def main(): parser = xdnn_io.default_parser_args() parser = yolo_parser_args(parser) args = parser.parse_args() args = xdnn_io.make_dict_args(args) g_nDispatchers = args['numprepproc'] g_nWorkers = args['numworkers'] # Setup the environment images = xdnn_io.getFilePaths(args['images']) if (args['golden'] or args['visualize']): assert args['labels'], "Provide --labels to compute mAP." assert args[ 'results_dir'], "For accuracy measurements, provide --results_dir to save the detections." # start comms xserver = xstream.Server() # acquire resources fmaster = FpgaMaster(args['vitis_rundir']) # update batch size inshape = list(fmaster.inshape) if args['batch_sz'] != -1: inshape[0] = args['batch_sz'] args['net_h'] = inshape[2] args['net_w'] = inshape[3] # spawn dispatchers dispatcher = yoloDispatcher(g_nDispatchers, g_nWorkers, inshape) # spawn workers workers = yoloWorkerPool(args['vitis_rundir'] + "_worker", g_nWorkers, args) # send work to system g_nQueries = int(np.ceil(len(images) / inshape[0])) work = [] for qIdx in range(g_nQueries): idx = qIdx * inshape[0] workBatch = [ images[(idx + i) % len(images)] for i in range(inshape[0]) ] work.append((qIdx, workBatch, (args['img_raw_scale'], args['img_mean'], args['img_input_scale']))) startTime = timeit.default_timer() dispatcher.run(work) del dispatcher t = timeit.default_timer() - startTime print("Queries: %d, Elapsed: %.2fs, QPS: %.2f, FPS: %.2f" \ % (g_nQueries, t, g_nQueries / t, g_nQueries * inshape[0] / t)) sys.stdout.flush() # cleanup del workers del fmaster del xserver # mAP calculation if (args['golden']): print() print("Computing mAP score : ") labels = xdnn_io.get_labels(args['labels']) print("Class names are : {} ".format(labels)) mAP = calc_detector_mAP(args['results_dir'], args['golden'], len(labels), labels, args['prob_threshold'], args['mapiouthresh'], args['points']) sys.stdout.flush()
def run(args=None): if not args: parser = xdnn_io.default_parser_args() parser = yolo_parser_args(parser) parser.add_argument('--startxstream', default=True, action='store_true', help='automatically start obj store server') parser.add_argument('--servermode', default=False, action='store_true', help='accept images from another process') parser.add_argument("--deploymodel", type=str, default='', help='Original prototxt') parser.add_argument("--caffemodel", type=str, default='', help='Original caffemodel') args = parser.parse_args() args = xdnn_io.make_dict_args(args) args['preprocseq'] = [('resize', (224, 224)), ('meansub', [104.007, 116.669, 122.679]), ('chtranspose', (2, 0, 1))] if (args['golden'] or args['visualize']): assert args['labels'], "Provide --labels to compute mAP." assert args[ 'results_dir'], "For accuracy measurements, provide --results_dir to save the detections." labels = xdnn_io.get_labels(args['labels']) colors = generate_colors(len(labels)) args['startxstream'] = True args['servermode'] = False timerQ = Queue() args['timerQ'] = timerQ compJson = xdnn.CompilerJsonParser(args['netcfg']) firstInputShape = next(itervalues(compJson.getInputs())) args['net_h'] = firstInputShape[2] args['net_w'] = firstInputShape[3] # start object store # (make sure to 'pip install pyarrow') xserver = None if args['startxstream']: xserver = xstream.Server() graph = grapher.Graph("yolo_v2") graph.node("prep", yolov2_pre.Node, args) graph.node("fpga", yolov2_fpga.Node, args) graph.node("post", yolov2_post.Node, args) graph.edge("START", None, "prep") graph.edge("prep", "prep", "fpga") graph.edge("fpga", "fpga", "post") graph.edge("DONE", "post", "fpga") graph.edge("DONE", "post", None) if