| /* 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. |
| ==============================================================================*/ |
| |
| #include "arm_nnfunctions.h" |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/c_api_internal.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h" |
| #include "tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/padding.h" |
| #include "tensorflow/lite/experimental/micro/kernels/cmsis-nn/scratch_buffer.h" |
| |
| namespace tflite { |
| namespace ops { |
| namespace micro { |
| namespace depthwise_conv { |
| namespace { |
| |
| constexpr int kInputTensor = 0; |
| constexpr int kFilterTensor = 1; |
| constexpr int kBiasTensor = 2; |
| constexpr int kOutputTensor = 0; |
| constexpr int kMaxChannels = 256; |
| |
| struct OpData { |
| TfLitePaddingValues padding; |
| // The scaling factor from input to output (aka the 'real multiplier') can |
| // be represented as a fixed point multiplier plus a left shift. |
| int32_t output_multiplier; |
| int output_shift; |
| |
| // Per channel output multiplier and shift. |
| // TODO(b/141139247): Allocate these dynamically when possible. |
| int32_t per_channel_output_multiplier[kMaxChannels]; |
| int32_t per_channel_output_shift[kMaxChannels]; |
| |
| // The range of the fused activation layer. For example for kNone and |
| // uint8_t these would be 0 and 255. |
| int32_t output_activation_min; |
| int32_t output_activation_max; |
| }; |
| |
| TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, |
| TfLiteDepthwiseConvParams* params, int width, |
| int height, int filter_width, int filter_height, |
| const TfLiteType data_type, OpData* data) { |
| bool has_bias = node->inputs->size == 3; |
| // Check number of inputs/outputs |
| TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); |
| TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); |
| |
| int unused_output_height, unused_output_width; |
| data->padding = ComputePaddingHeightWidth( |
| params->stride_height, params->stride_width, 1, 1, height, width, |
| filter_height, filter_width, params->padding, &unused_output_height, |
| &unused_output_width); |
| |
| // Note that quantized inference requires that all tensors have their |
| // parameters set. This is usually done during quantized training. |
| if (data_type != kTfLiteFloat32) { |
| const TfLiteTensor* input = GetInput(context, node, kInputTensor); |
| const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); |
| const TfLiteTensor* bias = |
| GetOptionalInputTensor(context, node, kBiasTensor); |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| |
| TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams( |
| context, input, filter, bias, output, params->activation, |
| &data->output_multiplier, &data->output_shift, |
| &data->output_activation_min, &data->output_activation_max, |
| data->per_channel_output_multiplier, |
| reinterpret_cast<int*>(data->per_channel_output_shift))); |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| return nullptr; |
| } |
| |
| void Free(TfLiteContext* context, void* buffer) {} |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, |
| TfLiteDepthwiseConvParams* params, OpData* data, |
| const TfLiteTensor* input, const TfLiteTensor* filter, |
| const TfLiteTensor* bias, TfLiteTensor* output) { |
| float output_activation_min, output_activation_max; |
| CalculateActivationRange(params->activation, &output_activation_min, |
| &output_activation_max); |
| |
| tflite::DepthwiseParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = data->padding.width; |
| op_params.padding_values.height = data->padding.height; |
| op_params.stride_width = params->stride_width; |
| op_params.stride_height = params->stride_height; |
| op_params.dilation_width_factor = 1; |
| op_params.dilation_height_factor = 1; |
| op_params.depth_multiplier = params->depth_multiplier; |
| op_params.float_activation_min = output_activation_min; |
| op_params.float_activation_max = output_activation_max; |
| |
| tflite::reference_ops::DepthwiseConv( |
| op_params, GetTensorShape(input), GetTensorData<float>(input), |
| GetTensorShape(filter), GetTensorData<float>(filter), |
| GetTensorShape(bias), GetTensorData<float>(bias), GetTensorShape(output), |
