mirror of
https://github.com/vee1e/tflite-micro.git
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d25b267865
- Save ~4KB of RAM by implementing a lightweight version of the vector, which only accesses integer values, without implicit conversion. - Change Micro kernels to access flexbuffers as vectors instead of maps, which is recommended by the flexbuffers doc for efficiency. Since the values in the vector are ordered alphabetically by their keys, the kernels can access them by index instead. - Revert detection_postprocess to the native flexbuffers API. The LiteVector API doesn't support IsNull() and I prefer not to support two custom flexbuffer APIs. Co-authored-by: Nat Jeffries <natmjeffries@gmail.com>
81 lines
2.9 KiB
C++
81 lines
2.9 KiB
C++
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/micro/flatbuffer_utils.h"
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#include <string>
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#include "flatbuffers/flexbuffers.h"
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#include "tensorflow/lite/micro/test_helpers.h"
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#include "tensorflow/lite/micro/testing/micro_test.h"
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TF_LITE_MICRO_TESTS_BEGIN
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TF_LITE_MICRO_TEST(TestFlexbufferWrapper) {
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struct TestParam {
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std::string name;
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std::string type;
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std::string value;
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};
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TestParam params[] = {
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{"xyz", "Int", "613"},
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{"Neuron", "Double", "13.22"},
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{"angle", "Int", "300"},
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{"llama", "Bool", "false"},
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{"Curl", "Float", "0.232"},
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{"aardvark", "Bool", "true"},
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{"ghost", "Double", "0.0000000001"},
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{"123stigma", "Bool", "true"},
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};
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// Index of elements sorted alphabetically by name
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int params_sorted[] = {7, 4, 1, 5, 2, 6, 3, 0};
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const int param_num = sizeof(params) / sizeof(params[0]);
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flexbuffers::Builder fbb;
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fbb.Map([&]() {
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for (int i = 0; i < param_num; i++) {
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const std::string& param_value = params[i].value;
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if (params[i].type == "Int") {
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fbb.Int(params[i].name.c_str(), std::stoi(param_value));
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} else if (params[i].type == "Bool") {
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fbb.Bool(params[i].name.c_str(), param_value == "true");
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} else if (params[i].type == "Double") {
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fbb.Double(params[i].name.c_str(), std::stod(param_value));
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} else if (params[i].type == "Float") {
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fbb.Float(params[i].name.c_str(), std::stof(param_value));
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}
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}
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});
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fbb.Finish();
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const std::vector<uint8_t> buffer = fbb.GetBuffer();
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tflite::FlexbufferWrapper wrapper(buffer.data(), buffer.size());
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for (int i = 0; i < param_num; i++) {
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std::string& param_value = params[params_sorted[i]].value;
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if (params[params_sorted[i]].type == "Int") {
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TF_LITE_MICRO_EXPECT(wrapper.ElementAsInt32(i) == std::stoi(param_value));
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} else if (params[params_sorted[i]].type == "Bool") {
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TF_LITE_MICRO_EXPECT(wrapper.ElementAsBool(i) == (param_value == "true"));
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} else if (params[params_sorted[i]].type == "Double") {
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TF_LITE_MICRO_EXPECT(wrapper.ElementAsDouble(i) ==
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std::stod(param_value));
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} else if (params[params_sorted[i]].type == "Float") {
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TF_LITE_MICRO_EXPECT(wrapper.ElementAsFloat(i) == std::stof(param_value));
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}
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}
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}
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TF_LITE_MICRO_TESTS_END
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