not args['servermode']: graph.serve(background=True) img_paths = xdnn_io.getFilePaths(args['images']) reqProc = mp.Process(target=request_process, args=( args, img_paths, graph._in[0], graph._out[0], )) t = timeit.default_timer() reqProc.start() reqProc.join() graph.stop(kill=False) t2 = args['timerQ'].get() full_time = t2 - t args['timerQ'].close() print("Total time : {}s for {} images".format(full_time, len(img_paths))) print("Average FPS : {} imgs/sec".format(len(img_paths) / full_time)) else: print("Serving %s -> %s" % (graph._in[0], graph._out[0])) graph.serve() # mAP calculation if (args['golden']): print(flush=True) print("Computing mAP score : ", flush=True) print("Class names are : {} ".format(labels), flush=True) mAP = calc_detector_mAP(args['results_dir'], args['golden'], len(labels), labels,\ args['prob_threshold'], args['mapiouthresh'], args['points']) sys.stdout.flush()
def main(): parser = argparse.ArgumentParser() parser = yolo_parser_args(parser) parser.add_argument( '--deploymodel', help="network definition prototxt file in case of caffe", required=True, type=extant_file, metavar="FILE") parser.add_argument( '--caffemodel', help="network weights caffe model file in case of caffe", required=True, type=extant_file, metavar="FILE") parser.add_argument('--images', nargs='*', help='directory or raw image files to use as input', required=True, type=extant_file, metavar="FILE") parser.add_argument('--labels', help='label ID', type=extant_file, metavar="FILE") parser.add_argument('--golden', help='Ground truth directory', type=extant_file, metavar="FILE") parser.add_argument( '--mean_value', type=int, nargs=3, default=[0, 0, 0], # BGR for Caffe help='image mean values ') parser.add_argument('--pxscale', type=float, default=(1.0 / 255.0), help='pix cale value') parser.add_argument( '--transpose', type=int, default=[2, 0, 1], nargs=3, help= "Passed to caffe.io.Transformer function set_transpose, default 2,0,1") parser.add_argument( '--channel_swap', type=int, default=[2, 1, 0], nargs=3, help= "Passed to caffe.io.Transformer function set_channel_swap, default 2,1,0" ) parser.add_argument('--caffe_backend_path', help='caffe backend') parser.add_argument('--gpu', type=int, default=None, help='GPU-ID to run Caffe inference on GPU') args = parser.parse_args() args = xdnn_io.make_dict_args(args) num_images_processed = yolo_gpu_inference( args['caffe_backend_path'], args['images'], args['deploymodel'], args['caffemodel'], args['results_dir'], args['iouthresh'], args['scorethresh'], args['mean_value'], args['pxscale'], args['transpose'], args['channel_swap'], args['yolo_model'], args['classes'], args) print('num images processed : ', num_images_processed) # mAP calculation if (args['golden']): labels = xdnn_io.get_labels(args['labels']) print() print("Computing mAP score : ") print("Class names are : {} ".format(labels)) mAP = calc_detector_mAP(args['results_dir'], args['golden'], len(labels), labels, args['prob_threshold'], args['iouthresh']) sys.stdout.flush()
