diff options
Diffstat (limited to 'tests/validation/CPP/DepthwiseConvolution.cpp')
-rw-r--r-- | tests/validation/CPP/DepthwiseConvolution.cpp | 52 |
1 files changed, 28 insertions, 24 deletions
diff --git a/tests/validation/CPP/DepthwiseConvolution.cpp b/tests/validation/CPP/DepthwiseConvolution.cpp index ad0653846b..229e044783 100644 --- a/tests/validation/CPP/DepthwiseConvolution.cpp +++ b/tests/validation/CPP/DepthwiseConvolution.cpp @@ -137,6 +137,7 @@ SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, co const int input_width = src.shape().x(); const int input_height = src.shape().y(); const int input_depth = src.shape().z(); + const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth); const int filter_half_size = filter_width / 2; const int pad_x = std::min(filter_half_size, static_cast<int>(conv_info.pad().first)); @@ -145,37 +146,40 @@ SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, co const int minimum_y = -pad_y + filter_half_size; int out_pos = 0; - for(int z = 0; z < input_depth; ++z) + for(int r = 0; r < num_batches; ++r) { - 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 z = 0; z < input_depth; ++z) { - for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first) + 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) { - 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 x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first) { - for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i) + 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) { - 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; + 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; } - 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; } } } |