blob: 598674617c0dadf247ac89f6249ccb014df9b6d5 [file] [log] [blame]
/*
* Copyright 2023 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "crt/kelvin.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/runtime_shape.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace kelvin::opt {
void MaxPoolGeneric(const tflite::PoolParams &params,
const tflite::RuntimeShape &input_shape,
const int8_t *input_data,
const tflite::RuntimeShape &output_shape,
int8_t *output_data) {
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int channel = 0;
for (; channel + 32 <= depth; channel += 32) {
vdup_b_x(v0, params.quantized_activation_min);
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
const int8_t *local_input =
input_data + Offset(input_shape, batch, in_y, in_x, channel);
vld_b_x(v1, local_input);
vmax_b_vv(v0, v0, v1);
}
}
vmin_b_vx(v0, v0, params.quantized_activation_max);
int8_t *local_output =
output_data + Offset(output_shape, batch, out_y, out_x, channel);
vst_b_x(v0, local_output);
}
if (channel == depth) {
continue;
}
int remaining_channels = depth - channel;
vdup_b_x(v0, params.quantized_activation_min);
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
const int8_t *local_input =
input_data + Offset(input_shape, batch, in_y, in_x, depth - 1);
vld_b_l_xx(v1, local_input, remaining_channels);
vmax_b_vv(v0, v0, v1);
}
}
vmin_b_vx(v0, v0, params.quantized_activation_max);
int8_t *local_output =
output_data + Offset(output_shape, batch, out_y, out_x, depth - 1);
vst_b_l_xx(v0, local_output, remaining_channels);
}
}
}
}
} // namespace kelvin::opt