| /* Copyright 2017 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_DEPTHWISECONV_FLOAT_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ |
| |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/compatibility.h" |
| #include "tensorflow/lite/kernels/internal/types.h" |
| |
| namespace tflite { |
| namespace reference_ops { |
| |
| inline void DepthwiseConv( |
| const DepthwiseParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& filter_shape, |
| const float* filter_data, const RuntimeShape& bias_shape, |
| const float* bias_data, const RuntimeShape& output_shape, |
| float* 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 float output_activation_min = params.float_activation_min; |
| const float output_activation_max = params.float_activation_max; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| 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); |
| TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| |
| 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; |
| float total = 0.f; |
| 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)) { |
| float input_value = |
| input_data[Offset(input_shape, b, in_y, in_x, ic)]; |
| float filter_value = filter_data[Offset( |
| filter_shape, 0, filter_y, filter_x, oc)]; |
| total += (input_value * filter_value); |
| } |
| } |
| } |
| float bias_value = 0.0f; |
| if (bias_data) { |
| bias_value = bias_data[oc]; |
| } |
| output_data[Offset(output_shape, b, out_y, out_x, oc)] = |
| ActivationFunctionWithMinMax(total + bias_value, |
| output_activation_min, |
| output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| } // end namespace reference_ops |
| } // end namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ |