Fix onehot input 3 convert

mindspore/lite/src/ops/one_hot.cc

@@ -82,7 +82,8 @@ Registry OneHotRegistry(schema::PrimitiveType_OneHot, OneHotCreator);
 
 namespace {
 constexpr size_t kOneHotInputNum = 4;
-}
+constexpr size_t kOneHotInputNumOpt = 3;
+}  // namespace
 int OneHot::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outputs) {
   if (this->primitive_ == nullptr) {
     return RET_NULL_PTR;
@@ -90,8 +91,10 @@ int OneHot::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outpu
 
   int axis = GetAxis();
   // indices, depth, on_value, off_value
-  if (inputs.size() != kOneHotInputNum) {
-    MS_LOG(ERROR) << "OneHot got inputs num " << inputs.size() << ", should be " << kOneHotInputNum;
+  // indices, depth, on_off_value(contain 2 values);
+  if (inputs.size() != kOneHotInputNum && inputs.size() != kOneHotInputNumOpt) {
+    MS_LOG(ERROR) << "OneHot got inputs num " << inputs.size() << ", should be " << kOneHotInputNum << " or "
+                  << kOneHotInputNumOpt;
     return RET_ERROR;
   }
   auto depth_tensor = inputs.at(1);

mindspore/lite/src/runtime/kernel/arm/fp32/space_to_batch_fp32.cc

@@ -43,7 +43,7 @@ int SpaceToBatchCPUKernel::ReSize() {
   MS_ASSERT(input_tensor);
   auto output_tensor = out_tensors_.at(0);
   MS_ASSERT(output_tensor);
-  MS_ASSERT(param);
+  MS_ASSERT(param_);
   for (size_t i = 0; i < DIMENSION_4D; i++) {
     param_->input_shape_[i] = input_tensor->shape().at(i);
     param_->output_shape_[i] = output_tensor->shape().at(i);

mindspore/lite/src/runtime/kernel/arm/fp32/squeeze_fp32.cc

@@ -34,15 +34,18 @@ int SqueezeCPUKernel::ReSize() { return RET_OK; }
 int SqueezeCPUKernel::Run() {
   mindspore::lite::STATUS ret = RET_ERROR;
   size_t data_size = in_tensors_.front()->Size();
-  MS_ASSERT(input_ptr);
-  MS_ASSERT(output_ptr);
+
   if (in_tensors_.front()->data_type() == kNumberTypeInt32) {
     auto input_ptr = reinterpret_cast<int32_t *>(in_tensors_.front()->MutableData());
     auto output_ptr = reinterpret_cast<int32_t *>(out_tensors_.front()->MutableData());
+    MS_ASSERT(input_ptr);
+    MS_ASSERT(output_ptr);
     ret = DoSqueezeInt32(input_ptr, output_ptr, data_size);
   } else {
     auto input_ptr = reinterpret_cast<float *>(in_tensors_.front()->MutableData());
     auto output_ptr = reinterpret_cast<float *>(out_tensors_.front()->MutableData());
+    MS_ASSERT(input_ptr);
+    MS_ASSERT(output_ptr);
     ret = DoSqueeze(input_ptr, output_ptr, data_size);
   }
 

mindspore/lite/src/runtime/kernel/arm/int8/squeeze_int8.cc

@@ -61,7 +61,7 @@ int SqueezeInt8CPUKernel::Init() {
     return RET_ERROR;
   }
   auto in_quant_args = in_tensors_.front()->quant_params();
-  MS_ASSERT(quant_args.size() > 0);
+  MS_ASSERT(in_quant_args.size() > 0);
   quant_squeeze_param_->in_quant_args_->scale_ = in_quant_args.front().scale;
   quant_squeeze_param_->in_quant_args_->zp_ = in_quant_args.front().zeroPoint;