| /* 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_INTEGER_OPS_DEPTHWISE_CONV_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ |
| |
| #include "tensorflow/lite/kernels/internal/common.h" |
| |
| namespace tflite { |
| namespace reference_integer_ops { |
| inline void DepthwiseConvPerChannel( |
| const DepthwiseParams& params, const int32* output_multiplier, |
| const int32* output_shift, const RuntimeShape& input_shape, |
| const int8* input_data, const RuntimeShape& filter_shape, |
| const int8* filter_data, const RuntimeShape& bias_shape, |
| const int32* bias_data, const RuntimeShape& output_shape, |
| int8* output_data) { |
| // Get parameters. |
| // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. |
| 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 int32 input_offset = params.input_offset; |
| const int32 output_offset = params.output_offset; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| |
| // Check dimensions of the tensors. |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| 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 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 in_channel = 0; in_channel < input_depth; ++in_channel) { |
| for (int m = 0; m < depth_multiplier; ++m) { |
| const int output_channel = m + in_channel * depth_multiplier; |
| 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) { |
| const int in_x = in_x_origin + dilation_width_factor * filter_x; |
| const int in_y = |
| in_y_origin + dilation_height_factor * filter_y; |
| // Zero padding by omitting the areas outside the image. |
| const bool is_point_inside_image = |
| (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && |
| (in_y < input_height); |
| if (is_point_inside_image) { |
| int32 input_val = input_data[Offset(input_shape, batch, in_y, |
| in_x, in_channel)]; |
| int32 filter_val = filter_data[Offset( |
| filter_shape, 0, filter_y, filter_x, output_channel)]; |
| // Accumulate with 32 bits accumulator. |
| // In the nudging process during model quantization, we force |
| // real value of 0.0 be represented by a quantized value. This |
| // guarantees that the input_offset is a int8, even though it |
| // is represented using int32. |
| // int32 += int8 * (int8 - int8) so the highest value we can |
| // get from each accumulation is [-127, 127] * ([-128, 127] - |
| // [-128, 127]), which is [-32512, 32512]. log2(32512) |
| // = 14.98, which means we can accumulate at least 2^16 |
| // multiplications without overflow. The accumulator is |
| // applied to a filter so the accumulation logic will hold as |
| // long as the filter size (filter_y * filter_x * in_channel) |
| // does not exceed 2^16, which is the case in all the models |
| // we have seen so far. |
| // TODO(jianlijianli): Add a check to make sure the |
| // accumulator depth is smaller than 2^16. |
| acc += filter_val * (input_val + input_offset); |
| } |
| } |
| } |
| if (bias_data) { |
| acc += bias_data[output_channel]; |
| } |
| acc = MultiplyByQuantizedMultiplier( |
| acc, output_multiplier[output_channel], |
| output_shift[output_channel]); |
| 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, |
| output_channel)] = static_cast<int8_t>(acc); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| inline void DepthwiseConvPerChannel( |
| const DepthwiseParams& params, const int32* output_multiplier, |
| const int32* output_shift, const RuntimeShape& input_shape, |
| const int16* input_data, const RuntimeShape& filter_shape, |
| const int8* filter_data, const RuntimeShape& bias_shape, |
| const std::int64_t* bias_data, const RuntimeShape& output_shape, |
| int16* output_data) { |
| // Get parameters. |
| 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 int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| |
| // Check dimensions of the tensors. |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| 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 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 in_channel = 0; in_channel < input_depth; ++in_channel) { |
| for (int m = 0; m < depth_multiplier; ++m) { |
| const int output_channel = m + in_channel * depth_multiplier; |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| std::int64_t acc = 0; |
| 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; |
| // Zero padding by omitting the areas outside the image. |
| const bool is_point_inside_image = |
| (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && |
| (in_y < input_height); |
| if (is_point_inside_image) { |
| int32 input_val = input_data[Offset(input_shape, batch, in_y, |
| in_x, in_channel)]; |
| int32 filter_val = filter_data[Offset( |
| filter_shape, 0, filter_y, filter_x, output_channel)]; |
| // Accumulate with 64 bits accumulator. |
| // We assume maximum of 2^16 accumulations as with the 8-bit |
| // case so actually the value in the accumulator should not |
| // exceed 40 bits |
| acc += static_cast<int64_t>(filter_val) * |
| static_cast<int64_t>(input_val); |
| } |
| } |
| } |
| if (bias_data) { |
| acc += bias_data[output_channel]; |
| } |
| int32 scaled_acc = MultiplyByQuantizedMultiplier( |
| acc, output_multiplier[output_channel], |
| output_shift[output_channel]); |
| scaled_acc = std::max(scaled_acc, output_activation_min); |
| scaled_acc = std::min(scaled_acc, output_activation_max); |
| output_data[Offset(output_shape, batch, out_y, out_x, |
| output_channel)] = |
| static_cast<int16_t>(scaled_acc); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| inline void DepthwiseConvHybridPerChannel( |
| const DepthwiseParams& params, float* scaling_factors_ptr, |
| const RuntimeShape& input_shape, const int8* input_data, |
| const RuntimeShape& filter_shape, const int8* filter_data, |
| const RuntimeShape& bias_shape, const float* bias_data, |
| const RuntimeShape& output_shape, float* output_data, |
| const float* per_channel_scale, int32_t* input_offset) { |
| 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; |
| // Check dimensions of the tensors. |
| 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); |
| const int bias_depth = bias_shape.FlatSize(); |
| TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); |
| TFLITE_DCHECK_EQ(bias_depth, output_depth); |
| |
| 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 in_channel = 0; in_channel < input_depth; ++in_channel) { |
| for (int m = 0; m < depth_multiplier; ++m) { |
| const int output_channel = m + in_channel * depth_multiplier; |
| 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) { |
| const int in_x = in_x_origin + dilation_width_factor * filter_x; |
| const int in_y = |
| in_y_origin + dilation_height_factor * filter_y; |
| // Zero padding by omitting the areas outside the image. |
| const bool is_point_inside_image = |
| (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && |
| (in_y < input_height); |
| if (is_point_inside_image) { |
| int32 input_val = input_data[Offset(input_shape, batch, in_y, |
| in_x, in_channel)]; |
| int32 filter_val = filter_data[Offset( |
| filter_shape, 0, filter_y, filter_x, output_channel)]; |
| acc += filter_val * (input_val - input_offset[batch]); |
| } |
| } |
| } |
| float acc_float = static_cast<float>(acc); |
| acc_float *= |
| per_channel_scale[output_channel] * scaling_factors_ptr[batch]; |
| if (bias_data && output_channel < bias_depth) { |
| acc_float += bias_data[output_channel]; |
| } |
| output_data[Offset(output_shape, batch, out_y, out_x, |
| output_channel)] = |
| ActivationFunctionWithMinMax(acc_float, output_activation_min, |
| output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| } // namespace reference_integer_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ |