Fix Fp16 bug

mindspore/lite/examples/export_models/prepare.sh

@@ -59,7 +59,7 @@ export_result_file=export_result.txt
 echo ' ' > ${export_result_file}
 
 
-CLOUD_MODEL_ZOO=../../../../model_zoo/
+CLOUD_MODEL_ZOO=../../../../../models/
 
 checkopts "$@"
 

mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc

@@ -96,6 +96,12 @@ int ConvolutionFP16CPUKernel::Prepare() {
   CHECK_LESS_RETURN(in_tensors_.size(), 2);
   CHECK_LESS_RETURN(out_tensors_.size(), 1);
   UpdateOriginWeightAndBias();
+#ifdef ENABLE_ARM64
+  row_tile_ = C16NUM;
+#else
+  row_tile_ = C12NUM;
+#endif
+  col_tile_ = C8NUM;
   if (op_parameter_->is_train_session_) {
     auto filter_tensor = in_tensors_.at(kWeightIndex);
     CHECK_NULL_RETURN(filter_tensor);
@@ -106,12 +112,6 @@ int ConvolutionFP16CPUKernel::Prepare() {
     int pack_weight_size = oc8 * in_channel * kernel_plane;
     set_workspace_size(pack_weight_size * sizeof(float16_t));
   }
-#ifdef ENABLE_ARM64
-  row_tile_ = C16NUM;
-#else
-  row_tile_ = C12NUM;
-#endif
-  col_tile_ = C8NUM;
   auto ret = InitConvWeightBias();
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init weight bias failed.";

mindspore/lite/test/st/scripts/run_net_train.sh

@@ -360,10 +360,6 @@ function Run_arm() {
          echo "rm -f ${inference_file}*" >> ${run_arm_log_file}
             echo "rm -f ${inference_file}*" >> ${adb_cmd_run_file}
         fi
-        if [ ${enable_fp16} = "true" ]; then
-            export_file=""
-            inference_file=""
-        fi
         adb -s ${device_id} shell < ${adb_cmd_run_file} >> ${run_arm_log_file}
         adb_cmd=$(cat <<-ENDM
         export LD_LIBRARY_PATH=./:/data/local/tmp/:${tmp_dir};./benchmark_train \

mindspore/lite/test/ut/src/runtime/kernel/arm/cxx_api/model_test.cc

@@ -220,7 +220,6 @@ TEST_F(TestCxxApiLiteModel, set_weights_FAILURE) {
   cpu_context->SetEnableFP16(true);
   context->MutableDeviceInfo().push_back(cpu_context);
   auto train_cfg = std::make_shared<TrainCfg>();
-  train_cfg->mix_precision_cfg_.is_raw_mix_precision_ = true;
 
   ASSERT_TRUE(Serialization::Load("./nets/mix_lenet_tod.ms", ModelType::kMindIR, &graph) == kSuccess);
   ASSERT_TRUE(model.Build(GraphCell(graph), context, train_cfg) == kSuccess);

mindspore/lite/tools/benchmark_train/net_train.cc

@@ -348,6 +348,7 @@ std::unique_ptr<session::LiteSession> NetTrain::CreateAndRunNetworkForTrain(cons
     }
   } else {
     MS_LOG(INFO) << "CreateTrainSession from model file" << filename.c_str();
+    std::cout << "CreateTrainSession from model file" << filename.c_str() << std::endl;
     session = std::unique_ptr<session::LiteSession>(
       session::TrainSession::CreateTrainSession(filename, &context, true, &train_cfg));
     if (session == nullptr) {
@@ -424,7 +425,7 @@ int NetTrain::CreateAndRunNetwork(const std::string &filename, const std::string
   if (train_session) {
     session = CreateAndRunNetworkForTrain(filename, bb_filename, context, train_cfg, epochs);
     if (session == nullptr) {
-      MS_LOG(ERROR) << "CreateAndRunNetworkForInference failed.";
+      MS_LOG(ERROR) << "CreateAndRunNetworkForTrain failed.";
       return RET_ERROR;
     }
   } else {