fix bug when op is custom

fix deconv bug

mindspore/lite/test/ut/tools/converter/parser/tflite/tflite_l2norm_parser_test.cc

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "ut/tools/converter/parser/tflite/tflite_parsers_test_utils.h"
+#include <iostream>
+#include "common/common_test.h"
+
+namespace mindspore {
+class TestTfliteParserL2Norm : public TestTfliteParser {
+ public:
+  TestTfliteParserL2Norm() = default;
+  void SetUp() override { meta_graph = LoadAndConvert("./l2norm.tflite", ""); }
+};
+
+TEST_F(TestTfliteParserL2Norm, OpType) {
+  ASSERT_NE(meta_graph, nullptr);
+  ASSERT_GT(meta_graph->nodes.size(), 0);
+  ASSERT_EQ(meta_graph->nodes.front()->primitive->value.type, schema::PrimitiveType_L2Norm) << "wrong Op Type";
+}
+
+TEST_F(TestTfliteParserL2Norm, AttrValue) {
+  ASSERT_NE(meta_graph->nodes.front()->primitive->value.AsL2Norm(), nullptr);
+  auto val = meta_graph->nodes.front()->primitive->value.AsL2Norm();
+  ASSERT_EQ(val->epsilon, 0.0);
+  std::vector<int32_t> axis = {0, 1, 2, 3};
+  ASSERT_EQ(val->axis, axis);
+}
+
+}  // namespace mindspore

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

@@ -92,8 +92,6 @@ STATUS TfliteDeConvParser::Parse(const std::unique_ptr<tflite::OperatorT> &tflit
              tflite_op->inputs[2], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);
   AddOpInput(op, tensors_id, tensors_format, tensors_id_map,
              tflite_op->inputs[1], tensors_id->size(), tflite_tensors.size(), schema::Format_KHWC);
-  AddOpInput(op, tensors_id, tensors_format, tensors_id_map,
-             tflite_op->inputs[0], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);
   AddOpOutput(op, tensors_id, tensors_format, tensors_id_map,
               tflite_op->outputs[0], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);
   return RET_OK;

mindspore/lite/tools/converter/parser/tflite/tflite_l2norm_parser.cc

+/**
+* Copyright 2020 Huawei Technologies Co., Ltd
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* distributed under the License is distributed on an AS
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "tools/converter/parser/tflite/tflite_l2norm_parser.h"
+#include <vector>
+#include <memory>
+#include <map>
+
+namespace mindspore {
+namespace lite {
+STATUS TfliteL2NormParser::Parse(const std::unique_ptr<tflite::OperatorT> &tflite_op,
+                               const std::vector<std::unique_ptr<tflite::TensorT>> &tflite_tensors,
+                               const std::vector<std::unique_ptr<tflite::BufferT>> &tflite_model_buffer,
+                               schema::CNodeT *op,
+                               std::vector<int32_t> *tensors_id,
+                               std::vector<schema::Format> *tensors_format,
+                               std::map<int, int>  *tensors_id_map)  {
+  MS_LOG(DEBUG) << "parse TfliteL2NormParser";
+
+  // set attr
+  if (op == nullptr) {
+    MS_LOG(ERROR) << "op is null";
+    return RET_NULL_PTR;
+  }
+  op->primitive = std::make_unique<schema::PrimitiveT>();
+  if (op->primitive == nullptr) {
+    MS_LOG(ERROR) << "op->primitive is null";
+    return RET_NULL_PTR;
+  }
+
+  std::unique_ptr<schema::L2NormT> attr(new schema::L2NormT());
+  auto data_index = tflite_op->inputs[0];
+  const auto &data_tensor = tflite_tensors[data_index];
+  if (data_tensor == nullptr) {
+    MS_LOG(ERROR) << "the input tensor is null";
+    return RET_NULL_PTR;
+  }
+
+  auto ndim = data_tensor->shape.size();
+  std::vector<int32_t> axis;
+  axis.reserve(ndim);
+  for (int i = 0; i < ndim; i++) {
+      axis.emplace_back(i);
+    }
+  attr->axis = axis;
+  attr->epsilon = 0.0f;
+
+  op->primitive->value.type = schema::PrimitiveType_L2Norm;
+  op->primitive->value.value = attr.release();
+
+  // set input
+  AddOpInput(op, tensors_id, tensors_format, tensors_id_map,
+               tflite_op->inputs[0], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);
+  AddOpOutput(op, tensors_id, tensors_format, tensors_id_map,
+              tflite_op->outputs[0], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);
+  return RET_OK;
+}
+
+TfliteNodeRegister g_tfliteL2NormParser("L2_NORMALIZATION", new TfliteL2NormParser());
+}  // namespace lite
+}  // namespace mindspore

