From b064ed864d1dc196ae97b33dc7860a418cff516e Mon Sep 17 00:00:00 2001
From: yeyunpeng2020 <yeyunpeng1@huawei.com>
Date: Fri, 9 Apr 2021 15:57:33 +0800
Subject: [PATCH] demo add annotation & fix gpu codex

---
 mindspore/lite/examples/quick_start_cpp/main.cc          | 4 ++++
 .../src/main/java/com/mindspore/lite/demo/Main.java      | 2 ++
 .../lite/src/runtime/kernel/opencl/kernel/one_hot.cc     | 9 ++++++---
 mindspore/lite/src/runtime/kernel/opencl/utils.cc        | 4 ++--
 4 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/mindspore/lite/examples/quick_start_cpp/main.cc b/mindspore/lite/examples/quick_start_cpp/main.cc
index 820ba60491..8262e9a6cd 100644
--- a/mindspore/lite/examples/quick_start_cpp/main.cc
+++ b/mindspore/lite/examples/quick_start_cpp/main.cc
@@ -107,18 +107,22 @@ int GenerateInputDataWithRandom(std::vector<mindspore::tensor::MSTensor *> input
 
 int Run(mindspore::session::LiteSession *session) {
   auto inputs = session->GetInputs();
+
+  // Generate random data as input data.
   auto ret = GenerateInputDataWithRandom(inputs);
   if (ret != mindspore::lite::RET_OK) {
     std::cerr << "Generate Random Input Data failed." << std::endl;
     return ret;
   }
 
+  // Run Inference.
   ret = session->RunGraph();
   if (ret != mindspore::lite::RET_OK) {
     std::cerr << "Inference error " << ret << std::endl;
     return ret;
   }
 
+  // Get Output Tensor Data.
   auto out_tensors = session->GetOutputs();
   for (auto tensor : out_tensors) {
     std::cout << "tensor name is:" << tensor.first << " tensor size is:" << tensor.second->Size()
diff --git a/mindspore/lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java b/mindspore/lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
index 9671f9e98c..a98100e917 100644
--- a/mindspore/lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
+++ b/mindspore/lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
@@ -39,6 +39,8 @@ public class Main {
 
     private static boolean compile() {
         MSConfig msConfig = new MSConfig();
+        // You can set config through Init Api or use the default parameters directly.
+        // The default parameter is that the backend type is DeviceType.DT_CPU, and the number of threads is 2.
         boolean ret = msConfig.init(DeviceType.DT_CPU, 2);
         if (!ret) {
             System.err.println("Init context failed");
diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/one_hot.cc b/mindspore/lite/src/runtime/kernel/opencl/kernel/one_hot.cc
index 7a7cac3e47..48b77c72fc 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/kernel/one_hot.cc
+++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/one_hot.cc
@@ -65,10 +65,13 @@ int OneHotOpenCLKernel::Prepare() {
 
 int OneHotOpenCLKernel::InitWeights() {
   depth_ = static_cast<int32_t *>(in_tensors_[1]->data_c())[0];
-  if (in_tensors_.size() > 2) {
-    on_value_ = static_cast<float *>(in_tensors_[2]->data_c())[0];
+  // inputs num is 3 or 4.
+  if (in_tensors_.size() == 3) {  // onnx
+    off_value_ = static_cast<float *>(in_tensors_[2]->data_c())[0];
+    on_value_ = static_cast<float *>(in_tensors_[2]->data_c())[1];
   }
-  if (in_tensors_.size() > 3) {
+  if (in_tensors_.size() == 4) {  // tf
+    on_value_ = static_cast<float *>(in_tensors_[2]->data_c())[0];
     off_value_ = static_cast<float *>(in_tensors_[3]->data_c())[0];
   }
   return RET_OK;
diff --git a/mindspore/lite/src/runtime/kernel/opencl/utils.cc b/mindspore/lite/src/runtime/kernel/opencl/utils.cc
index fdbde0be1f..0abc134cfb 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/utils.cc
+++ b/mindspore/lite/src/runtime/kernel/opencl/utils.cc
@@ -300,7 +300,7 @@ static std::set<void *> tmp_weights;
 
 void StoreTmpWeight(lite::Tensor *tensor) {
   MS_LOG(WARNING) << "store weight when kernel don't infer shape!";
-  if (tensor && tensor->data_c() && tensor->Size()) {
+  if (tensor != nullptr && tensor->data_c() != nullptr && tensor->Size() > 0) {
     void *new_data = malloc(tensor->Size());
     MS_ASSERT(new_data);
     if (new_data == nullptr) {
@@ -314,8 +314,8 @@ void StoreTmpWeight(lite::Tensor *tensor) {
 
 void FreeTmpWeight(void *data) {
   if (tmp_weights.count(data)) {
-    free(data);
     tmp_weights.erase(data);
+    free(data);
   }
 }