| /* 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 "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/c_api_internal.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/op_macros.h" |
| |
| namespace tflite { |
| namespace ops { |
| namespace micro { |
| namespace reshape { |
| |
| constexpr int kInputTensor = 0; |
| constexpr int kShapeTensor = 1; |
| constexpr int kOutputTensor = 0; |
| |
| TfLiteStatus ReshapeOutput(TfLiteContext* context, TfLiteNode* node) { |
| const TfLiteTensor* input = GetInput(context, node, kInputTensor); |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| // Tensorflow's Reshape allows one of the shape components to have the |
| // special -1 value, meaning it will be calculated automatically based on the |
| // input. Here we calculate what that dimension should be so that the number |
| // of output elements in the same as the number of input elements. |
| int num_input_elements = NumElements(input); |
| TfLiteIntArray* output_shape = output->dims; |
| |
| if (NumInputs(node) == 1 && // Legacy scalar supported with params. |
| output_shape->size == 1 && output_shape->data[0] == 0) { |
| // Legacy tflite models use a shape parameter of [0] to indicate scalars, |
| // so adjust accordingly. TODO(b/111614235): Allow zero-sized buffers during |
| // toco conversion. |
| output_shape->size = 0; |
| } |
| |
| int num_output_elements = 1; |
| int stretch_dim = -1; |
| for (int i = 0; i < output_shape->size; ++i) { |
| int value = output_shape->data[i]; |
| if (value == -1) { |
| TF_LITE_ENSURE_EQ(context, stretch_dim, -1); |
| stretch_dim = i; |
| } else { |
| num_output_elements *= value; |
| } |
| } |
| if (stretch_dim != -1) { |
| output_shape->data[stretch_dim] = num_input_elements / num_output_elements; |
| num_output_elements *= output_shape->data[stretch_dim]; |
| } |
| |
| TF_LITE_ENSURE_EQ(context, input->type, output->type); |
| TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements); |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); |
| TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| const TfLiteTensor* input = GetInput(context, node, kInputTensor); |
| TfLiteTensor* output = GetOutput(context, node, kOutputTensor); |
| if (ReshapeOutput(context, node) != kTfLiteOk) { |
| return kTfLiteError; |
| } |
| |
| for (int i = 0; i < input->bytes; ++i) { |
| output->data.raw[i] = input->data.raw[i]; |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace reshape |
| |
| TfLiteRegistration* Register_RESHAPE() { |
| static TfLiteRegistration r = {nullptr, nullptr, reshape::Prepare, |
| reshape::Eval}; |
| return &r; |
| } |
| |
| } // namespace micro |
| } // namespace ops |
| } // namespace tflite |