/tools/converter/quantizer/quantize_util.cc
@@ -308,21 +308,24 @@ STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, doubl
 
 STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_max, int quant_min, size_t bitNum,
                    bool per_channel) {
-  if (per_channel) {
-    // per channel
-    auto dims = weightPtr->tensor_shape();
-    if (dims.size() < 1) {
-      MS_LOG(ERROR) << "weight dims size error";
-      return RET_ERROR;
-    }
-    // todo(x)
+  auto dims = weightPtr->tensor_shape();
+  if (dims.size() != 4) {
+    MS_LOG(ERROR) << "weight dims size error: " << dims.size() << " Back to per layer.";
+    per_channel = false;
+  } else {
     uint32_t channels = dims[3];
     if (channels == 0) {
-      MS_LOG(ERROR) << "channels error 0";
+      MS_LOG(ERROR) << "channels is 0";
       return RET_ERROR;
     }
+  }
 
+  if (per_channel) {
+    // notice:
+    // at now for tflite model, Conv2D's weight format is KHWC, so is DepthwiseConv2D
+    // if TransWeightFormat is done before PostTraingingQuantization, the DepthwiseCon2D's weight is CHWK
     size_t shapeSize = weightPtr->tensor_shape_size();
+    auto channels = dims[3];
     size_t oneFilterSize = shapeSize / channels;
     auto *rawDatas = reinterpret_cast<const float *>(weightPtr->tensor_addr());
     if (rawDatas == nullptr) {
@@ -330,17 +333,17 @@ STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_
       return RET_ERROR;
     }
 
+    float min = FLT_MAX;
+    float max = FLT_MIN;
     weightPtr->quant_param().clear();
     vector<int8_t> qDatas(shapeSize);
+
     for (uint32_t i = 0; i < channels; i++) {
-      float min = 0;
-      float max = 0;
       // find min and max
       for (uint32_t j = 0; j < oneFilterSize; j++) {
-        min = std::min(min, rawDatas[j + i * oneFilterSize]);
-        max = std::max(max, rawDatas[j + i * oneFilterSize]);
+        min = std::min(min, rawDatas[i + j * oneFilterSize]);
+        max = std::max(max, rawDatas[i + j * oneFilterSize]);
       }
-
       std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
       STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
       if (status != RET_OK) {
@@ -349,11 +352,10 @@ STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_
       }
       // update data and datatype
       for (uint32_t j = 0; j < oneFilterSize; j++) {
-        float rawData = rawDatas[j + i * oneFilterSize];
+        float rawData = rawDatas[i + j * oneFilterSize];
         auto qData = QuantizeData<int8_t>(rawData, quantParam.get(), quant_max, quant_min);
-        qDatas[j + i * oneFilterSize] = qData;
+        qDatas[i + j * oneFilterSize] = qData;
       }
-
       weightPtr->set_quant_param(quantParam);
     }
     auto ret =