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// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#include <opencv2/opencv.hpp>
#include <iostream>
#include <string>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/script.h>
using namespace std;
// experimental. don't use
int main(int argc, const char* argv[]) {
if (argc != 3) {
return 1;
}
std::string image_file = argv[2];
torch::autograd::AutoGradMode guard(false);
auto module = torch::jit::load(argv[1]);
assert(module.buffers().size() > 0);
// Assume that the entire model is on the same device.
// We just put input to this device.
auto device = (*begin(module.buffers())).device();
cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR);
const int height = input_img.rows;
const int width = input_img.cols;
// FPN models require divisibility of 32
assert(height % 32 == 0 && width % 32 == 0);
const int channels = 3;
auto input = torch::from_blob(
input_img.data, {1, height, width, channels}, torch::kUInt8);
// NHWC to NCHW
input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous();
std::array<float, 3> im_info_data{height * 1.0f, width * 1.0f, 1.0f};
auto im_info = torch::from_blob(im_info_data.data(), {1, 3}).to(device);
// run the network
auto output = module.forward({std::make_tuple(input, im_info)});
// run 3 more times to benchmark
int N_benchmark = 3;
auto start_time = chrono::high_resolution_clock::now();
for (int i = 0; i < N_benchmark; ++i) {
output = module.forward({std::make_tuple(input, im_info)});
}
auto end_time = chrono::high_resolution_clock::now();
auto ms = chrono::duration_cast<chrono::microseconds>(end_time - start_time)
.count();
cout << "Latency (should vary with different inputs): "
<< ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl;
auto outputs = output.toTuple()->elements();
// parse Mask R-CNN outputs
auto bbox = outputs[0].toTensor(), scores = outputs[1].toTensor(),
labels = outputs[2].toTensor(), mask_probs = outputs[3].toTensor();
cout << "bbox: " << bbox.toString() << " " << bbox.sizes() << endl;
cout << "scores: " << scores.toString() << " " << scores.sizes() << endl;
cout << "labels: " << labels.toString() << " " << labels.sizes() << endl;
cout << "mask_probs: " << mask_probs.toString() << " " << mask_probs.sizes()
<< endl;
int num_instances = bbox.sizes()[0];
cout << bbox << endl;
return 0;
}
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