| GetTensorData<float>(output)); |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node, |
| TfLiteDepthwiseConvParams* params, OpData* data, |
| const TfLiteTensor* input, |
| const TfLiteTensor* filter, |
| const TfLiteTensor* bias, TfLiteTensor* output) { |
| #if defined(ARM_MATH_DSP) && defined(ARM_MATH_LOOPUNROLL) |
| DepthwiseParams op_params; |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = data->padding.width; |
| op_params.padding_values.height = data->padding.height; |
| op_params.stride_width = params->stride_width; |
| op_params.stride_height = params->stride_height; |
| op_params.dilation_width_factor = params->dilation_width_factor; |
| op_params.dilation_height_factor = params->dilation_height_factor; |
| op_params.depth_multiplier = params->depth_multiplier; |
| op_params.input_offset = -input->params.zero_point; |
| op_params.weights_offset = 0; |
| op_params.output_offset = output->params.zero_point; |
| // TODO(b/130439627): Use calculated value for clamping. |
| op_params.quantized_activation_min = std::numeric_limits<int8_t>::min(); |
| op_params.quantized_activation_max = std::numeric_limits<int8_t>::max(); |
| RuntimeShape filter_shape = GetTensorShape(filter); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| RuntimeShape input_shape = GetTensorShape(input); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| RuntimeShape output_shape = GetTensorShape(output); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| RuntimeShape bias_shape = GetTensorShape(bias); |
| |
| if (op_params.depth_multiplier == 1) { |
| int16_t* buf = nullptr; |
| const int32_t buf_size = |
| arm_depthwise_conv_s8_opt_get_buffer_size(input_depth, |
| filter_width, |
| filter_height); |
| TF_LITE_ENSURE_OK(context, |
| get_cmsis_scratch_buffer(context, &buf, buf_size)); |
| TF_LITE_ENSURE_EQ(context, |
| arm_depthwise_conv_s8_opt( |
| GetTensorData<int8_t>(input), |
| input_width, input_height, input_depth, |
| GetTensorData<int8_t>(filter), |
| input_depth, |
| filter_width, filter_height, |
| op_params.padding_values.width, |
| op_params.padding_values.height, |
| op_params.stride_width, |
| op_params.stride_height, |
| GetTensorData<int32>(bias), |
| GetTensorData<int8_t>(output), |
| data->per_channel_output_shift, |
| data->per_channel_output_multiplier, |
| output_width, |
| output_height, |
| op_params.output_offset, |
| op_params.input_offset, |
| op_params.quantized_activation_min, |
| op_params.quantized_activation_max, |
| op_params.dilation_width_factor, |
| op_params.dilation_height_factor, |
| buf), |
| ARM_MATH_SUCCESS); |
| } else { |
| TF_LITE_ENSURE_EQ(context, |
| arm_depthwise_conv_s8( |
| GetTensorData<int8_t>(input), |
| input_width, input_height, input_depth, |
| GetTensorData<int8_t>(filter), |
| op_params.depth_multiplier * input_depth, |
| op_params.depth_multiplier, |
| filter_width, filter_height, |
| op_params.padding_values.width, |
| op_params.padding_values.height, |
| op_params.stride_width, |
| op_params.stride_height, |
| GetTensorData<int32>(bias), |
| GetTensorData<int8_t>(output), |
| data->per_channel_output_shift, |
| data->per_channel_output_multiplier, |
| output_width, |
| output_height, |
| op_params.output_offset, |
| op_params.input_offset, |
| op_params.quantized_activation_min, |
| op_params.quantized_activation_max, |
| op_params.dilation_width_factor, |
| op_params.dilation_height_factor, |
| nullptr), |
| ARM_MATH_SUCCESS); |
| } |
| #else |
| #error ARM_MATH_DSP and ARM_MATH_LOOPUNROLL must be set |
| #endif |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, |
| TfLiteDepthwiseConvParams* params, OpData* data, |
| const TfLiteTensor* input, const TfLiteTensor* filter, |
| const TfLiteTensor* bias, TfLiteTensor* output) { |
| const int32_t input_offset = -input->params.zero_point; |
| const int32_t filter_offset = -filter->params.zero_point; |
| const int32_t output_offset = output->params.zero_point; |
| |
| tflite::DepthwiseParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = data->padding.width; |
| op_params.padding_values.height = data->padding.height; |
| op_params.stride_width = params->stride_width; |
| op_params.stride_height = params->stride_height; |
| op_params.dilation_width_factor = 1; |
| op_params.dilation_height_factor = 1; |
| op_params.depth_multiplier = params->depth_multiplier; |