mindspore/lite/tools/converter/parser/tflite/tflite_l2norm_parser.h

+/**
+* Copyright 2020 Huawei Technologies Co., Ltd
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TFLITE_L2NORM_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TFLITE_L2NORM_PARSER_H
+
+#include <memory>
+#include <vector>
+#include <map>
+#include "tools/converter/parser/tflite/tflite_node_parser.h"
+#include "tools/converter/parser/tflite/tflite_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+class TfliteL2NormParser : public TfliteNodeParser {
+ public:
+  TfliteL2NormParser() : TfliteNodeParser("L2_NORMALIZATION") {}
+
+  STATUS Parse(const std::unique_ptr<tflite::OperatorT> &tflite_op,
+               const std::vector<std::unique_ptr<tflite::TensorT>> &tflite_tensors,
+               const std::vector<std::unique_ptr<tflite::BufferT>> &tflite_model_buffer,
+               schema::CNodeT *op,
+               std::vector<int32_t> *tensors_id,
+               std::vector<schema::Format> *tensors_format,
+               std::map<int, int>  *tensors_id_map) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TFLITE_L2NORM_PARSER_H

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

@@ -96,6 +96,11 @@ STATUS TfliteModelParser::ConvertOp(const std::unique_ptr<tflite::ModelT> &tflit
   for (const auto &tflite_op : tflite_subgraph->operators) {
     auto tflite_op_type = (tflite_model->operator_codes[tflite_op->opcode_index])->builtin_code;
     auto op_type = GetMSOpType(tflite_op_type);
+    if (op_type == "CUSTOM") {
+      auto custom_type = (tflite_model->operator_codes[tflite_op->opcode_index])->custom_code;
+      MS_LOG(ERROR) << "CUSTOM op is not supported, the type is " << custom_type;
+      return RET_ERROR;
+    }
 
     std::unique_ptr<schema::CNodeT> op(new schema::CNodeT);
     op->name = op_type + "-" + std::to_string(idx++);
@@ -216,7 +221,7 @@ STATUS TfliteModelParser::GetGraphInfo(const std::unique_ptr<tflite::SubGraphT>
   return RET_OK;
 }
 
-STATUS TfliteModelParser::UpdateOp(schema::MetaGraphT *sub_graph) {
+STATUS TfliteModelParser::ConvertGroupDepthwiseOp(schema::MetaGraphT* sub_graph) {
   for (auto &op : sub_graph->nodes) {
     if (op->primitive->value.type == schema::PrimitiveType_DepthwiseConv2D) {
       auto attr = op->primitive->value.AsDepthwiseConv2D();
@@ -248,7 +253,6 @@ STATUS TfliteModelParser::UpdateOp(schema::MetaGraphT *sub_graph) {
         auto weight_id = op->inputIndex[1];
         auto &weight_tensor = sub_graph->allTensors.at(weight_id);
         if (weight_tensor->dataType == TypeId::kNumberTypeUInt8) {
-          // convert weight format KHWC -> CHWK
           auto status = TransFilterFormat<uint8_t>(weight_tensor.get(), kKHWC2CHWK);
           if (status != RET_OK) {
             MS_LOG(ERROR) << "Trans depthwiseConv Filter Format failed.";
@@ -256,13 +260,13 @@ STATUS TfliteModelParser::UpdateOp(schema::MetaGraphT *sub_graph) {
           }
         }
         if (weight_tensor->dataType == kNumberTypeFloat32 || weight_tensor->dataType == kNumberTypeFloat) {
-          // convert weight format KHWC -> CHWK
           auto status = TransFilterFormat<float>(weight_tensor.get(), kKHWC2CHWK);
           if (status != RET_OK) {
-            MS_LOG(ERROR) << "Trans depthwiseConv Filter Format failed.";
+            MS_LOG(ERROR) << "Trans filter format failed.";
             return RET_ERROR;
           }
         }
+        weight_tensor->format = schema::Format_CHWK;
       }
     }
   }
@@ -303,8 +307,8 @@ MetaGraphT *TfliteModelParser::Parse(const std::string &model_file, const std::s
   }
 
   // update for depthwiseConv
-  if (UpdateOp(sub_graph.get()) != RET_OK) {
-    MS_LOG(ERROR) << "update depthwise conv failed";
+  if (ConvertGroupDepthwiseOp(sub_graph.get()) != RET_OK) {
+    MS_LOG(ERROR) << "convert group depthwise conv failed";
     return nullptr;
   }
 

mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.h

@@ -67,7 +67,7 @@ class TfliteModelParser : public ModelParser {
   STATUS GetGraphInfo(const std::unique_ptr<tflite::SubGraphT> &tflite_subgraph,
                       schema::MetaGraphT* sub_graph);
 
-  STATUS UpdateOp(schema::MetaGraphT* sub_graph);
+  STATUS ConvertGroupDepthwiseOp(schema::MetaGraphT* sub_graph);
 
  private:
   std::vector<int32_t> tensorsId;