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Add Benchmark readme (#236)
* add ppcls benchmark * add ppcls benchmark * add ppcls benchmark * add ppcls benchmark * fixed txt path * resolve conflict * resolve conflict * deal with comments * Add enable_trt_fp16 option * Add OV backend for seg and det * fixed valid backends in ppdet * fixed valid backends in yolo * add copyright and rm Chinese Notes * add ppdet&ppseg&yolo benchmark * add cpu/gpu mem info * Add benchmark readme * fixed bug Co-authored-by: Jason <jiangjiajun@baidu.com>
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benchmark/README.md
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benchmark/README.md
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# FastDeploy Benchmarks
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在跑benchmark前,需确认以下两个步骤
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* 1. 软硬件环境满足要求,参考[FastDeploy环境要求](../docs/environment.md)
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* 2. FastDeploy Python whl包安装,参考[FastDeploy Python安装](../docs/quick_start)
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FastDeploy 目前支持多种推理后端,下面以 PaddleClas MobileNetV1 为例,跑出多后端在 CPU/GPU 对应 benchmark 数据
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```bash
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# 下载 MobileNetV1 模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz
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tar -xvf MobileNetV1_x0_25_infer
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# 下载图片
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# CPU
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# Paddle Inference
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --cpu_num_thread 8 --iter_num 2000 --backend paddle
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# ONNX Runtime
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --cpu_num_thread 8 --iter_num 2000 --backend ort
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# OpenVINO
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --cpu_num_thread 8 --iter_num 2000 --backend ov
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# GPU
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# Paddle Inference
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --iter_num 2000 --backend paddle
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# ONNX Runtime
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --iter_num 2000 --backend ort
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# TensorRT
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --iter_num 2000 --backend trt
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# TensorRT fp16
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python benchmark_ppcls.py --model MobileNetV1_x0_25_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --iter_num 2000 --backend trt --enable_trt_fp16 True
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```
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**具体参数说明**
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| 参数 | 作用 |
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| -------------------- | ------------------------------------------ |
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| --model | 模型路径 |
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| --image | 图片路径 |
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| --device | 选择 CPU 还是 GPU,默认 CPU |
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| --cpu_num_thread | CPU 线程数 |
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| --device_id | GPU 卡号 |
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| --iter_num | 跑 benchmark 的迭代次数 |
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| --backend | 指定后端类型,有ort, ov, trt, paddle四个选项 |
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| --enable_trt_fp16 | 当后端为trt时,是否开启fp16 |
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**最终txt结果**
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将当前目录的所有txt汇总并结构化,执行下列命令
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```bash
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# 汇总
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cat *.txt >> ./result_ppcls.txt
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# 结构化信息
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python convert_info.py --txt_path result_ppcls.txt --domain ppcls
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```
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得到 CPU 结果```struct_cpu_ppcls.txt```以及 GPU 结果```struct_gpu_ppcls.txt```如下所示
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```bash
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# struct_cpu_ppcls.txt
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model_name thread_nums ort_run ort_end2end cpu_rss_mb ov_run ov_end2end cpu_rss_mb paddle_run paddle_end2end cpu_rss_mb
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MobileNetV1_x0_25 8 1.18 3.27 270.43 0.87 1.98 272.26 3.13 5.29 899.57
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# struct_gpu_ppcls.txt
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model_name ort_run ort_end2end gpu_rss_mb paddle_run paddle_end2end gpu_rss_mb trt_run trt_end2end gpu_rss_mb trt_fp16_run trt_fp16_end2end gpu_rss_mb
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MobileNetV1_x0_25 1.25 3.24 677.06 2.00 3.77 945.06 0.67 2.66 851.06 0.53 2.46 839.06
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```
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**结果说明**
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* ```_run```后缀代表一次infer耗时,包括H2D以及D2H;```_end2end```后缀代表包含前后处理耗时
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* ```cpu_rss_mb```代表内存占用;```gpu_rss_mb```代表显存占用
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若有多个PaddleClas模型,在当前目录新建ppcls_model目录,将所有模型放入该目录即可,运行下列命令
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```bash
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sh run_benchmark_ppcls.sh
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```
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一键得到所有模型在 CPU 以及 GPU 的 benchmark 数据
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@@ -117,7 +117,9 @@ if __name__ == '__main__':
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gpu_id = args.device_id
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end2end_statis = list()
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cpu_mem, gpu_mem, gpu_util = 0, 0, 0
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_thread) + ".txt"
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@@ -139,23 +141,26 @@ if __name__ == '__main__':
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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gpu_util += get_current_gputil(gpu_id)
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gpu_util.append(get_current_gputil(gpu_id))
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cm, gm = get_current_memory_mb(gpu_id)
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cpu_mem += cm
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gpu_mem += gm
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cpu_mem.append(cm)
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gpu_mem.append(gm)
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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repeat_iter = args.iter_num - warmup_iter
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end2end_statis = end2end_statis[warmup_iter:]
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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cpu_mem_repeat = cpu_mem[warmup_iter:]
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gpu_mem_repeat = gpu_mem[warmup_iter:]
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gpu_util_repeat = gpu_util[warmup_iter:]
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dump_result = dict()
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis) * 1000
