arm_compute v18.05
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
index c1ba5dd..40ada8f 100644
--- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
@@ -34,6 +34,7 @@
NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_conv_f(),
+ _upsample_f(),
_scaled_output(),
_input(nullptr),
_info(),
@@ -41,13 +42,64 @@
{
}
+Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info,
+ unsigned int inner_border_right, unsigned int inner_border_top)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
+
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
+
+ auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->dimension(1),
+ info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, bias);
+
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
+ }
+
+ if(output->tensor_shape().total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+ const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+ }
+
+ TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
+ info)));
+ const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+ for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(i) != scale_out_info.dimension(i));
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, WeightsInfo()));
+
+ return Status{};
+}
+
void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
- ARM_COMPUTE_ERROR_ON(!info.padding_is_symmetric());
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
_input = input;
_info = info;
@@ -55,15 +107,9 @@
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
- auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
- info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
- const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
-
- ARM_COMPUTE_UNUSED(output_shape);
- ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
- ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
- ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(NEDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
_memory_group.manage(&_scaled_output);
@@ -79,44 +125,20 @@
// Allocate auxiliary tensors
_scaled_output.allocator()->allocate();
+
+ // configure upsample function
+ _upsample_f.configure(input, &_scaled_output, info, inner_border_right, inner_border_top);
}
void NEDeconvolutionLayer::run()
{
_memory_group.acquire();
- // Initialize _scaled_output buffer
- const int width_in = _input->info()->dimension(0);
- const int height_in = _input->info()->dimension(1);
- const int width_scaled = _scaled_output.info()->dimension(0);
- const int height_scaled = _scaled_output.info()->dimension(1);
- const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in);
- const int stride_x = _info.stride().first;
- const int stride_y = _info.stride().second;
-
- std::fill_n(_scaled_output.buffer(), _scaled_output.info()->total_size(), 0);
-
- // scaled_output is the input for the forward convolution. We copy the input elements to scaled_output
- // and insert rows and columns with zeroes depending on the stride values.
- for(int slice = 0; slice < num_2d_slices; ++slice)
- {
- const int start_x = _info.pad().first;
- const int start_y = _inner_border.second + _info.pad().second;
- const int end_y = height_scaled - _info.pad().second;
- const int end_x = width_scaled - _inner_border.first - _info.pad().first;
-
- for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
- {
- for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++)
- {
- const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice))));
- *(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in;
- }
- }
- }
+ // Run upsample kernel
+ _upsample_f.run();
// Run convolution layer
_conv_f.run();
_memory_group.release();
-}
+}
\ No newline at end of file