ac8f6785d3
This kernel currently only supports float type and has filter with the same data format as TensorFlow Conv3D op. The conversion change will be added in a follow-up cl. PiperOrigin-RevId: 351726918 Change-Id: Id2cc805cb89a1da1fda656a2260516eda8bd9119
115 lines
5.0 KiB
C++
115 lines
5.0 KiB
C++
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV3D_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV3D_H_
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace reference_ops {
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inline void Conv3D(const Conv3DParams& params, const RuntimeShape& input_shape,
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const float* input_data, const RuntimeShape& filter_shape,
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const float* filter_data, const RuntimeShape& bias_shape,
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const float* bias_data, const RuntimeShape& output_shape,
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float* output_data) {
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TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 5);
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TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 5);
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TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 5);
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const int batches = MatchingDim(input_shape, 0, output_shape, 0);
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const int input_num_channels = MatchingDim(input_shape, 4, filter_shape, 3);
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const int output_num_channels = MatchingDim(filter_shape, 4, output_shape, 4);
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if (bias_data) {
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TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_num_channels);
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}
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// Only NDHWC format is currently supported.
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const int input_width = input_shape.Dims(3);
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const int input_height = input_shape.Dims(2);
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const int input_depth = input_shape.Dims(1);
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const int filter_width = filter_shape.Dims(2);
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const int filter_height = filter_shape.Dims(1);
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const int filter_depth = filter_shape.Dims(0);
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const int output_width = output_shape.Dims(3);
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const int output_height = output_shape.Dims(2);
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const int output_depth = output_shape.Dims(1);
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const int pad_width = params.padding_values.width;
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const int pad_height = params.padding_values.height;
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const int pad_depth = params.padding_values.depth;
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for (int batch = 0; batch < batches; ++batch) {
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for (int out_d = 0; out_d < output_depth; ++out_d) {
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const int in_d_origin = (out_d * params.stride_depth) - pad_depth;
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for (int out_y = 0; out_y < output_height; ++out_y) {
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const int in_y_origin = (out_y * params.stride_height) - pad_height;
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for (int out_x = 0; out_x < output_width; ++out_x) {
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const int in_x_origin = (out_x * params.stride_width) - pad_width;
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for (int out_channel = 0; out_channel < output_num_channels;
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++out_channel) {
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float total = 0.f;
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for (int filter_d = 0; filter_d < filter_depth; ++filter_d) {
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const int in_d = in_d_origin + params.dilation_depth * filter_d;
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for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
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const int in_y =
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in_y_origin + params.dilation_height * filter_y;
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for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
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const int in_x =
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in_x_origin + params.dilation_width * filter_x;
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// Zero padding by omitting the areas outside the image.
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const bool is_point_inside_image =
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(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
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(in_y < input_height) && (in_d >= 0) &&
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(in_d < input_depth);
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if (!is_point_inside_image) {
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continue;
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}
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for (int in_channel = 0; in_channel < input_num_channels;
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++in_channel) {
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float input_value = input_data[Offset(
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input_shape, batch, in_d, in_y, in_x, in_channel)];
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float filter_value =
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filter_data[Offset(filter_shape, filter_d, filter_y,
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filter_x, in_channel, out_channel)];
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total += (input_value * filter_value);
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}
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}
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}
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}
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float bias_value = 0.0f;
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if (bias_data) {
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bias_value = bias_data[out_channel];
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}
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output_data[Offset(output_shape, batch, out_d, out_y, out_x,
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out_channel)] =
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ActivationFunctionWithMinMax(total + bias_value,
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params.float_activation_min,
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params.float_activation_max);
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}
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}
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}
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}
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}
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}
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} // namespace reference_ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV3D_H_
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