def run(): csv_file_path = args.csv_file_path model_path = args.model_dir precision = args.precision # System Check system_check = utilities(jetson_devkit=args.jetson_devkit, gpu_freq=args.gpu_freq, dla_freq=args.dla_freq) if system_check.check_trt(): sys.exit() system_check.set_power_mode(args.power_mode, args.jetson_devkit) system_check.clear_ram_space() system_check.run_set_clocks_withDVFS() system_check.set_jetson_fan(255) benchmark_data = read_write_data(csv_file_path=csv_file_path, model_path=model_path) # Actually run the ssd-mobilenet-v1 benchmark here model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=7) if not download_err: fps, error_log = model.report() if not error_log: model.remove() system_check.clear_ram_space() system_check.set_jetson_fan(0) return fps[3]
def run(): csv_file_path = args.csv_file_path model_path = args.model_dir precision = args.precision # System Check system_check = utilities(jetson_devkit=args.jetson_devkit, gpu_freq=args.gpu_freq, dla_freq=args.dla_freq) if system_check.check_trt(): sys.exit() system_check.set_power_mode(args.power_mode, args.jetson_devkit) system_check.clear_ram_space() system_check.run_set_clocks_withDVFS() system_check.set_jetson_fan(255) benchmark_data = read_write_data(csv_file_path=csv_file_path, model_path=model_path) # Run through all the benchmarks here running_total = 0 running_total += inception_benchmark.run() running_total += vgg_benchmark.run() running_total += superres_benchmark.run() running_total += unet_benchmark.run() running_total += pose_benchmark.run() running_total += yolo_benchmark.run() running_total += resnet_benchmark.run() running_total += mobilenet_benchmark.run() average_fps = running_total / 8 system_check.clear_ram_space() system_check.set_jetson_fan(0) return average_fps
def main(): camera = cv2.VideoCapture(0) i = 0 util_funcs = utilities() while True: return_value, image = camera.read() cv2.imwrite('temp/'+'opencv'+str(i)+'.png', image) test_images,x1s,y1s,widths,heights = util_funcs.extract_multiple_faces('temp/'+'opencv'+str(i)+'.png') test_image = 'temp/'+'opencv'+str(i)+'.png' util_funcs.predict(test_image,test_images,x1s,y1s,widths,heights) cv2.imwrite('temp/'+'opencv'+str(i)+'.png', image) if cv2.waitKey(1) & 0xFF == ord('q'): break i+=1 camera.release() cv2.destroyAllWindows()
def main(): # System Check arg_parser = setup_argparser() args = arg_parser.make_args() system_check = utilities(jetson_devkit=args.jetson_devkit, gpu_freq=args.gpu_freq, dla_freq=args.dla_freq) if args.benchmark_mode == 1: print("Set Jetson Benchmark Mode : ON!\n") system_check.close_all_notice() if system_check.check_trt(): sys.exit() system_check.set_power_mode(args.power_mode, args.jetson_devkit) system_check.clear_ram_space() if args.jetson_clocks: system_check.set_jetson_clocks() else: system_check.run_set_clocks_withDVFS() system_check.set_jetson_fan(255) else: system_check.clear_ram_space() system_check.set_jetson_fan(0) print("Set Jetson Benchmark Mode : OFF!\n")
def main(): # Set Parameters arg_parser = benchmark_argparser() args = arg_parser.make_args() csv_file_path = args.csv_file_path model_path = args.model_dir precision = args.precision dummy_check = utilities() if dummy_check.check_trt(): print( "Couldn't find TensorRT, please check trtexec in the path(\"/usr/src/tensorrt/bin/trtexec \")" ) sys.exit() dummy_check.close_all_warning() # Read CSV and Write Data benchmark_data = read_write_data(csv_file_path=csv_file_path, model_path=model_path) if args.all: latency_each_model = [] print( "Running all benchmarks.. This would take long time if the trt engine is not prepared..." ) for read_index in range(0, len(benchmark_data)): gc.collect() model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=read_index) if not download_err: # Reading Results latency_fps, error_log = model.report() latency_each_model.append(latency_fps) del gc.garbage[:] benchmark_table = pd.DataFrame(latency_each_model, columns=[ 'GPU (ms)', 'DLA0 (ms)', 'DLA1 (ms)', 'FPS', 'Model Name' ], dtype=float) # Note: GPU, DLA latencies are measured in miliseconds, FPS = Frames per Second print(benchmark_table[['Model Name', 'FPS']]) if args.plot: benchmark_data.plot_perf(latency_each_model) elif args.model_name == 'inception_v4': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=0) if not download_err: _, error_log = model.report() elif args.model_name == 'super_resolution': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=2) if not download_err: _, error_log = model.report() elif args.model_name == 'unet': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=3) if not download_err: _, error_log = model.report() elif args.model_name == 'tiny-yolov3': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=5) if not download_err: _, error_log = model.report() elif args.model_name == 'resnet': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=6) if not download_err: _, error_log = model.report() elif args.model_name == 'ssd-mobilenet-v1': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=7) if not download_err: _, error_log = model.report()
def main(): # Set Parameters arg_parser = benchmark_argparser() args = arg_parser.make_args() csv_file_path = args.csv_file_path model_path = args.model_dir precision = args.precision # System Check system_check = utilities(jetson_devkit=args.jetson_devkit, gpu_freq=args.gpu_freq, dla_freq=args.dla_freq) system_check.close_all_apps() if system_check.check_trt(): sys.exit() system_check.set_power_mode(args.power_mode, args.jetson_devkit) system_check.clear_ram_space() if args.jetson_clocks: system_check.set_jetson_clocks() else: system_check.run_set_clocks_withDVFS() system_check.set_jetson_fan(255) # Read CSV and Write Data benchmark_data = read_write_data(csv_file_path=csv_file_path, model_path=model_path) if args.all: latency_each_model =[] print("Running all benchmarks.. This will take at least 2 hours...") for read_index in range (0,len(benchmark_data)): gc.collect() model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=read_index) if not download_err: # Reading Results latency_fps, error_log = model.report() latency_each_model.append(latency_fps) # Remove engine and txt files if not error_log: model.remove() del gc.garbage[:] system_check.clear_ram_space() benchmark_table = pd.DataFrame(latency_each_model, columns=['GPU (ms)', 'DLA0 (ms)', 'DLA1 (ms)', 'FPS', 'Model Name'], dtype=float) # Note: GPU, DLA latencies are measured in miliseconds, FPS = Frames per Second print(benchmark_table[['Model Name', 'FPS']]) if args.plot: benchmark_data.plot_perf(latency_each_model) elif args.model_name == 'inception_v4': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=0) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'vgg19': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=1) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'super_resolution': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=2) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'unet': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=3) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'pose_estimation': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=4) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'tiny-yolov3': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=5) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'resnet': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=6) if not download_err: _, error_log = model.report() if not error_log: model.remove() elif args.model_name == 'ssd-mobilenet-v1': model = run_benchmark_models(csv_file_path=csv_file_path, model_path=model_path, precision=precision, benchmark_data=benchmark_data) download_err = model.execute(read_index=7) if not download_err: _, error_log = model.report() if not error_log: model.remove() system_check.clear_ram_space() system_check.set_jetson_fan(0)