COMPMID-471 Implement Deconvolution on OpenCL
Change-Id: Ie00c6b08a51d30c5ce2637d40ee3d165b8a68686
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110311
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index e98f5e9..f2455f3 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017, 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -43,20 +43,15 @@
class DeconvolutionLayerFixtureBase : public framework::Fixture
{
public:
- /*
- *
- * @param[in] a The number of zeros added to right and bottom edges of the input.
- * @param[in] u How much to scale the X and Y axis.
- */
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
- const std::pair<unsigned int, unsigned int> &a, const std::pair<unsigned int, unsigned int> &u, DataType data_type, int fractional_bits)
+ const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, int fractional_bits)
{
_fractional_bits = fractional_bits;
_data_type = data_type;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, a, u, data_type, fractional_bits);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, a, data_type, fractional_bits);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, fractional_bits);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, fractional_bits);
}
protected:
@@ -75,13 +70,9 @@
library->fill_tensor_uniform(tensor, i);
}
}
- /*
- *
- * @param[in] a The number of zeros added to right and bottom edges of the input.
- * @param[in] u How much to scale the X and Y axis.
- */
+
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
- const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a, const std::pair<float, float> &u, DataType data_type, int fixed_point_position)
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, int fixed_point_position)
{
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
@@ -91,7 +82,7 @@
// Create and configure function
FunctionType conv;
- conv.configure(&src, &weights, &bias, &dst, info, a.first, a.second, u.first, u.second);
+ conv.configure(&src, &weights, &bias, &dst, info, inner_border.first, inner_border.second);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -121,7 +112,7 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
- const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> a, DataType data_type, int fixed_point_position)
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border, DataType data_type, int fixed_point_position)
{
// Create reference
SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
@@ -133,7 +124,7 @@
fill(weights, 1);
fill(bias, 2);
- return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, a);
+ return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, inner_border);
}
TensorType _target{};
@@ -148,18 +139,16 @@
public:
template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
- unsigned int ax, unsigned int ay, unsigned int ux, unsigned int uy, unsigned int num_kernels, DataType data_type)
+ unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type)
{
ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
- const std::pair<unsigned int, unsigned int> a(ax, ay);
- const std::pair<float, float> u(ux, uy);
- auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, a.first, a.second, u.first, u.second,
- DimensionRoundingType::CEIL);
+ const std::pair<unsigned int, unsigned int> inner_border(inner_border_right, inner_border_top);
+ auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, inner_border.first, inner_border.second, sx, sy);
TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, a, u, data_type, 0);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, 0);
}
};