| /* Copyright 2020 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 "tensorflow/lite/kernels/internal/reference/transpose_conv.h" |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/transpose_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/micro/kernels/kernel_util.h" |
| |
| namespace tflite { |
| namespace { |
| |
| // For the TfLite transpose_conv implementation, input tensor 0 corresponds to |
| // the OutputShapeTensor. However, since TFLM does not support dynamic tensors, |
| // the TFLM implementation ignores input tensor 0 and the only inputs we care |
| // about are kFilterTensor, kInputTensor and kBiasTensor. |
| constexpr int kFilterTensor = 1; |
| constexpr int kInputTensor = 2; |
| constexpr int kBiasTensor = 3; |
| constexpr int kOutputTensor = 0; |
| |
| // Conv is quantized along dimension 0: |
| // https://www.tensorflow.org/lite/performance/quantization_spec |
| constexpr int kConvQuantizedDimension = 0; |
| |
| struct OpData { |
| ConvParams params; |
| |
| // A scratch buffer is required for quantized implementations. |
| int scratch_buffer_index; |
| |
| // Multiplier and shift arrays are required for the int8 implementation. |
| int32_t* per_channel_output_multiplier; |
| int32_t* per_channel_output_shift; |
| }; |
| |
| inline PaddingType RuntimePaddingType(TfLitePadding padding) { |
| switch (padding) { |
| case TfLitePadding::kTfLitePaddingSame: |
| return PaddingType::kSame; |
| case TfLitePadding::kTfLitePaddingValid: |
| return PaddingType::kValid; |
| case TfLitePadding::kTfLitePaddingUnknown: |
| default: |
| return PaddingType::kNone; |
| } |
| } |
| |
| TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, |
| const TfLiteConvParams* params, int width, |
| int height, int filter_width, int filter_height, |
| int out_width, int out_height, |
| const TfLiteType data_type, OpData* data) { |
| bool has_bias = node->inputs->size == 4; |
| // Check number of inputs/outputs |
| TF_LITE_ENSURE(context, has_bias || node->inputs->size == 3); |
| TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); |
| |
| // Matching GetWindowedOutputSize in TensorFlow. |
| auto padding = params->padding; |
| TfLitePaddingValues padding_values = ComputePaddingHeightWidth( |
| params->stride_height, params->stride_width, |
| params->dilation_height_factor, params->dilation_width_factor, height, |
| width, filter_height, filter_width, padding, &out_height, &out_width); |
| |
| data->params.padding_type = RuntimePaddingType(padding); |
| data->params.padding_values.width = padding_values.width; |
| data->params.padding_values.height = padding_values.height; |
| |
| // 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); |
| TF_LITE_ENSURE(context, input != nullptr); |
| const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); |
| TF_LITE_ENSURE(context, filter != nullptr); |
| const TfLiteTensor* bias = |
| GetOptionalInputTensor(context, node, kBiasTensor); |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| TF_LITE_ENSURE(context, output != nullptr); |
| int output_channels = filter->dims->data[kConvQuantizedDimension]; |
| |
| TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams( |
| context, input, filter, bias, output, params->activation, |
| &data->params.output_multiplier, &data->params.output_shift, |
| &data->params.quantized_activation_min, |
| &data->params.quantized_activation_max, |
| data->per_channel_output_multiplier, data->per_channel_output_shift, |
| output_channels)); |
| } |
| return kTfLiteOk; |
| } |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); |
| return context->AllocatePersistentBuffer(context, sizeof(OpData)); |
| } |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| TFLITE_DCHECK(node->builtin_data != nullptr); |
| |
| OpData* data = static_cast<OpData*>(node->user_data); |
| const auto params = static_cast<const TfLiteConvParams*>(node->builtin_data); |
| |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| TF_LITE_ENSURE(context, output != nullptr); |
| const TfLiteTensor* input = GetInput(context, node, kInputTensor); |
| TF_LITE_ENSURE(context, input != nullptr); |
| const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); |
| TF_LITE_ENSURE(context, filter != nullptr); |
| |
| int input_width = input->dims->data[2]; |
| int input_height = input->dims->data[1]; |
| int filter_width = filter->dims->data[2]; |
| int filter_height = filter->dims->data[1]; |
| int output_width = output->dims->data[2]; |
| int output_height = output->dims->data[1]; |
| |
| // Dynamically allocate per-channel quantization parameters. |
| const int num_channels = filter->dims->data[kConvQuantizedDimension]; |
| data->per_channel_output_multiplier = |
