| /* |
| * 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 ¶ms, |
| 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 |