arm_compute v18.11
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index 40562b5..e07feb2 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -27,6 +27,8 @@
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
#include <memory>
#include <tuple>
@@ -38,7 +40,10 @@
: _memory_group(std::move(memory_manager)),
_scale_f(),
_conv_f(),
+ _flip_weights(),
_scaled_output(),
+ _original_weights(nullptr),
+ _weights_flipped(),
_is_prepared(false)
{
}
@@ -47,9 +52,17 @@
unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, 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_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+
+ const DataLayout data_layout = input->data_layout();
+
+ const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1);
ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
const unsigned int stride_x = info.stride().first;
@@ -58,24 +71,34 @@
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);
+ auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h),
+ info.pad().first, info.pad().second, stride_x, stride_y);
- const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+ const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
if(bias != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ if(is_data_type_quantized_asymmetric(input->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ }
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias);
}
- 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.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "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)));
+ unsigned int padx = 0;
+ unsigned int pady = 0;
+ const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady);
+ TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout));
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info));
@@ -84,7 +107,7 @@
return Status{};
}
-void CLDeconvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
@@ -92,36 +115,46 @@
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 DataLayout data_layout = input->info()->data_layout();
- const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
+ const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+ _original_weights = weights;
+ _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
+ _flip_weights.configure(weights, &_weights_flipped);
+
+ auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h),
+ info.pad().first, info.pad().second, stride_x, stride_y);
+
+ const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info());
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout));
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
- _is_prepared = false;
+ _is_prepared = weights_info.retain_internal_weights();
_memory_group.manage(&_scaled_output);
- // configure scale function
- // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
- TensorShape scale_out_shape(input->info()->tensor_shape());
- const unsigned int out_x = input->info()->dimension(0) + (input->info()->dimension(0) - 1) * (stride_x - 1) + inner_border_right + 2 * info.pad().first;
- const unsigned int out_y = input->info()->dimension(1) + (input->info()->dimension(1) - 1) * (stride_y - 1) + inner_border_top + 2 * info.pad().second;
- scale_out_shape.set(0, out_x);
- scale_out_shape.set(1, out_y);
- TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type());
+ // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape
+ unsigned int padx = 0;
+ unsigned int pady = 0;
+ const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady);
+
+ TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info());
+ scale_out_info.set_data_layout(data_layout);
_scaled_output.allocator()->init(scale_out_info);
- _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), info);
+ // configure scale function
+ const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2);
+ _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), upsample_info);
// setup the function to convolve the upscaled output
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
- _conv_f.configure(&_scaled_output, weights, bias, output, conv_info, weights_info);
+ _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info, weights_info);
_scaled_output.allocator()->allocate();
}
@@ -141,7 +174,25 @@
{
if(!_is_prepared)
{
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+ // Run weights flipping and mark original weights tensor as unused
+ _weights_flipped.allocator()->allocate();
+ _weights_flipped.map(true);
+ _original_weights->map(CLScheduler::get().queue(), true);
+ CPPScheduler::get().schedule(&_flip_weights, Window::DimZ);
+ _weights_flipped.unmap();
+ _original_weights->unmap(CLScheduler::get().queue());
+ _original_weights->mark_as_unused();
+
+ // Prepare convolution
_conv_f.prepare();
+
+ if(!_weights_flipped.is_used())
+ {
+ _weights_flipped.allocator()->free();
+ }
+
_is_prepared = true;
}
}