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author | Dmitry Savenko <dsavenko@xored.com> | 2017-11-20 22:00:08 +0700 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | d7295b7079f6b9126596cea998146ca9c6e87706 (patch) | |
tree | bcefca66765ec120090c437621388debe70ae21d /tests/validation/CPP/DepthwiseConvolution.cpp | |
parent | 900b78f599ea5997d60e7538831a906b92265ae0 (diff) | |
download | ComputeLibrary-d7295b7079f6b9126596cea998146ca9c6e87706.tar.gz |
COMPMID-661: Add QASYMM8 support (and basic tests) to CLDepthwiseConvolution3x3 kernel (#28)
Change-Id: I51bebe74e3814c1245812ad575fe7854d460674f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/109864
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/CPP/DepthwiseConvolution.cpp')
-rw-r--r-- | tests/validation/CPP/DepthwiseConvolution.cpp | 83 |
1 files changed, 80 insertions, 3 deletions
diff --git a/tests/validation/CPP/DepthwiseConvolution.cpp b/tests/validation/CPP/DepthwiseConvolution.cpp index e29d014f77..ad0653846b 100644 --- a/tests/validation/CPP/DepthwiseConvolution.cpp +++ b/tests/validation/CPP/DepthwiseConvolution.cpp @@ -26,8 +26,13 @@ #include "ConvolutionLayer.h" #include "Utils.h" +#include "tests/validation/CPP/Utils.h" +#include "tests/validation/CPP/UtilsQuantizedAsymm.h" +#include "tests/validation/FixedPoint.h" #include "tests/validation/Helpers.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + namespace arm_compute { namespace test @@ -44,8 +49,8 @@ namespace reference * - Padding, stride and output shape "match" * */ -template <typename T> -SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info) +template <typename T, typename TB> +SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info) { // Create reference SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position() }; @@ -97,8 +102,80 @@ SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTe } coords.set(0, x); coords.set(1, y); - dst[out_pos++] = saturate_cast<T>(val + *static_cast<const T *>(biases(Coordinates(z)))); + dst[out_pos++] = saturate_cast<T>(val + *static_cast<const TB *>(biases(Coordinates(z)))); + } + } + } + } + + return dst; +} + +template <> +SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape, + const PadStrideInfo &conv_info) +{ + // Create reference + SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() }; + + const int input_offset = -src.quantization_info().offset; + const float input_scale = src.quantization_info().scale; + const int weights_offset = -weights.quantization_info().offset; + const float weights_scale = weights.quantization_info().scale; + const int output_offset = dst.quantization_info().offset; + const float output_scale = dst.quantization_info().scale; + + int output_multiplier; + int output_shift; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + // Compute reference + const int filter_width = weights.shape().x(); + const int filter_height = weights.shape().y(); + const int filter_plane = filter_width * filter_height; + const int input_width = src.shape().x(); + const int input_height = src.shape().y(); + const int input_depth = src.shape().z(); + + const int filter_half_size = filter_width / 2; + const int pad_x = std::min(filter_half_size, static_cast<int>(conv_info.pad().first)); + const int pad_y = std::min(filter_half_size, static_cast<int>(conv_info.pad().second)); + const int minimum_x = -pad_x + filter_half_size; + const int minimum_y = -pad_y + filter_half_size; + + int out_pos = 0; + for(int z = 0; z < input_depth; ++z) + { + int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(z))); + for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second) + { + for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first) + { + Coordinates coords(x, y, z); + int filter_offset = filter_plane * z; + + uint32_t val = 0; + for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j) + { + for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i) + { + coords.set(0, i); + coords.set(1, j); + auto in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, 0); + uint8_t w_val = *(weights.data() + filter_offset); + val += (in_val + input_offset) * (w_val + weights_offset); + ++filter_offset; + } } + val += bias_val; + val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift); + val += output_offset; + val = std::max<int32_t>(val, 0); + val = std::min<int32_t>(val, 255); + + // Store the result + dst[out_pos++] = val; } } } |