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@ -28,17 +28,15 @@ using mindspore::schema::PrimitiveType_Unsqueeze;
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namespace mindspore::kernel {
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int UnsqueezeCPUKernel::Init() {
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if (context_->infer_shape_interrupt_ && !context_->running_) {
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set_need_reinit();
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if (!InferShapeDone()) {
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return RET_OK;
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}
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int ret = ReSize();
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return ret;
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return ReSize();
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}
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int UnsqueezeCPUKernel::ReSize() {
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data_size_ = in_tensors_.at(0)->ElementsNum();
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thread_sz_count_ = MSMIN(thread_count_, data_size_);
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thread_sz_count_ = MSMIN(context_->thread_num_, data_size_);
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thread_sz_stride_ = UP_DIV(data_size_, thread_sz_count_);
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return RET_OK;
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}
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@ -48,7 +46,7 @@ int UnsqueezeCPUKernel::DoUnsqueeze(int task_id) {
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if (size == 0) {
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return RET_OK;
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}
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int offset = task_id * thread_sz_stride_;
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size_t offset = task_id * thread_sz_stride_ * sizeof(float);
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int ret = Unsqueeze(in_ptr_ + offset, out_ptr_ + offset, size * sizeof(float));
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "UnsqueezeRun error task_id[" << task_id << "] error_code[" << ret << "]";
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@ -73,8 +71,8 @@ int UnsqueezeCPUKernel::Run() {
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MS_LOG(ERROR) << "Prepare failed.";
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return RET_ERROR;
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}
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in_ptr_ = reinterpret_cast<float *>(in_tensors_.at(0)->Data());
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out_ptr_ = reinterpret_cast<float *>(out_tensors_.at(0)->Data());
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in_ptr_ = reinterpret_cast<int8_t *>(in_tensors_.at(0)->Data());
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out_ptr_ = reinterpret_cast<int8_t *>(out_tensors_.at(0)->Data());
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ret = LiteBackendParallelLaunch(UnsqueezeRun, this, thread_sz_count_);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "UnsqueezeRun error error_code[" << ret << "]";
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@ -85,19 +83,19 @@ int UnsqueezeCPUKernel::Run() {
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kernel::LiteKernel *CpuUnsqueezeFp32KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
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const std::vector<lite::tensor::Tensor *> &outputs,
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OpParameter *opParameter, const lite::Context *ctx,
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OpParameter *parameter, const lite::Context *ctx,
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const kernel::KernelKey &desc, const lite::Primitive *primitive) {
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MS_ASSERT(opParameter != nullptr);
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MS_ASSERT(parameter != nullptr);
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MS_ASSERT(desc.type == schema::PrimitiveType_Unsqueeze);
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auto *kernel = new (std::nothrow) UnsqueezeCPUKernel(opParameter, inputs, outputs, ctx, primitive);
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auto *kernel = new (std::nothrow) UnsqueezeCPUKernel(parameter, inputs, outputs, ctx, primitive);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "new UnsqueezeCPUKernel fail!";
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
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<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
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MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_ << ", type: "
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<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
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delete kernel;
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return nullptr;
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}
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@ -105,4 +103,5 @@ kernel::LiteKernel *CpuUnsqueezeFp32KernelCreator(const std::vector<lite::tensor
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}
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REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Unsqueeze, CpuUnsqueezeFp32KernelCreator)
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REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_Unsqueeze, CpuUnsqueezeFp32KernelCreator)
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} // namespace mindspore::kernel
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