diff options
Diffstat (limited to 'src/graph/nodes/BatchNormalizationLayer.cpp')
-rw-r--r-- | src/graph/nodes/BatchNormalizationLayer.cpp | 37 |
1 files changed, 22 insertions, 15 deletions
diff --git a/src/graph/nodes/BatchNormalizationLayer.cpp b/src/graph/nodes/BatchNormalizationLayer.cpp index a6a990fd3f..25e9e9bffb 100644 --- a/src/graph/nodes/BatchNormalizationLayer.cpp +++ b/src/graph/nodes/BatchNormalizationLayer.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph; namespace { template <typename BatchBatchNormalizationLayer, typename TensorType, TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) +std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) { auto norm = arm_compute::support::cpp14::make_unique<BatchBatchNormalizationLayer>(); norm->configure( @@ -52,58 +52,65 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe } template <TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon); +std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, + float epsilon) { return instantiate_function<arm_compute::CLBatchNormalizationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, mean, var, beta, gamma, epsilon); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) { return instantiate_function<arm_compute::NEBatchNormalizationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, mean, var, beta, gamma, epsilon); } } // namespace -std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) { + ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr); + ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr); + std::unique_ptr<arm_compute::IFunction> func; _target_hint = ctx.hints().target_hint(); - unsigned int batch_norm_size = input->info()->dimension(2); + arm_compute::ITensor *in = input->tensor(); + arm_compute::ITensor *out = output->tensor(); + + unsigned int batch_norm_size = in->info()->dimension(2); if(_mean.tensor() == nullptr) { - _mean.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _mean.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } if(_var.tensor() == nullptr) { - _var.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _var.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } if(_beta.tensor() == nullptr) { - _beta.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _beta.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } if(_gamma.tensor() == nullptr) { - _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } if(_target_hint == TargetHint::OPENCL) { - func = instantiate<TargetHint::OPENCL>(input, output, _mean, _var, _beta, _gamma, _epsilon); + func = instantiate<TargetHint::OPENCL>(in, out, _mean, _var, _beta, _gamma, _epsilon); ARM_COMPUTE_LOG("Instantiating CLBatchNormalizationLayer"); } else { - func = instantiate<TargetHint::NEON>(input, output, _mean, _var, _beta, _gamma, _epsilon); + func = instantiate<TargetHint::NEON>(in, out, _mean, _var, _beta, _gamma, _epsilon); ARM_COMPUTE_LOG("Instantiating NEBatchNormalizationLayer"); } - ARM_COMPUTE_LOG(" Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() + ARM_COMPUTE_LOG(" Data Type: " << in->info()->data_type() + << " Input shape: " << in->info()->tensor_shape() + << " Output shape: " << out->info()->tensor_shape() << std::endl); return func; |