diff options
author | Yu-Cheng Ling <ycling@google.com> | 2017-12-14 21:26:55 -0800 |
---|---|---|
committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-12-14 21:31:06 -0800 |
commit | dbcb1ffcca6a3c52e3c109a1739018350bc41925 (patch) | |
tree | aa4f440d1f8f4382cea7a1b4e4cfc623da2a76f0 /tensorflow/contrib/lite/kernels/batch_to_space_nd.cc | |
parent | f806269602219d5095265d036f294cc9a6260971 (diff) |
Support BatchToSpaceND in TFLite
The internal implementation only support 4D tensors for now.
The dimension has to be 1 batch + 2 spatial + 1 other.
The most common format within this restriction is NHWC.
Cropping is not supported by the internal implementation.
PiperOrigin-RevId: 179143332
Diffstat (limited to 'tensorflow/contrib/lite/kernels/batch_to_space_nd.cc')
-rw-r--r-- | tensorflow/contrib/lite/kernels/batch_to_space_nd.cc | 161 |
1 files changed, 161 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc new file mode 100644 index 0000000000..0eed680fdc --- /dev/null +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc @@ -0,0 +1,161 @@ +/* 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. +==============================================================================*/ +#include <string.h> +#include <vector> +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace batch_to_space_nd { + +// This file has two implementations of BatchToSpaceND. +enum KernelType { + kReference, + kGenericOptimized, +}; + +struct BatchToSpaceNDContext { + BatchToSpaceNDContext(TfLiteContext* context, TfLiteNode* node) { + params = reinterpret_cast<TfLiteBatchToSpaceNDParams*>(node->builtin_data); + input = GetInput(context, node, 0); + output = GetOutput(context, node, 0); + } + TfLiteBatchToSpaceNDParams* params; + TfLiteTensor* input; + TfLiteTensor* output; +}; + +// Currently, only 4D NHWC input/output op_context are supported. +// The 4D array need to have exactly 2 spatial dimensions. +// TODO(ycling): Support arbitrary dimension in BatchToSpaceND. +const int kInputDimensionNum = 4; +const int kOutputDimensionNum = 4; +const int kSpatialDimensionNum = 2; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + // The 2nd tensor (block_shape) and the 3rd tensor (crops) are ignored now. + TF_LITE_ENSURE(context, NumInputs(node) >= 1 && NumInputs(node) <= 3); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + BatchToSpaceNDContext op_context(context, node); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.input), + kInputDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context.params->num_spatial_dimensions, + kSpatialDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); + + const TfLiteIntArray* input_size = op_context.input->dims; + const int* block_shape = op_context.params->block_shape; + + // Number of batch must be multiple of (block_shape[0] * block_shape[1]). + TF_LITE_ENSURE_EQ(context, + input_size->data[0] % (block_shape[0] * block_shape[1]), 0); + + const int output_batch_size = + input_size->data[0] / (block_shape[0] * block_shape[1]); + const int output_height = input_size->data[1] * block_shape[0]; + const int output_width = input_size->data[2] * block_shape[1]; + const int output_channel_size = input_size->data[3]; + + TfLiteIntArray* output_size = TfLiteIntArrayCreate(kOutputDimensionNum); + output_size->data[0] = output_batch_size; + output_size->data[1] = output_height; + output_size->data[2] = output_width; + output_size->data[3] = output_channel_size; + + return context->ResizeTensor(context, op_context.output, output_size); +} + +template <KernelType kernel_type> +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + BatchToSpaceNDContext op_context(context, node); + + int block_shape_dims_array[1] = {kSpatialDimensionNum}; + Dims<4> block_shape_dims = GetTensorDims(block_shape_dims_array, 1); + +#define TF_LITE_BATCH_TO_SPACE_ND(type, scalar) \ + type::BatchToSpaceND(GetTensorData<scalar>(op_context.input), \ + GetTensorDims(op_context.input), \ + op_context.params->block_shape, block_shape_dims, \ + GetTensorData<scalar>(op_context.output), \ + GetTensorDims(op_context.output)) + switch (op_context.input->type) { // Already know in/out types are same. + case kTfLiteFloat32: + if (kernel_type == kReference) { + TF_LITE_BATCH_TO_SPACE_ND(reference_ops, float); + } else { + TF_LITE_BATCH_TO_SPACE_ND(optimized_ops, float); + } + break; + case kTfLiteUInt8: + if (kernel_type == kReference) { + TF_LITE_BATCH_TO_SPACE_ND(reference_ops, uint8_t); + } else { + TF_LITE_BATCH_TO_SPACE_ND(optimized_ops, uint8_t); + } + break; + case kTfLiteInt32: + if (kernel_type == kReference) { + TF_LITE_BATCH_TO_SPACE_ND(reference_ops, int32_t); + } else { + TF_LITE_BATCH_TO_SPACE_ND(optimized_ops, int32_t); + } + break; + case kTfLiteInt64: + if (kernel_type == kReference) { + TF_LITE_BATCH_TO_SPACE_ND(reference_ops, int64_t); + } else { + TF_LITE_BATCH_TO_SPACE_ND(optimized_ops, int64_t); + } + break; + default: + context->ReportError(context, + "Type is currently not supported by BatchToSpace."); + return kTfLiteError; + } +#undef TF_LITE_BATCH_TO_SPACE_ND + return kTfLiteOk; +} + +} // namespace batch_to_space_nd + +TfLiteRegistration* Register_BATCH_TO_SPACE_ND_REF() { + static TfLiteRegistration r = { + nullptr, nullptr, batch_to_space_nd::Prepare, + batch_to_space_nd::Eval<batch_to_space_nd::kReference>}; + return &r; +} + +TfLiteRegistration* Register_BATCH_TO_SPACE_ND_GENERIC_OPT() { + static TfLiteRegistration r = { + nullptr, nullptr, batch_to_space_nd::Prepare, + batch_to_space_nd::Eval<batch_to_space_nd::kGenericOptimized>}; + return &r; +} + +TfLiteRegistration* Register_BATCH_TO_SPACE_ND() { + return Register_BATCH_TO_SPACE_ND_GENERIC_OPT(); +} + +} // namespace builtin +} // namespace ops +} // namespace tflite |