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Diffstat (limited to 'src/runtime/NEON/functions/NEWinogradLayer.cpp')
-rw-r--r--src/runtime/NEON/functions/NEWinogradLayer.cpp113
1 files changed, 55 insertions, 58 deletions
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp
index 21f298ca25..da46f8773c 100644
--- a/src/runtime/NEON/functions/NEWinogradLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -43,8 +43,8 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
namespace arm_compute
{
NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _winograd_kernel(), _permute_input(), _permute_weights(), _permute_output(), _workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),
- _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv()
+ : _memory_group(std::move(memory_manager)), _winograd_kernel(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(),
+ _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv()
{
} /* arm_compute */
@@ -72,36 +72,37 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides.");
// Get convolved dimensions
- auto padding = PADDING_VALID;
- const int in_channels = input->info()->dimension(2);
- const int out_channels = output->info()->dimension(2);
- const int weights_width = weights->info()->dimension(0);
- const int weights_height = weights->info()->dimension(1);
+ const int in_channels = input->info()->dimension(2);
+ const int out_channels = output->info()->dimension(2);
- const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });
const Tensor4DShape in_shape(internal_get_input_shape(input));
// Get the memory required to instantiate a new Winograd operator.
- constexpr size_t kstore_alignment = 64;
- const size_t kernel_storage_per_thread = NEWinogradLayerKernel::get_kernel_storage_size(kernel_shape);
- _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_per_thread + kstore_alignment - 1) }, 1, DataType::U8));
+ constexpr size_t storage_alignment = 64;
+ const size_t kernel_storage_size = NEWinogradLayerKernel::get_weight_storage_size(out_channels, in_channels) * sizeof(float);
+ _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
_memory_group.manage(&_kernel_storage);
-
- // Get workbench size and allocate memory
-
- constexpr size_t wspace_alignment = 64;
- const size_t ws_size = NEWinogradLayerKernel::get_working_space_size(in_shape, kernel_shape, padding);
- _workspace.allocator()->init(TensorInfo(TensorShape{ (ws_size + wspace_alignment - 1) }, 1, DataType::U8));
- _memory_group.manage(&_workspace);
_memory_group.manage(&_input_nhwc);
_kernel_storage.allocator()->allocate();
- _workspace.allocator()->allocate();
+ // Input storage
+ const size_t input_storage_size = NEWinogradLayerKernel::get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, false) * sizeof(float);
+ _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
+ _memory_group.manage(&_input_workspace);
+ _input_workspace.allocator()->allocate();
+
+ // Output storage
+ const size_t output_storage_size = NEWinogradLayerKernel::get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, false) * sizeof(float);
+ _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
+ _memory_group.manage(&_output_workspace);
+ _output_workspace.allocator()->allocate();
- // Create Winograd operator object
- _conv = support::cpp14::make_unique<Winograd3x3F32>(kernel_shape, in_shape, padding, _kernel_storage.buffer());
+ // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+ TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
+ _output->info()->dimension(1), _output->info()->dimension(3)),
+ 1, _output->info()->data_type());
+ _output_nhwc.allocator()->init(info);
- // Configure the kernel, padding not needed so it's safe to call configure after allocare
- _winograd_kernel.configure(_conv.get());
+ _output_nhwc.allocator()->allocate();
// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
switch(weights->info()->num_dimensions())
@@ -122,60 +123,56 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
break;
}
}
+
+ _weights_hwio.allocator()->allocate();
+
// configure the kernel to transform the input tensor from NCHW -> NHWC
_permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
- // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
- TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
- _output->info()->dimension(1), _output->info()->dimension(3)),
- 1, _output->info()->data_type());
- _output_nhwc.allocator()->init(info);
-
- _output_nhwc.allocator()->allocate();
- _weights_hwio.allocator()->allocate();
_input_nhwc.allocator()->allocate();
+
+ // Create Winograd operator object
+ _conv = support::cpp14::make_unique<Winograd3x3F32>(
+ in_shape.n_batches,
+ in_shape.n_channels,
+ in_shape.n_rows,
+ in_shape.n_cols,
+ out_channels,
+ false,
+ reinterpret_cast<const float *>(_weights_hwio.buffer()),
+ reinterpret_cast<float *>(_kernel_storage.buffer()),
+ reinterpret_cast<float *>(_input_nhwc.buffer()),
+ reinterpret_cast<float *>(_input_workspace.buffer()),
+ reinterpret_cast<float *>(_output_nhwc.buffer()),
+ reinterpret_cast<float *>(_output_workspace.buffer()));
+
+ // Configure the kernel, padding not needed so it's safe to call configure after allocare
+ _winograd_kernel.configure(_conv.get());
+
+ // Reorder the convoluted output to ACL's ordering NCHW
+ _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
+
}
void NEWinogradLayer::run()
{
-#if defined(__aarch64__)
_memory_group.acquire();
if(!_reshaped_kernel)
{
_reshaped_kernel = true;
_permute_weights.run();
- _conv->transform_weights(reinterpret_cast<const float *>(_weights_hwio.buffer()), nullptr);
+ _conv->transform_weights();
}
- const Tensor4DShape in_shape(internal_get_input_shape(_input));
- auto padding = PADDING_VALID;
-
//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
_permute_input.run();
-
- //Setup matrices ptrs and transfor the input tensor to the appropriate form before running GEMM.
- _conv->reshape_input(in_shape, padding, reinterpret_cast<float *>(_input_nhwc.buffer()), _workspace.buffer());
-
+ // Transform input tensor to the winograd domain
+ _conv->transform_input();
//Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
NEScheduler::get().schedule(&_winograd_kernel, Window::DimX);
-
- //Transform the output to the appropriate form
- _conv->reshape_output(in_shape, padding, reinterpret_cast<float *>(_output_nhwc.buffer()));
-
+ // Transform output tensor to the spatial domain
+ _conv->transform_output();
// Reorder the convoluted output to ACL's ordering NCHW
- _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
_permute_output.run();
-
_memory_group.release();
-#else /* __aarch64__ */
- ARM_COMPUTE_UNUSED(_winograd_kernel);
- ARM_COMPUTE_UNUSED(_workspace);
- ARM_COMPUTE_UNUSED(_kernel_storage);
- ARM_COMPUTE_UNUSED(_input);
- ARM_COMPUTE_UNUSED(_weights);
- ARM_COMPUTE_UNUSED(_output);
- ARM_COMPUTE_UNUSED(_reshaped_kernel);
- ARM_COMPUTE_UNUSED(_conv);
- ARM_COMPUTE_ERROR("Winograd only supported for aarch64, recompile with arch=arm64-v8a.");
-#endif /* __aarch64__ */
}
} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-28 20:13:56 +0000