tflite_micro_person_detection_init

This commit is contained in:
yangqingsheng
2020-12-08 17:16:20 +08:00
parent 55168d954d
commit 200c0ff460
310 changed files with 121982 additions and 208 deletions

View File

@@ -0,0 +1,127 @@
/* Copyright 2019 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/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
namespace tflite {
namespace ops {
namespace micro {
namespace pack {
namespace {
constexpr int kOutputTensor = 0;
template <typename T>
TfLiteStatus PackImpl(TfLiteContext* context, TfLiteNode* node,
TfLiteEvalTensor* output, int values_count, int axis) {
const TfLiteEvalTensor* input0 =
tflite::micro::GetEvalInput(context, node, 0);
const int dimensions = output->dims->size;
const TfLiteIntArray* input_dims = input0->dims;
const TfLiteIntArray* output_dims = output->dims;
if (axis < 0) {
axis += dimensions;
}
int outer_size = 1;
for (int i = 0; i < axis; ++i) {
outer_size *= output_dims->data[i];
}
int copy_size = 1;
for (int i = axis + 1; i < dimensions; ++i) {
copy_size *= output_dims->data[i];
}
int input_size = 1;
for (int i = 0; i < input_dims->size; ++i) {
input_size *= input_dims->data[i];
}
TFLITE_DCHECK_EQ(input_size, copy_size * outer_size);
T* output_data = tflite::micro::GetTensorData<T>(output);
for (int i = 0; i < values_count; ++i) {
const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
const T* input_data = tflite::micro::GetTensorData<T>(t);
for (int k = 0; k < outer_size; ++k) {
const T* input_ptr = input_data + copy_size * k;
int loc = k * values_count * copy_size + i * copy_size;
T* output_ptr = output_data + loc;
for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
}
}
return kTfLiteOk;
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLitePackParams* data =
reinterpret_cast<TfLitePackParams*>(node->builtin_data);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
switch (output->type) {
case kTfLiteFloat32: {
return PackImpl<float>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteUInt8: {
return PackImpl<uint8_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt8: {
return PackImpl<int8_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt32: {
return PackImpl<int32_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt64: {
return PackImpl<int64_t>(context, node, output, data->values_count,
data->axis);
}
default: {
TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by pack.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
}
return kTfLiteOk;
}
} // namespace
} // namespace pack
TfLiteRegistration Register_PACK() {
return {/*init=*/nullptr,
/*free=*/nullptr,
/*prepare=*/nullptr,
/*invoke=*/pack::Eval,
/*profiling_string=*/nullptr,
/*builtin_code=*/0,
/*custom_name=*/nullptr,
/*version=*/0};
}
} // namespace micro
} // namespace ops
} // namespace tflite