| op_params.quantized_activation_min = data->output_activation_min; |
| op_params.quantized_activation_max = data->output_activation_max; |
| op_params.input_offset = input_offset; |
| op_params.weights_offset = filter_offset; |
| op_params.output_offset = output_offset; |
| op_params.output_multiplier = data->output_multiplier; |
| // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. |
| op_params.output_shift = -data->output_shift; |
| |
| #if defined(ARM_MATH_DSP) |
| // optimizations utilize loop unrolling which requires the following power |
| // of two kernel dimensions |
| RuntimeShape filter_shape = GetTensorShape(filter); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| if (0 == op_params.depth_multiplier % 2 && 0 == filter_width % 2) { |
| RuntimeShape input_shape = GetTensorShape(input); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = input_shape.Dims(3); |
| RuntimeShape output_shape = GetTensorShape(output); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| arm_depthwise_conv_u8_basic_ver1( |
| GetTensorData<uint8_t>(input), input_width, input_height, input_depth, |
| GetTensorData<uint8_t>(filter), filter_width, filter_height, |
| op_params.depth_multiplier, op_params.padding_values.width, |
| op_params.padding_values.height, op_params.stride_width, |
| op_params.stride_height, op_params.dilation_width_factor, |
| op_params.dilation_height_factor, GetTensorData<int32_t>(bias), |
| op_params.input_offset, op_params.weights_offset, |
| op_params.output_offset, GetTensorData<uint8_t>(output), output_width, |
| output_height, op_params.quantized_activation_min, |
| op_params.quantized_activation_max, op_params.output_shift, |
| op_params.output_multiplier); |
| } else |
| #endif |
| { |
| tflite::reference_ops::DepthwiseConv( |
| op_params, GetTensorShape(input), GetTensorData<uint8_t>(input), |
| GetTensorShape(filter), GetTensorData<uint8_t>(filter), |
| GetTensorShape(bias), GetTensorData<int32_t>(bias), |
| GetTensorShape(output), GetTensorData<uint8_t>(output)); |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| auto* params = |
| reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data); |
| |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| const TfLiteTensor* input = GetInput(context, node, kInputTensor); |
| const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); |
| const TfLiteTensor* bias = |
| (NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr; |
| |
| const TfLiteType data_type = input->type; |
| int width = SizeOfDimension(input, 2); |
| int height = SizeOfDimension(input, 1); |
| int filter_width = SizeOfDimension(filter, 2); |
| int filter_height = SizeOfDimension(filter, 1); |
| |
| OpData data; |
| |
| if (input->type != kTfLiteFloat32) { |
| TF_LITE_ENSURE_EQ(context, filter->quantization.type, |
| kTfLiteAffineQuantization); |
| |
| const auto* affine_quantization = |
| reinterpret_cast<TfLiteAffineQuantization*>( |
| filter->quantization.params); |
| TF_LITE_ENSURE(context, affine_quantization); |
| TF_LITE_ENSURE(context, affine_quantization->scale); |
| } |
| |
| TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height, |
| filter_width, filter_height, data_type, |
| &data)); |
| |
| // TODO(aselle): Consider whether float conv and quantized conv should be |
| // separate ops to avoid dispatch overhead here. |
| switch (input->type) { // Already know in/out types are same. |
| case kTfLiteFloat32: |
| return EvalFloat(context, node, params, &data, input, filter, bias, |
| output); |
| break; |
| case kTfLiteInt8: |
| return EvalQuantizedPerChannel(context, node, params, &data, input, |
| filter, bias, output); |
| break; |
| case kTfLiteUInt8: |
| return EvalQuantized(context, node, params, &data, input, filter, bias, |
| output); |
| break; |
| default: |
| context->ReportError(context, "Type %s (%d) not supported.", |
| TfLiteTypeGetName(input->type), input->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace depthwise_conv |
| |
| TfLiteRegistration* Register_DEPTHWISE_CONV_2D() { |
| static TfLiteRegistration r = {depthwise_conv::Init, depthwise_conv::Free, |
| depthwise_conv::Prepare, depthwise_conv::Eval}; |
| return &r; |
| } |
| |
| } // namespace micro |
| } // namespace ops |
| } // namespace tflite |