def main(): parser = xdnn_io.default_parser_args() parser = yolo_parser_args(parser) args = parser.parse_args() args = xdnn_io.make_dict_args(args) # Setup the environment img_paths = xdnn_io.getFilePaths(args['images']) if (args['golden'] or args['visualize']): assert args['labels'], "Provide --labels to compute mAP." assert args[ 'results_dir'], "For accuracy measurements, provide --results_dir to save the detections." labels = xdnn_io.get_labels(args['labels']) colors = generate_colors(len(labels)) if args['yolo_version'] == 'v2': yolo_postproc = yolo.yolov2_postproc elif args['yolo_version'] == 'v3': yolo_postproc = yolo.yolov3_postproc runner = Runner(args['vitis_rundir']) # Setup the blobs inTensors = runner.get_input_tensors() outTensors = runner.get_output_tensors() batch_sz = args['batch_sz'] if batch_sz == -1: batch_sz = inTensors[0].dims[0] fpgaBlobs = [] for io in [inTensors, outTensors]: blobs = [] for t in io: shape = (batch_sz, ) + tuple([t.dims[i] for i in range(t.ndims)][1:]) blobs.append(np.empty((shape), dtype=np.float32, order='C')) fpgaBlobs.append(blobs) fpgaInput = fpgaBlobs[0][0] # Setup the YOLO config net_h, net_w = fpgaInput.shape[-2:] args['net_h'] = net_h args['net_w'] = net_w biases = bias_selector(args) # Setup profiling env prep_time = 0 exec_time = 0 post_time = 0 # Start the execution for i in range(0, len(img_paths), batch_sz): pl = [] img_shapes = [] # Prep images t1 = timeit.default_timer() for j, p in enumerate(img_paths[i:i + batch_sz]): fpgaInput[j, ...], img_shape = xdnn_io.loadYoloImageBlobFromFile( p, net_h, net_w) pl.append(p) img_shapes.append(img_shape) t2 = timeit.default_timer() # Execute jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1]) runner.wait(jid) # Post Proc t3 = timeit.default_timer() boxes = yolo_postproc(fpgaBlobs[1], args, img_shapes, biases=biases) t4 = timeit.default_timer() prep_time += (t2 - t1) exec_time += (t3 - t2) post_time += (t4 - t3) for i in range(min(batch_sz, len(img_shapes))): print("Detected {} boxes in {}".format(len(boxes[i]), pl[i])) # Save the result if (args['results_dir']): for i in range(min(batch_sz, len(img_shapes))): filename = os.path.splitext(os.path.basename(pl[i]))[0] out_file_txt = os.path.join(args['results_dir'], filename + '.txt') print("Saving {} boxes to {}".format(len(boxes[i]), out_file_txt)) sys.stdout.flush() saveDetectionDarknetStyle(out_file_txt, boxes[i], img_shapes[i]) if (args['visualize']): out_file_png = os.path.join(args['results_dir'], filename + '.png') print("Saving result to {}".format(out_file_png)) sys.stdout.flush() draw_boxes(pl[i], boxes[i], labels, colors, out_file_png) # Profiling results if (args['profile']): print("\nAverage Latency in ms:") print(" Image Prep: {0:3f}".format(prep_time * 1000.0 / len(img_paths))) print(" Exec: {0:3f}".format(exec_time * 1000.0 / len(img_paths))) print(" Post Proc: {0:3f}".format(post_time * 1000.0 / len(img_paths))) sys.stdout.flush() # mAP calculation if (args['golden']): print() print("Computing mAP score : ") print("Class names are : {} ".format(labels)) mAP = calc_detector_mAP(args['results_dir'], args['golden'], len(labels), labels, args['prob_threshold'], args['mapiouthresh'], args['points']) sys.stdout.flush()
print(" Post Proc: {0:3f}".format(post_time * 1000.0 / len(img_paths))) sys.stdout.flush() # mAP calculation if(args['golden']): print() print("Computing mAP score : ") print("Class names are : {} ".format(labels)) mAP = calc_detector_mAP(args['results_dir'], args['golden'], len(labels), labels, args['prob_threshold'], args['iouthresh']) sys.stdout.flush() if __name__ == '__main__': #main() parser = xdnn_io.default_parser_args() parser = yolo_parser_args(parser) args = parser.parse_args() args = xdnn_io.make_dict_args(args) q_img = mp.Queue() q_shape = mp.Queue() #Creating a process to run HW pre-processing kernel p_preprocess = mp.Process(target=pre_process,args=(q_img, q_shape, args)) #Process to run XDNN p_xdnn = mp.Process(target=process_xdnn,args=(q_img, q_shape, args)) p_preprocess.start() p_xdnn.start() p_preprocess.join() p_xdnn.join()