Add support for the "DEQUANTIZE" op. This cover only ops that are generated by TOCO

in order to handle UINT8 input to floating-point models.

PiperOrigin-RevId: 188182372

1 files changed, 77 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/dequantize.cc b/tensorflow/contrib/lite/kernels/dequantize.cc
new file mode 100644
index 0000000000..e685f2465f
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/dequantize.cc
@@ -0,0 +1,77 @@
+/* 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 <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/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 dequantize {
+
+struct OpContext {
+  OpContext(TfLiteContext* context, TfLiteNode* node) {
+    input = GetInput(context, node, 0);
+    output = GetOutput(context, node, 0);
+  }
+  TfLiteTensor* input;
+  TfLiteTensor* output;
+};
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  OpContext op_context(context, node);
+
+  TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8);
+
+  op_context.output->type = kTfLiteFloat32;
+  return context->ResizeTensor(context, op_context.output,
+                               TfLiteIntArrayCopy(op_context.input->dims));
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  OpContext op_context(context, node);
+
+  auto zero_point = op_context.input->params.zero_point;
+  auto scale = op_context.input->params.scale;
+
+  optimized_ops::Dequantize(GetTensorData<uint8_t>(op_context.input),
+                            GetTensorDims(op_context.input), zero_point, scale,
+                            GetTensorData<float>(op_context.output),
+                            GetTensorDims(op_context.output));
+  return kTfLiteOk;
+}
+
+}  // namespace dequantize
+
+TfLiteRegistration* Register_DEQUANTIZE_OPT() {
+  static TfLiteRegistration r = {nullptr, nullptr, dequantize::Prepare,
+                                 dequantize::Eval};
+  return &r;
+}
+
+TfLiteRegistration* Register_DEQUANTIZE() { return Register_DEQUANTIZE_OPT(); }
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite