diff options
Diffstat (limited to 'compute/cker/include/cker/operation/DepthwiseConv.h')
| -rw-r--r-- | compute/cker/include/cker/operation/DepthwiseConv.h | 126 |
1 files changed, 38 insertions, 88 deletions
diff --git a/compute/cker/include/cker/operation/DepthwiseConv.h b/compute/cker/include/cker/operation/DepthwiseConv.h index 7d022477d..814a9e019 100644 --- a/compute/cker/include/cker/operation/DepthwiseConv.h +++ b/compute/cker/include/cker/operation/DepthwiseConv.h @@ -21,118 +21,68 @@ #include "cker/Shape.h" #include "cker/Types.h" #include "cker/Utils.h" +#include "cker/neon/neon_check.h" +#include "cker/operation/optimized/DepthwiseConvUint8.h" namespace nnfw { namespace cker { -struct DepthwiseConvParams -{ - PaddingType padding_type; - PaddingValues padding_values; - int16_t stride_width; - int16_t stride_height; - int16_t dilation_width_factor; - int16_t dilation_height_factor; - int16_t depth_multiplier; - // uint8 inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - inline void DepthwiseConv(const DepthwiseConvParams ¶ms, const Shape &input_shape, const uint8_t *input_data, const Shape &filter_shape, const uint8_t *filter_data, const Shape &bias_shape, const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; const int depth_multiplier = params.depth_multiplier; const int32_t output_activation_min = params.quantized_activation_min; const int32_t output_activation_max = params.quantized_activation_max; - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + assert(dilation_width_factor >= 1); + assert(dilation_height_factor >= 1); + UNUSED_RELEASE(dilation_width_factor); + UNUSED_RELEASE(dilation_height_factor); assert(input_shape.DimensionsCount() == 4); assert(filter_shape.DimensionsCount() == 4); assert(output_shape.DimensionsCount() == 4); - assert(output_activation_min <= output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); + UNUSED_RELEASE(output_activation_min); + UNUSED_RELEASE(output_activation_max); const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); assert(output_depth == input_depth * depth_multiplier); assert(bias_shape.FlatSize() == output_depth); + UNUSED_RELEASE(input_depth); UNUSED_RELEASE(output_depth); - UNUSED_RELEASE(bias_shape); + UNUSED_RELEASE(depth_multiplier); - for (int b = 0; b < batches; ++b) - { - for (int out_y = 0; out_y < output_height; ++out_y) - { - for (int out_x = 0; out_x < output_width; ++out_x) - { - for (int ic = 0; ic < input_depth; ++ic) - { - for (int m = 0; m < depth_multiplier; m++) - { - const int oc = m + ic * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) - { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) - { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height)) - { - int32_t input_val = input_data[Offset(input_shape, b, in_y, in_x, ic)]; - int32_t filter_val = filter_data[Offset(filter_shape, 0, filter_y, filter_x, oc)]; - acc += (filter_val + filter_offset) * (input_val + input_offset); - } - } - } - if (bias_data) - { - acc += bias_data[oc]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, b, out_y, out_x, oc)] = static_cast<uint8_t>(acc); - } - } - } - } - } +// Enable for arm64 except for the Nvidia Linux 4 Tegra (L4T) running on +// Jetson TX-2. This compiler does not support the offsetof() macro. +#if defined(__aarch64__) +// TODO Use below codes + +// const int stride_width = params.stride_width; +// const int stride_height = params.stride_height; +// const int pad_width = params.padding_values.width; +// const int pad_height = params.padding_values.height; +// const int output_shift = params.output_shift; +// +// // Call kernel optimized for depthwise convolutions using 3x3 filters if +// // parameters are supported. +// if (Fast3x3FilterKernelSupported( +// input_shape, filter_shape, stride_width, stride_height, +// dilation_width_factor, dilation_height_factor, pad_width, pad_height, +// depth_multiplier, output_shape, output_shift)) { +// DepthwiseConv3x3Filter(params, input_shape, input_data, filter_shape, +// filter_data, bias_shape, bias_data, output_shape, +// output_data); +// return; +// } +#endif + + optimized::DepthwiseConvGeneral(params, input_shape, input_data, filter_shape, filter_data, + bias_shape, bias_data, output_shape, output_data); } inline void DepthwiseConv(const DepthwiseConvParams ¶ms, const Shape &input_shape, |