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dump_result["cpu_rss_mb"] = cpu_mem / repeat_iter
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dump_result["gpu_rss_mb"] = gpu_mem / repeat_iter
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dump_result["gpu_util"] = gpu_util / repeat_iter
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat)
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dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat)
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dump_result["gpu_util"] = np.mean(gpu_util_repeat)
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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gpu_id = args.device_id
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end2end_statis = list()
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cpu_mem, gpu_mem, gpu_util = 0, 0, 0
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_thread) + ".txt"
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@@ -159,23 +161,26 @@ if __name__ == '__main__':
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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gpu_util += get_current_gputil(gpu_id)
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gpu_util.append(get_current_gputil(gpu_id))
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cm, gm = get_current_memory_mb(gpu_id)
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cpu_mem += cm
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gpu_mem += gm
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cpu_mem.append(cm)
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gpu_mem.append(gm)
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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repeat_iter = args.iter_num - warmup_iter
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end2end_statis = end2end_statis[warmup_iter:]
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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cpu_mem_repeat = cpu_mem[warmup_iter:]
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gpu_mem_repeat = gpu_mem[warmup_iter:]
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gpu_util_repeat = gpu_util[warmup_iter:]
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dump_result = dict()
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis) * 1000
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dump_result["cpu_rss_mb"] = cpu_mem / repeat_iter
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dump_result["gpu_rss_mb"] = gpu_mem / repeat_iter
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dump_result["gpu_util"] = gpu_util / repeat_iter
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat)
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dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat)
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dump_result["gpu_util"] = np.mean(gpu_util_repeat)
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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@@ -117,7 +117,9 @@ if __name__ == '__main__':
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gpu_id = args.device_id
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end2end_statis = list()
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cpu_mem, gpu_mem, gpu_util = 0, 0, 0
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_thread) + ".txt"
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@@ -138,23 +140,26 @@ if __name__ == '__main__':
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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gpu_util += get_current_gputil(gpu_id)
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gpu_util.append(get_current_gputil(gpu_id))
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cm, gm = get_current_memory_mb(gpu_id)
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cpu_mem += cm
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gpu_mem += gm
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cpu_mem.append(cm)
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gpu_mem.append(gm)
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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repeat_iter = args.iter_num - warmup_iter
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end2end_statis = end2end_statis[warmup_iter:]
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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cpu_mem_repeat = cpu_mem[warmup_iter:]
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gpu_mem_repeat = gpu_mem[warmup_iter:]
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gpu_util_repeat = gpu_util[warmup_iter:]
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dump_result = dict()
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis) * 1000
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dump_result["cpu_rss_mb"] = cpu_mem / repeat_iter
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dump_result["gpu_rss_mb"] = gpu_mem / repeat_iter
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dump_result["gpu_util"] = gpu_util / repeat_iter
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat)
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dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat)
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dump_result["gpu_util"] = np.mean(gpu_util_repeat)
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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@@ -117,7 +117,9 @@ if __name__ == '__main__':
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gpu_id = args.device_id
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end2end_statis = list()
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cpu_mem, gpu_mem, gpu_util = 0, 0, 0
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_thread) + ".txt"
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@@ -152,23 +154,26 @@ if __name__ == '__main__':
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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gpu_util += get_current_gputil(gpu_id)
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gpu_util.append(get_current_gputil(gpu_id))
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cm, gm = get_current_memory_mb(gpu_id)
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cpu_mem += cm
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gpu_mem += gm
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cpu_mem.append(cm)
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gpu_mem.append(gm)
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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repeat_iter = args.iter_num - warmup_iter
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end2end_statis = end2end_statis[warmup_iter:]
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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cpu_mem_repeat = cpu_mem[warmup_iter:]
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gpu_mem_repeat = gpu_mem[warmup_iter:]
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gpu_util_repeat = gpu_util[warmup_iter:]
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dump_result = dict()
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis) * 1000
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dump_result["cpu_rss_mb"] = cpu_mem / repeat_iter
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dump_result["gpu_rss_mb"] = gpu_mem / repeat_iter
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dump_result["gpu_util"] = gpu_util / repeat_iter
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat)
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dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat)
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dump_result["gpu_util"] = np.mean(gpu_util_repeat)
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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