COMPMID-661: directconv-uint8 (#20)
Change-Id: I84f7a1ce3658be0d3c91e65096467258af48f0b6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94341
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/CPP/ConvolutionLayer.cpp b/tests/validation/CPP/ConvolutionLayer.cpp
index ab3690a..aa73869 100644
--- a/tests/validation/CPP/ConvolutionLayer.cpp
+++ b/tests/validation/CPP/ConvolutionLayer.cpp
@@ -23,11 +23,15 @@
*/
#include "ConvolutionLayer.h"
+#include "tests/validation/CPP/Utils.h"
+#include "tests/validation/CPP/UtilsQuantizedAsymm.h"
#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/framework/Asserts.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
namespace arm_compute
{
namespace test
@@ -45,9 +49,14 @@
// 3D convolution for floating point type
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
-void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int fixed_point_position)
+void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, SimpleTensor<T> &out,
+ int i_offset, int w_offset, int b_offset, int o_offset,
+ int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
+ const T *in_ptr = in.data() + i_offset;
+ const T *w_ptr = weights.data() + w_offset;
+ const T *b_ptr = bias.data() + b_offset;
+ T *out_ptr = out.data() + o_offset;
const int half_width_weights = width_weights / 2;
const int half_height_weights = height_weights / 2;
@@ -72,8 +81,8 @@
const int idx = xk + half_width_weights;
const int idy = yk + half_height_weights;
- const T i_value = in[offset_slice_in + xk + yk * width_in];
- const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights];
+ const T i_value = in_ptr[offset_slice_in + xk + yk * width_in];
+ const T w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
acc += i_value * w_value;
}
@@ -82,14 +91,21 @@
}
// Accumulate the bias and store the result
- *out = acc + (*bias);
+ *out_ptr = acc + (*b_ptr);
}
// 3D convolution for fixed point type
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights,
- int fixed_point_position)
+void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, SimpleTensor<T> &out,
+ int i_offset, int w_offset, int b_offset, int o_offset,
+ int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
{
+ const T *in_ptr = in.data() + i_offset;
+ const T *w_ptr = weights.data() + w_offset;
+ const T *b_ptr = bias.data() + b_offset;
+ T *out_ptr = out.data() + o_offset;
+ int fixed_point_position = in.fixed_point_position();
+
const int half_width_weights = width_weights / 2;
const int half_height_weights = height_weights / 2;
@@ -116,8 +132,8 @@
const int idx = xk + half_width_weights;
const int idy = yk + half_height_weights;
- const fixed_point<promoted_type> i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
- const fixed_point<promoted_type> w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
+ const fixed_point<promoted_type> i_value(in_ptr[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
const fixed_point<promoted_type> iw = i_value * w_value;
acc = iw + acc;
}
@@ -126,12 +142,79 @@
}
// Get the bias
- const fixed_point<promoted_type> b(*bias, fixed_point_position, true);
+ const fixed_point<promoted_type> b(*b_ptr, fixed_point_position, true);
// Accumulate the bias and covert back
acc = acc + b;
fixed_point<T> res(acc);
- *out = res.raw();
+ *out_ptr = res.raw();
+}
+
+// 3D convolution for QASYMM8 type
+template <>
+void convolution3d(const SimpleTensor<uint8_t> &in, const SimpleTensor<uint8_t> &weights, const SimpleTensor<uint8_t> &bias, SimpleTensor<uint8_t> &out,
+ int i_offset, int w_offset, int b_offset, int o_offset,
+ int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights)
+{
+ const uint8_t *in_ptr = in.data() + i_offset;
+ const uint8_t *w_ptr = weights.data() + w_offset;
+ const uint8_t *b_ptr = bias.data() + b_offset;
+ uint8_t *out_ptr = out.data() + o_offset;
+
+ const int input_offset = -in.quantization_info().offset;
+ const float input_scale = in.quantization_info().scale;
+ const int weights_offset = -weights.quantization_info().offset;
+ const float weights_scale = weights.quantization_info().scale;
+ const int output_offset = out.quantization_info().offset;
+ const float output_scale = out.quantization_info().scale;
+
+ int output_multiplier = 0;
+ int output_shift = 0;
+ const float multiplier = input_scale * weights_scale / output_scale;
+ arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ const int half_width_weights = width_weights / 2;
+ const int half_height_weights = height_weights / 2;
+
+ // Reset accumulator
+ int32_t acc(0);
+
+ // Compute a 2D convolution for each IFM and accumulate the result
+ for(int ifm = 0; ifm < depth_in; ++ifm)
+ {
+ // Compute the offset for the input slice
+ const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
+
+ // Compute 2D convolution
+ for(int yk = -half_height_weights; yk <= half_height_weights; ++yk)
+ {
+ for(int xk = -half_width_weights; xk <= half_width_weights; ++xk)
+ {
+ // Check if the pixel is out-of-bound
+ if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in))
+ {
+ const int idx = xk + half_width_weights;
+ const int idy = yk + half_height_weights;
+
+ const uint8_t i_value = in_ptr[offset_slice_in + xk + yk * width_in];
+ const uint8_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights];
+
+ acc += (i_value + input_offset) * (w_value + weights_offset);
+ }
+ }
+ }
+ }
+
+ // Accumulate the bias
+ acc += (*b_ptr);
+
+ acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift);
+ acc += output_offset;
+ acc = std::max<int32_t>(acc, 0);
+ acc = std::min<int32_t>(acc, 255);
+
+ // Store the result
+ *out_ptr = acc;
}
} // namespace
@@ -139,7 +222,7 @@
SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info)
{
// Create reference
- SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position() };
+ SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
// Compute reference
const int width_in = src.shape().x();
@@ -182,14 +265,11 @@
ARM_COMPUTE_ASSERT(yo < height_out);
// Compute 3D convolution
- convolution3d(src.data() + offset_in,
- weights.data() + ofm * width_weights * height_weights * depth_weights,
- bias.data() + ofm,
- dst.data() + offset_out,
+ convolution3d(src, weights, bias, dst,
+ offset_in, ofm * width_weights * height_weights * depth_weights, ofm, offset_out,
xi, yi,
width_in, height_in, depth_in,
- width_weights, height_weights,
- src.fixed_point_position());
+ width_weights, height_weights);
}
}
}
@@ -206,6 +286,8 @@
const PadStrideInfo &info);
template SimpleTensor<qint16_t> convolution_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &output_shape,
const PadStrideInfo &info);
+template SimpleTensor<uint8_t> convolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<uint8_t> &bias, const TensorShape &output_shape,
+ const PadStrideInfo &info);
} // namespace reference
} // namespace validation
} // namespace test