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()