Implementation of transpose_conv

PiperOrigin-RevId: 196806646

1 files changed, 146 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/transpose_conv.cc b/tensorflow/contrib/lite/kernels/transpose_conv.cc
new file mode 100644
index 0000000000..3c99661029
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/transpose_conv.cc
@@ -0,0 +1,146 @@
+/* Copyright 2018 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 <unistd.h>
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+#include <limits>
+
+#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/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+#include "tensorflow/contrib/lite/kernels/padding.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace transpose_conv {
+
+constexpr int kOutputShapeTensor = 0;
+constexpr int kWeightsTensor = 1;
+constexpr int kDataInputTensor = 2;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus ResizeOutputShape(TfLiteContext* context,
+                               const TfLiteTensor* output_shape,
+                               TfLiteTensor* output) {
+  // Currently only support int32 for output shape.
+  if (output_shape->type != kTfLiteInt32) {
+    context->ReportError(context, "Output shape is %d, not int32.",
+                         output_shape->type);
+    return kTfLiteError;
+  }
+  const int output_dimensions = NumElements(output_shape);
+  TfLiteIntArray* output_shape_array = TfLiteIntArrayCreate(output_dimensions);
+  for (int i = 0; i < output_dimensions; ++i) {
+    output_shape_array->data[i] = GetTensorData<int32_t>(output_shape)[i];
+  }
+
+  return context->ResizeTensor(context, output, output_shape_array);
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  const TfLiteTensor* output_shape =
+      GetInput(context, node, kOutputShapeTensor);
+  const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* input = GetInput(context, node, kDataInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  TF_LITE_ENSURE_EQ(context, NumDimensions(output_shape), 1);
+  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
+  TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 4);
+
+  // Currenlty only supports float32.
+  const TfLiteType data_type = input->type;
+  TF_LITE_ENSURE(context, data_type == kTfLiteFloat32);
+  TF_LITE_ENSURE_EQ(context, output->type, data_type);
+  TF_LITE_ENSURE_EQ(context, weights->type, data_type);
+
+  // Ensure that weights and inputs have the same channel dimension.
+  // Note: TOCO will reorder weights in the following format: OHWI.
+  TF_LITE_ENSURE_EQ(context, SizeOfDimension(input, 3),
+                    SizeOfDimension(weights, 0));
+
+  if (!IsConstantTensor(output_shape)) {
+    SetTensorToDynamic(output);
+    return kTfLiteOk;
+  }
+  return ResizeOutputShape(context, output_shape, output);
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  const TfLiteTensor* output_shape =
+      GetInput(context, node, kOutputShapeTensor);
+  const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* input = GetInput(context, node, kDataInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  const auto* params =
+      reinterpret_cast<TfLiteTransposeConvParams*>(node->builtin_data);
+
+  if (IsDynamicTensor(output)) {
+    TF_LITE_ENSURE_OK(context,
+                      ResizeOutputShape(context, output_shape, output));
+  }
+
+  // Get height and width of the output image.
+  const int width = SizeOfDimension(output, 2);
+  const int height = SizeOfDimension(output, 1);
+  const int filter_width = SizeOfDimension(weights, 1);
+  const int filter_height = SizeOfDimension(weights, 2);
+
+  const int stride_width = params->stride_width;
+  const int stride_height = params->stride_height;
+
+  const TfLitePaddingValues& padding_size =
+      ComputePaddingHeightWidth(stride_height, stride_width, 1, height, width,
+                                filter_height, filter_width, params->padding);
+
+  // Currently only support float32.
+  switch (input->type) {
+    case kTfLiteFloat32:
+      optimized_ops::TransposeConv(
+          GetTensorData<float>(input), GetTensorDims(input),
+          GetTensorData<float>(weights), GetTensorDims(weights), stride_width,
+          stride_height, padding_size.width, padding_size.height,
+          GetTensorData<float>(output), GetTensorDims(output));
+      break;
+    default:
+      context->ReportError(context, "Type %d, not currently supported.",
+                           input->type);
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
+}  // namespace transpose_conv
+
+TfLiteRegistration* Register_TRANSPOSE_CONV() {
+  static TfLiteRegistration r = {nullptr, nullptr, transpose_conv::Prepare,
+                                 transpose_conv::Eval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite