diff options
Diffstat (limited to 'tests/validation/CPP/ConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/CPP/ConvolutionLayer.cpp | 118 |
1 files changed, 100 insertions, 18 deletions
diff --git a/tests/validation/CPP/ConvolutionLayer.cpp b/tests/validation/CPP/ConvolutionLayer.cpp index ab3690a493..aa73869a0e 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 @@ inline bool is_valid_pixel(int i, int min, int max) // 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 @@ void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, 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 @@ void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, } // 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 @@ void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, 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 @@ void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, } // 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 @@ template <typename T> 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 @@ SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor 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 @@ template SimpleTensor<qint8_t> convolution_layer(const SimpleTensor<qint8_t> &sr 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 |