| /* Copyright 2019 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_CONV_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ |
| |
| #include "tensorflow/lite/kernels/internal/types.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| |
| |
| |
| namespace tflite { |
| |
| namespace reference_ops { |
| |
| |
| inline void Conv(const ConvParams& 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 RuntimeShape& im2col_shape, |
| float* im2col_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 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); |
| |
| (void)im2col_data; // only used in optimized code. |
| (void)im2col_shape; // only used in optimized code. |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); |
| const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); |
| if (bias_data) { |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| } |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| 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); |
| 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 out_channel = 0; out_channel < output_depth; ++out_channel) { |
| 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) { |
| for (int in_channel = 0; in_channel < input_depth; ++in_channel) { |
| 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, batch, in_y, in_x, in_channel)]; |
| float filter_value = |
| filter_data[Offset(filter_shape, out_channel, filter_y, |
| filter_x, in_channel)]; |
| total += (input_value * filter_value); |
| } |
| } |
| } |
| } |
| float bias_value = 0.0f; |
| if (bias_data) { |
| bias_value = bias_data[out_channel]; |
| } |
| output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = |
| ActivationFunctionWithMinMax(total + bias_value, |
| output_activation_min, |
| output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& filter_shape, |
| const uint8* filter_data, const RuntimeShape& bias_shape, |
| const int32* bias_data, const RuntimeShape& output_shape, |
| uint8* output_data, const RuntimeShape& im2col_shape, |
| uint8* im2col_data, void* cpu_backend_context) { |
| (void)cpu_backend_context; // only used in optimized code. |
| (void)im2col_data; // only used in optimized code. |
| (void)im2col_shape; // only used in optimized code. |
| 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 int32 input_offset = params.input_offset; |
| const int32 filter_offset = params.weights_offset; |
| const int32 output_offset = params.output_offset; |
| const int32 output_multiplier = params.output_multiplier; |
| const int output_shift = params.output_shift; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| TFLITE_DCHECK_LE(output_activation_min, output_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 input_depth = MatchingDim(input_shape, 3, filter_shape, 3); |
| const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); |
| if (bias_data) { |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| } |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| 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); |
| 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 out_channel = 0; out_channel < output_depth; ++out_channel) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| int32 acc = 0; |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| for (int in_channel = 0; in_channel < input_depth; ++in_channel) { |
| 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)) { |
| int32 input_val = input_data[Offset(input_shape, batch, in_y, |
| in_x, in_channel)]; |
| int32 filter_val = |
| filter_data[Offset(filter_shape, out_channel, filter_y, |
| filter_x, in_channel)]; |
| acc += |
| (filter_val + filter_offset) * (input_val + input_offset); |
| } |
| } |
| } |
| } |
| if (bias_data) { |
| acc += bias_data[out_channel]; |
| } |
| acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, |
| output_shift); |
| acc += output_offset; |
| acc = std::max(acc, output_activation_min); |
| acc = std::min(acc, output_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = |
| static_cast<uint8>(acc); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void HybridConvPerChannel( |
| const ConvParams& params, float* scaling_factors_ptr, |
| const RuntimeShape& input_shape, const int8_t* input_data, |
| const RuntimeShape& filter_shape, const int8_t* filter_data, |
| const RuntimeShape& bias_shape, const float* bias_data, |
| const RuntimeShape& output_shape, float* output_data, |
| const RuntimeShape& im2col_shape, int8_t* im2col_data, |
| const float* per_channel_scale, int32_t* input_offset) { |
| (void)im2col_data; // only used in optimized code. |
| (void)im2col_shape; // only used in optimized code. |
| 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 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 input_depth = MatchingDim(input_shape, 3, filter_shape, 3); |
| const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); |
| if (bias_data) { |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); |
| } |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| 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); |
| 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 out_channel = 0; out_channel < output_depth; ++out_channel) { |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| int32 acc = 0; |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| for (int in_channel = 0; in_channel < input_depth; ++in_channel) { |
| 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)) { |
| int32 input_val = input_data[Offset(input_shape, batch, in_y, |
| in_x, in_channel)]; |
| int32 filter_val = |
| filter_data[Offset(filter_shape, out_channel, filter_y, |
| filter_x, in_channel)]; |
| acc += filter_val * (input_val - input_offset[batch]); |
| } |
| } |
| } |
| } |
| float acc_float = |
| acc * per_channel_scale[out_channel] * scaling_factors_ptr[batch]; |
| if (bias_data) { |
| acc_float += bias_data[out_channel]; |
| } |
| output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = |
| ActivationFunctionWithMinMax(acc_float, output_activation_min, |
| output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| |
| } // namespace reference_ops |
| } // namespace tflite |
| |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ |