mirror of https://github.com/RE-OWOD/RE-OWOD
77 lines
2.6 KiB
C++
77 lines
2.6 KiB
C++
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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#include <opencv2/opencv.hpp>
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#include <iostream>
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#include <string>
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#include <c10/cuda/CUDAStream.h>
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#include <torch/csrc/autograd/grad_mode.h>
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#include <torch/script.h>
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using namespace std;
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// experimental. don't use
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int main(int argc, const char* argv[]) {
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if (argc != 3) {
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return 1;
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}
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std::string image_file = argv[2];
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torch::autograd::AutoGradMode guard(false);
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auto module = torch::jit::load(argv[1]);
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assert(module.buffers().size() > 0);
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// Assume that the entire model is on the same device.
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// We just put input to this device.
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auto device = (*begin(module.buffers())).device();
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cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR);
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const int height = input_img.rows;
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const int width = input_img.cols;
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// FPN models require divisibility of 32
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assert(height % 32 == 0 && width % 32 == 0);
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const int channels = 3;
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auto input = torch::from_blob(
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input_img.data, {1, height, width, channels}, torch::kUInt8);
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// NHWC to NCHW
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input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous();
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std::array<float, 3> im_info_data{height * 1.0f, width * 1.0f, 1.0f};
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auto im_info = torch::from_blob(im_info_data.data(), {1, 3}).to(device);
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// run the network
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auto output = module.forward({std::make_tuple(input, im_info)});
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if (device.is_cuda())
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c10::cuda::getCurrentCUDAStream().synchronize();
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// run 3 more times to benchmark
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int N_benchmark = 3;
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auto start_time = chrono::high_resolution_clock::now();
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for (int i = 0; i < N_benchmark; ++i) {
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output = module.forward({std::make_tuple(input, im_info)});
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if (device.is_cuda())
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c10::cuda::getCurrentCUDAStream().synchronize();
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}
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auto end_time = chrono::high_resolution_clock::now();
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auto ms = chrono::duration_cast<chrono::microseconds>(end_time - start_time)
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.count();
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cout << "Latency (should vary with different inputs): "
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<< ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl;
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auto outputs = output.toTuple()->elements();
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// parse Mask R-CNN outputs
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auto bbox = outputs[0].toTensor(), scores = outputs[1].toTensor(),
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labels = outputs[2].toTensor(), mask_probs = outputs[3].toTensor();
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cout << "bbox: " << bbox.toString() << " " << bbox.sizes() << endl;
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cout << "scores: " << scores.toString() << " " << scores.sizes() << endl;
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cout << "labels: " << labels.toString() << " " << labels.sizes() << endl;
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cout << "mask_probs: " << mask_probs.toString() << " " << mask_probs.sizes()
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<< endl;
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int num_instances = bbox.sizes()[0];
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cout << bbox << endl;
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return 0;
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}
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