代码拉取完成,页面将自动刷新
// Licensed under the MIT License <http://opensource.org/licenses/MIT>.
// SPDX-License-Identifier: MIT
// Copyright (c) 2018 - 2020 Daniil Goncharov <neargye@gmail.com>.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
#include "tf_utils.hpp"
#include <scope_guard.hpp>
#include <iostream>
#include <vector>
#include <string>
void PrintOpInputs(TF_Graph*, TF_Operation* op) {
auto num_inputs = TF_OperationNumInputs(op);
std::cout << "Number inputs: " << num_inputs << std::endl;
for (auto i = 0; i < num_inputs; ++i) {
auto input = TF_Input{op, i};
auto type = TF_OperationInputType(input);
std::cout << std::to_string(i) << " type : " << tf_utils::DataTypeToString(type) << std::endl;
}
}
void PrintOpOutputs(TF_Graph* graph, TF_Operation* op, TF_Status* status) {
auto num_outputs = TF_OperationNumOutputs(op);
std::cout << "Number outputs: " << num_outputs << std::endl;
for (auto i = 0; i < num_outputs; ++i) {
auto output = TF_Output{op, i};
auto type = TF_OperationOutputType(output);
std::cout << std::to_string(i) << " type : " << tf_utils::DataTypeToString(type);
auto num_dims = TF_GraphGetTensorNumDims(graph, output, status);
if (TF_GetCode(status) != TF_OK) {
std::cout << "Can't get tensor dimensionality" << std::endl;
continue;
}
std::cout << " dims: " << num_dims;
if (num_dims <= 0) {
std::cout << " []" << std::endl;;
continue;
}
std::vector<std::int64_t> dims(num_dims);
TF_GraphGetTensorShape(graph, output, dims.data(), num_dims, status);
if (TF_GetCode(status) != TF_OK) {
std::cout << "Can't get get tensor shape" << std::endl;
continue;
}
std::cout << " [";
for (auto j = 0; j < num_dims; ++j) {
std::cout << dims[j];
if (j < num_dims - 1) {
std::cout << ",";
}
}
std::cout << "]" << std::endl;
}
}
void PrintOps(TF_Graph* graph, TF_Status* status) {
TF_Operation* op;
std::size_t pos = 0;
while ((op = TF_GraphNextOperation(graph, &pos)) != nullptr) {
auto name = TF_OperationName(op);
auto type = TF_OperationOpType(op);
auto device = TF_OperationDevice(op);
auto num_outputs = TF_OperationNumOutputs(op);
auto num_inputs = TF_OperationNumInputs(op);
std::cout << pos << ": " << name << " type: " << type << " device: " << device << " number inputs: " << num_inputs << " number outputs: " << num_outputs << std::endl;
PrintOpInputs(graph, op);
PrintOpOutputs(graph, op, status);
std::cout << std::endl;
}
}
int main() {
auto graph = tf_utils::LoadGraph("graph.pb");
SCOPE_EXIT{ tf_utils::DeleteGraph(graph); };
if (graph == nullptr) {
std::cout << "Can't load graph" << std::endl;
return 1;
}
auto status = TF_NewStatus();
SCOPE_EXIT{ TF_DeleteStatus(status); }; // Auto-delete on scope exit.
PrintOps(graph, status);
return 0;
}
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。