def parse_graph(graph, args, name):
manager = node_manager.NodeManager()
graphObj = grapher.Graph(name)
use_const_args = True if len(args) == 1 else False
service_args = []
for index, node in enumerate(graph['nodes']):
node_path = manager.get_path(node)
try:
module = importlib.import_module(str(node_path))
except Exception as e:
print(e)
node_args = args[0] if use_const_args else args[index]
graphObj.node('n_' + str(index), module.Node, node_args, node)
try:
node_args = module.Args().to_dict()
node_args['name'] = node
service_args.append(node_args)
except AttributeError:
service_args.append(None)
for edge in graph['edges']:
(start, end, index) = edge
if start == -1:
graphObj.edge("e" + str(index), None, "n_" + str(end))
elif end == -1:
graphObj.edge("e" + str(index), "n_" + str(start), None)
else:
graphObj.edge("e" + str(index), "n_" + str(start), "n_" + str(end))
return graphObj, module, service_args
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 run(args=None):
if not args:
parser = xdnn_io.default_parser_args()
parser.add_argument('--numprepproc',
type=int,
default=1,
help='# parallel procs to decode/quantize images')
parser.add_argument('--numstream',
type=int,
default=6,
help='number of FPGA streams')
parser.add_argument('--deviceID',
type=int,
default=0,
help='FPGA no. -> FPGA ID to use multiple FPGAs')
parser.add_argument('--benchmarkmode',
type=int,
default=0,
help='bypass pre/post processing for benchmarking')
parser.add_argument('--startxstream',
default=False,
action='store_true',
help='automatically start obj store server')
parser.add_argument('--servermode',
default=False,
action='store_true',
help='accept images from another process')
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))]
# start object store
# (make sure to 'pip install pyarrow')
xserver = None
if args['startxstream']:
xserver = xstream.Server()
graph = grapher.Graph("imagenet")
graph.node("prep", pre.Node, args)
graph.node("fpga", fpga.Node, args)
graph.node("post", 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],
))
reqProc.start()
reqProc.join()
graph.stop(kill=False)
else:
print("Serving %s -> %s" % (graph._in[0], graph._out[0]))
graph.serve()