| /* Copyright 2018 The TensorFlow Authors. All Rights Reserved. |
| |
| 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. |
| ==============================================================================*/ |
| #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ |
| |
| #include <limits> |
| #include "tensorflow/lite/kernels/internal/common.h" |
| |
| namespace tflite { |
| namespace reference_integer_ops { |
| |
| inline void AveragePool(const PoolParams& params, |
| const RuntimeShape& input_shape, const int8* input_data, |
| const RuntimeShape& output_shape, int8* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| 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) { |
| for (int channel = 0; channel < depth; ++channel) { |
| 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); |
| int32 acc = 0; |
| int filter_count = 0; |
| 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; |
| acc += |
| input_data[Offset(input_shape, batch, in_y, in_x, channel)]; |
| filter_count++; |
| } |
| } |
| // Round to the closest integer value. |
| acc = acc > 0 ? (acc + filter_count / 2) / filter_count |
| : (acc - filter_count / 2) / filter_count; |
| acc = std::max(acc, params.quantized_activation_min); |
| acc = std::min(acc, params.quantized_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, channel)] = |
| static_cast<int8>(acc); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, |
| const int8* input_data, const RuntimeShape& output_shape, |
| int8* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_GE(params.quantized_activation_min, |
| std::numeric_limits<int8_t>::min()); |
| TFLITE_DCHECK_LE(params.quantized_activation_max, |
| std::numeric_limits<int8_t>::max()); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| 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) { |
| for (int channel = 0; channel < depth; ++channel) { |
| 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); |
| int8_t max = std::numeric_limits<int8_t>::lowest(); |
| 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; |
| max = std::max( |
| max, |
| input_data[Offset(input_shape, batch, in_y, in_x, channel)]); |
| } |
| } |
| max = std::max<int8_t>(max, params.quantized_activation_min); |
| max = std::min<int8_t>(max, params.quantized_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, channel)] = |
| static_cast<int8_t>(max); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void AveragePool(const PoolParams& params, |
| const RuntimeShape& input_shape, |
| const int16* input_data, |
| const RuntimeShape& output_shape, int16* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| 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) { |
| for (int channel = 0; channel < depth; ++channel) { |
| 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); |
| int32 acc = 0; |
| int filter_count = 0; |
| 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; |
| acc += |
| input_data[Offset(input_shape, batch, in_y, in_x, channel)]; |
| filter_count++; |
| } |
| } |
| // Round to the closest integer value. |
| acc = acc > 0 ? (acc + filter_count / 2) / filter_count |
| : (acc - filter_count / 2) / filter_count; |
| acc = std::max(acc, params.quantized_activation_min); |
| acc = std::min(acc, params.quantized_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, channel)] = |
| static_cast<int16>(acc); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, |
| const int16* input_data, const RuntimeShape& output_shape, |
| int16* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_GE(params.quantized_activation_min, |
| std::numeric_limits<int16_t>::min()); |
| TFLITE_DCHECK_LE(params.quantized_activation_max, |
| std::numeric_limits<int16_t>::max()); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| 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) { |
| for (int channel = 0; channel < depth; ++channel) { |
| 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); |
| int16_t max = std::numeric_limits<int16_t>::lowest(); |
| 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; |
| max = std::max( |
| max, |
| input_data[Offset(input_shape, batch, in_y, in_x, channel)]); |
| } |
| } |
| max = std::max<int16_t>(max, params.quantized_activation_min); |
| max = std::min<int16_t>(max, params.quantized_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, channel)] = |
| static_cast<int16_t>(max); |
| } |
| } |
| } |
| } |
| } |
| |
| } // namespace reference_integer_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ |