tod fix compilation issues

mindspore/lite/include/train_session.h

@@ -23,7 +23,7 @@
 
 namespace mindspore {
 namespace lite {
-class Model;
+struct Model;
 }
 namespace lite::tensor {
 class Tensor;

mindspore/lite/src/runtime/kernel/arm/fp32_grad/apply_momentum.cc

@@ -66,7 +66,7 @@ int ApplyMomentumCPUKernel::Init() {
   // Only for test with uninitialized Data
   size_t elem_num = in_tensors_[0]->ElementsNum();
   auto accumulate = reinterpret_cast<float *>(in_tensors_[1]->Data());
-  for (int i =0; i < elem_num; i++) accumulate[i] = 0.0;
+  for (size_t i =0; i < elem_num; i++) accumulate[i] = 0.0;
 
   workspace = new float[elem_num];
   return 0;

mindspore/lite/src/runtime/kernel/arm/fp32_grad/arithmetic_grad.cc

@@ -27,10 +27,6 @@ using mindspore::lite::RET_ERROR;
 using mindspore::lite::RET_OK;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kArithGradOpInputNum = 3;
-constexpr int kArithGradOpOutputNum = 2;
-}  // namespace
 
 int ArithmeticGradCPUKernel::Init() {
   auto dx1 = out_tensors_[0];

mindspore/lite/src/runtime/kernel/arm/fp32_grad/convolution_grad_filter.cc

@@ -37,9 +37,10 @@ int ConvolutionGradFilterCPUKernel::Init() {
   MS_ASSERT(x_tensor != nullptr);
   auto *dy_tensor = in_tensors_.at(0);
   MS_ASSERT(dy_tensor != nullptr);
+#if 0
   auto *weight_tensor = out_tensors_.at(0);
   MS_ASSERT(weight_tensor != nullptr);
-
+#endif
   auto conv_param = reinterpret_cast<ConvParameter *>(op_parameter_);
   conv_param->output_batch_ = dy_tensor->shape().at(kNHWC_N);
   conv_param->input_batch_ = x_tensor->shape().at(kNHWC_N);

mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.cc

@@ -138,7 +138,7 @@ int SparseSoftmaxCrossEntropyWithLogitsCPUKernel::Init() {
   sm_params_.n_dim_ = 2;
   sm_params_.element_size_ = data_size;
   sm_params_.axis_ = 1;
-  for (int i = 0; i < dims.size(); i++) sm_params_.input_shape_[i] = dims[i];
+  for (size_t i = 0; i < dims.size(); i++) sm_params_.input_shape_[i] = dims[i];
 
   return RET_OK;
 }

mindspore/lite/src/scheduler.cc

@@ -92,7 +92,6 @@ int Scheduler::InferShape(const lite::Model *model, std::vector<tensor::Tensor *
     for (size_t j = 0; j < in_size; ++j) {
       inputs.emplace_back(tensors->at(node->input_indices_[j]));
     }
-
     auto out_size = node->output_indices_.size();
     for (size_t j = 0; j < out_size; ++j) {
       outputs.emplace_back(tensors->at(node->output_indices_[j]));

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/activation_grad_fp32_tests.cc

@@ -16,6 +16,7 @@
 #include <iostream>
 #include <memory>
 #include <vector>
+#include <algorithm>
 
 #include "utils/log_adapter.h"
 #include "common/common_test.h"
@@ -295,7 +296,8 @@ TEST_F(TestActGradFp32, hswishGradFp32) {
   printf("single thread running time : %f ms\n", time_avg / 1000.0f);
 
   printf("==================output data=================\n");
-  for (int i = 0; i < std::min(output_data_size, 20UL); i++) {
+  size_t min = (output_data_size < 20UL) ? output_data_size : 20UL;
+  for (size_t i = 0; i < min; i++) {
     std::cout << output_data[i] << " ,";
   }
   std::cout << std::endl;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/network_test.cc

@@ -181,7 +181,6 @@ TEST_F(NetworkTest, tuning_layer) {
   }
   meta_graph->inputIndex = {6, 0};  // XXX TODO why is it reverse?
   meta_graph->outputIndex = {5, 14};
-  const int NUM_OF_OUTPUTS = 2;
 
   auto input0 = std::make_unique<schema::TensorT>();
   input0->nodeType = schema::NodeType::NodeType_ValueNode;
@@ -452,7 +451,7 @@ int32_t fileIterator(mindspore::session::TrainSession *session, const std::strin
   int32_t res = 0;
   if (auto dir = opendir(path.c_str())) {
     while (auto f = readdir(dir)) {
-      if (!f->d_name || f->d_name[0] == '.') continue;
+      if (f->d_name[0] == '.') continue;
       if (f->d_type == DT_DIR) fileIterator(session, path + f->d_name + "/", cb);
 
       if (f->d_type == DT_REG)
@@ -462,11 +461,10 @@ int32_t fileIterator(mindspore::session::TrainSession *session, const std::strin
   }
   return res;
 }
-#if 0
 void replaceExt(const std::string &src, std::string *dst) {
-  dst = &std::move(src.substr(0, src.find_last_of('.')) + ".emb");
+  *dst = src.substr(0, src.find_last_of('.')) + ".emb";
 }
-#endif
+
 int32_t runEffNet(mindspore::session::TrainSession *session, const std::string &in, const std::string &out) {
   // setup input
   auto inputs = session->GetInputs();
@@ -494,7 +492,6 @@ int32_t runEffNet(mindspore::session::TrainSession *session, const std::string &
 }
 
 TEST_F(NetworkTest, efficient_net) {
-  const int NUM_OF_INPUTS = 1;
   char *buf = nullptr;
   size_t net_size = 0;
   std::string net = "./test_data/nets/efficientnet_b0_f.ms";
@@ -520,7 +517,7 @@ TEST_F(NetworkTest, efficient_net) {
     int32_t res = 0;
     if (in.find(".bin") != std::string::npos) {
       std::string out;
-      replaceExt(in, out);
+      replaceExt(in, &out);
       res = runEffNet(session, in, out);
       std::cout << "input file: " << in << (res ?  " Fail" : " Pass") << std::endl;
     }