| static_cast<int32_t*>(context->AllocatePersistentBuffer( |
| context, num_channels * sizeof(int32_t))); |
| data->per_channel_output_shift = |
| static_cast<int32_t*>(context->AllocatePersistentBuffer( |
| context, num_channels * sizeof(int32_t))); |
| |
| // Quantized kernels use an int32 scratch buffer. |
| if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { |
| TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr); |
| TFLITE_DCHECK(context->RequestScratchBufferInArena( |
| context, |
| GetTensorShape(output).FlatSize() * sizeof(int32_t), |
| &(data->scratch_buffer_index)) == kTfLiteOk); |
| } |
| |
| // All per-channel quantized tensors need valid zero point and scale arrays. |
| if (input->type == kTfLiteInt8) { |
| TF_LITE_ENSURE_EQ(context, filter->quantization.type, |
| kTfLiteAffineQuantization); |
| |
| const auto* affine_quantization = |
| static_cast<TfLiteAffineQuantization*>(filter->quantization.params); |
| TF_LITE_ENSURE(context, affine_quantization); |
| TF_LITE_ENSURE(context, affine_quantization->scale); |
| TF_LITE_ENSURE(context, affine_quantization->zero_point); |
| |
| TF_LITE_ENSURE(context, |
| affine_quantization->scale->size == 1 || |
| affine_quantization->scale->size == |
| filter->dims->data[kConvQuantizedDimension]); |
| TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size, |
| affine_quantization->zero_point->size); |
| } |
| |
| TF_LITE_ENSURE_STATUS(CalculateOpData( |
| context, node, params, input_width, input_height, filter_width, |
| filter_height, output_width, output_height, input->type, data)); |
| |
| // Offsets (zero points) |
| data->params.input_offset = -input->params.zero_point; |
| data->params.weights_offset = -filter->params.zero_point; |
| data->params.output_offset = output->params.zero_point; |
| |
| // Stride + dilation |
| data->params.stride_width = params->stride_width; |
| data->params.stride_height = params->stride_height; |
| data->params.dilation_width_factor = params->dilation_width_factor; |
| data->params.dilation_height_factor = params->dilation_height_factor; |
| |
| float output_activation_min, output_activation_max; |
| CalculateActivationRange(params->activation, &output_activation_min, |
| &output_activation_max); |
| data->params.float_activation_min = output_activation_min; |
| data->params.float_activation_max = output_activation_max; |
| return kTfLiteOk; |
| } // namespace conv |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| const TfLiteEvalTensor* input = |
| tflite::micro::GetEvalInput(context, node, kInputTensor); |
| const TfLiteEvalTensor* filter = |
| tflite::micro::GetEvalInput(context, node, kFilterTensor); |
| const TfLiteEvalTensor* bias = |
| (NumInputs(node) == 4) |
| ? tflite::micro::GetEvalInput(context, node, kBiasTensor) |
| : nullptr; |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData& data = *(static_cast<const OpData*>(node->user_data)); |
| |
| TF_LITE_ENSURE_EQ(context, input->type, output->type); |
| TF_LITE_ENSURE_MSG(context, input->type == filter->type, |
| "Hybrid models are not supported on TFLite Micro."); |
| |
| switch (input->type) { // Already know in/out types are same. |
| case kTfLiteFloat32: { |
| reference_ops::TransposeConv( |
| data.params, tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<float>(input), |
| tflite::micro::GetTensorShape(filter), |
| tflite::micro::GetTensorData<float>(filter), |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetTensorData<float>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<float>(output), |
| tflite::micro::GetTensorShape(nullptr), nullptr); |
| break; |
| } |
| case kTfLiteInt8: { |
| int32_t* scratch_buffer = static_cast<int32_t*>( |
| context->GetScratchBuffer(context, data.scratch_buffer_index)); |
| reference_integer_ops::TransposeConv( |
| data.params, data.per_channel_output_multiplier, |
| data.per_channel_output_shift, tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int8_t>(input), |
| tflite::micro::GetTensorShape(filter), |
| tflite::micro::GetTensorData<int8_t>(filter), |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetTensorData<int32_t>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int8_t>(output), |
| tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer); |
| break; |
| } |
| default: |
| TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", |
| TfLiteTypeGetName(input->type), input->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TfLiteRegistration Register_TRANSPOSE_CONV() { |
| return {/*init=*/Init, |
| /*free=*/nullptr, |
| /*prepare=*/Prepare, |
| /*invoke=*/Eval, |
| /*profiling_string=*/nullptr, |
| /*builtin_code=*/0, |
| /*custom_name=*/nullptr, |
| /*version=*/0}; |
| } |
| |
| } // namespace tflite |