add tflite parsers for PRELU and NEG operators

mindspore/lite/src/runtime/kernel/arm/base/quant_dtype_cast.cc

@@ -95,8 +95,8 @@ int QuantDTypeCastCPUKernel::QuantDTypeCast(int task_id) {
     MS_LOG(ERROR) << "QuantDTypeCast need quantization parameters which is not found.";
     return RET_ERROR;
   }
-  auto quant_arg = !in_tensors_.front()->GetQuantParams().empty() ? in_tensors_.front()->GetQuantParams().front()
-                                                                  : out_tensors_.front()->GetQuantParams().front();
+  auto quant_arg = !out_tensors_.front()->GetQuantParams().empty() ? out_tensors_.front()->GetQuantParams().front()
+                                                                   : in_tensors_.front()->GetQuantParams().front();
   int ret;
   if (uint8_ptr_ == nullptr) {
     if (inverse_) {

mindspore/lite/src/runtime/kernel/arm/base/resize_base.cc

@@ -97,10 +97,6 @@ int ResizeBaseCPUKernel::CheckInputsOuputs() {
     MS_LOG(ERROR) << "Resize input num should be no more than" << kMaxInputNum << ", but got " << in_tensors_.size();
     return RET_ERROR;
   }
-  auto input = in_tensors_.at(0);
-  if (input == nullptr) {
-    return RET_NULL_PTR;
-  }
   if (out_tensors_.size() != kOutputNum) {
     MS_LOG(ERROR) << "Resize output num should be " << kOutputNum << ", but got " << out_tensors_.size();
     return RET_ERROR;

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

@@ -300,7 +300,7 @@ STATUS TfliteSingleInputOpParser::Parse(const std::unique_ptr<tflite::OperatorT>
     }
     op->primitive->value.type = schema::PrimitiveType_Floor;
     op->primitive->value.value = attr.release();
-  } else if (std::strcmp(node_name, "Neg") == 0) {
+  } else if (std::strcmp(node_name, "NEG") == 0) {
     MS_LOG(DEBUG) << "parse TfliteNegParser";
     auto attr = std::make_unique<schema::NegT>();
     if (attr == nullptr) {
@@ -424,7 +424,7 @@ TfliteNodeRegister g_TfliteLogParser("Log", new TfliteLogParser());
 TfliteNodeRegister g_tfliteRoundParser("Round", new TfliteRoundParser());
 TfliteNodeRegister g_TfliteCeilParser("Ceil", new TfliteCeilParser());
 TfliteNodeRegister g_tfliteFloorParser("flOOR", new TfliteFloorParser());
-TfliteNodeRegister g_tfliteNegParser("Neg", new TfliteNegParser());
+TfliteNodeRegister g_tfliteNegParser("NEG", new TfliteNegParser());
 
 TfliteNodeRegister g_tfliteEqualParser("Equal", new TfliteEqualParser());
 TfliteNodeRegister g_tfliteNotEqualParser("NotEqual", new TfliteNotEqualParser());

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

@@ -46,7 +46,9 @@ STATUS TfliteDequantizeParser::Parse(const std::unique_ptr<tflite::OperatorT> &t
     MS_LOG(ERROR) << "output tensor is null";
     return RET_NULL_PTR;
   }
-  if (GetTfliteDataType(in_tensor->type) == kNumberTypeInt8 || GetTfliteDataType(in_tensor->type) == kNumberTypeUInt8) {
+  if (GetTfliteDataType(in_tensor->type) != GetTfliteDataType(out_tensor->type) &&
+      (GetTfliteDataType(in_tensor->type) == kNumberTypeInt8 ||
+       GetTfliteDataType(in_tensor->type) == kNumberTypeUInt8)) {
     std::unique_ptr<schema::QuantDTypeCastT> attr = std::make_unique<schema::QuantDTypeCastT>();
     if (attr == nullptr) {
       MS_LOG(ERROR) << "new op failed";

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

@@ -0,0 +1,61 @@
+/**
+ * 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_prelu_parser.h"
+#include <vector>
+#include <memory>
+#include <map>
+
+namespace mindspore {
+namespace lite {
+STATUS TflitePReLUParser::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 TflitePReLUParser";
+  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::PReLUT> attr = std::make_unique<schema::PReLUT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  attr->channelShared = true;
+  op->primitive->value.type = schema::PrimitiveType_PReLU;
+  op->primitive->value.value = attr.release();
+
+  AddOpInput(op, tensors_id, tensors_format, tensors_id_map, tflite_op->inputs[0], tensors_id->size(),
+             tflite_tensors.size(), schema::Format::Format_NHWC);
+  AddOpInput(op, tensors_id, tensors_format, tensors_id_map, tflite_op->inputs[1], tensors_id->size(),
+             tflite_tensors.size(), schema::Format::Format_NHWC);
+  AddOpOutput(op, tensors_id, tensors_format, tensors_id_map, tflite_op->outputs[0], tensors_id->size(),
+              tflite_tensors.size(), schema::Format::Format_NHWC);
+  return RET_OK;
+}
+
+TfliteNodeRegister g_tflitePReLUParser("PRELU", new TflitePReLUParser());
+}  // namespace lite
+}  // namespace mindspore

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

@@ -0,0 +1,41 @@
+/**
+ * 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_PRELU_PARSER_H
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TFLITE_PRELU_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 TflitePReLUParser : public TfliteNodeParser {
+ public:
+  TflitePReLUParser() : TfliteNodeParser("PRELU") {}
+
+  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_PRELU_PARSER_H

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

@@ -46,8 +46,9 @@ STATUS TfliteQuantizeParser::Parse(const std::unique_ptr<tflite::OperatorT> &tfl
     MS_LOG(ERROR) << "output tensor is null";
     return RET_NULL_PTR;
   }
-  if (GetTfliteDataType(out_tensor->type) == kNumberTypeInt8 ||
-      GetTfliteDataType(out_tensor->type) == kNumberTypeUInt8) {
+  if (GetTfliteDataType(in_tensor->type) != GetTfliteDataType(out_tensor->type) &&
+      (GetTfliteDataType(out_tensor->type) == kNumberTypeInt8 ||
+       GetTfliteDataType(out_tensor->type) == kNumberTypeUInt8)) {
     std::unique_ptr<schema::QuantDTypeCastT> attr = std::make_unique<schema::QuantDTypeCastT>();
     if (attr == nullptr) {
       MS_LOG(ERROR) << "new op failed";