def fpga_init():
# Parse arguments
parser = xdnn_io.default_parser_args()
parser.add_argument('--deviceID', type=int, default=0,
help='FPGA no. -> FPGA ID to run in case multiple FPGAs')
args = parser.parse_args()
args = xdnn_io.make_dict_args(args)
# Create manager
if not xdnn.createManager():
raise Exception("Failed to create manager")
compilerJSONObj = xdnn.CompilerJsonParser(args['netcfg'])
# Get input and output shape
input_shapes = list(map(lambda x: (x), compilerJSONObj.getInputs().itervalues()))
output_shapes = list(map(lambda x: (x), compilerJSONObj.getOutputs().itervalues()))
for in_idx in range(len(input_shapes)):
input_shapes[in_idx][0] = args['batch_sz']
for out_idx in range(len(output_shapes)):
output_shapes[out_idx][0] = args['batch_sz']
input_node_names = list(map(lambda x: str(x), compilerJSONObj.getInputs().iterkeys()))
output_node_names = list(map(lambda x: str(x), compilerJSONObj.getOutputs().iterkeys()))
num_inputs = len(input_shapes)
num_outputs = len(output_shapes)
# Create runtime
ret, handles = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0", [args["deviceID"]])
if ret != 0:
raise Exception("Failed to create handle, return value: {error}".format(error=ret))
fpgaRT = xdnn.XDNNFPGAOp(handles, args)
print("Batch size:", args['batch_sz'])
print("Input shapes:", input_shapes)
print("Input nodes:", input_node_names)
print("Ouput shapes:", output_shapes)
print("Ouput nodes:", output_node_names)
output_buffers = []
for _ in range(N_STREAMS):
buffer = {name: np.empty(shape=shape, dtype=np.float32)
for name, shape in zip(output_node_names, output_shapes)}
output_buffers.append(buffer)
# fpgaRT.exec_async({input_node_names[0]: np.zeros(input_shapes[0])},
# output_buffers[0], 0)
# fpgaRT.get_result(0)
(fcWeight, fcBias) = xdnn_io.loadFCWeightsBias(args)
return fpgaRT, output_buffers,\
{name: shape for name, shape in zip(input_node_names, input_shapes)},\
fcWeight, fcBias
def setup(self, bottom, top):
_args = eval(self.param_str)
#args = xdnn_io.make_dict_args(param_dict)
if 'save' in _args and len(_args['save']) > 0:
import os.path as osp
import os
if not osp.exists(_args['save']): os.makedirs(_args['save'])
self.rt = compilerxdnnRT(_args['netcfg'], _args['weights'],
_args['device'], _args["input_names"],
_args["output_names"], _args['xdnnv3'],
_args['save'])
self._indictnames = _args["input_names"]
self._outdictnames = _args["output_names"]
self._parser = xdnn.CompilerJsonParser(_args["netcfg"])
def setup(self, bottom, top):
self.param_dict = eval(self.param_str) # Get args from prototxt
self._args = xdnn_io.make_dict_args(self.param_dict)
self._numPE = self._args[
"batch_sz"] # Bryan hack to detremine number of PEs in FPGA
# Establish FPGA Communication, Load bitstream
ret, handles = xdnn.createHandle(self._args["xclbin"], "kernelSxdnn_0")
if ret != 0:
raise Exception("Failed to open FPGA handle.")
self._args["scaleB"] = 1
self._args["PE"] = -1
# Instantiate runtime interface object
self.fpgaRT = xdnn.XDNNFPGAOp(handles, self._args)
self._indictnames = self._args["input_names"]
self._outdictnames = self._args["output_names"]
self._parser = xdnn.CompilerJsonParser(self._args["netcfg"])
def __init__(self, params):
self._args = xdnn_io.make_dict_args(params)
self._numPE = self._args[
"batch_sz"
] # Bryan hack to detremine number of PEs in FPGA
# Establish FPGA Communication, Load bitstream
ret, handles = xdnn.createHandle(self._args["xclbin"], "kernelSxdnn_0")
if ret != 0:
raise Exception("Failed to open FPGA handle.")
self._args["scaleB"] = 1
self._args["PE"] = -1
self._streamIds = [0, 1, 2, 3, 4, 5, 6, 7] # Allow 8 streams
# Instantiate runtime interface object
self.fpgaRT = xdnn.XDNNFPGAOp(handles, self._args)
self._indictnames = self._args["input_names"]
self._outdictnames = self._args["output_names"]
self._parser = xdnn.CompilerJsonParser(self._args["netcfg"])
def run(self, imgList, fpgaOutput_list, fpgaOutputShape_list, shapeArr):
if self.numProcessed == 0:
self.startTime = timeit.default_timer()
self.labels = xdnn_io.get_labels(self.args['labels'])
self.zmqPub = None
if self.args['zmqpub']:
self.zmqPub = mp_classify.ZmqResultPublisher(
self.args['deviceID'])
self.goldenMap = None
self.numProcessed += len(imgList)
firstInputShape = xdnn.CompilerJsonParser(
self.args['netcfg']).getInputs().itervalues().next()
if ((args['yolo_model'] == 'standard_yolo_v3')
or (args['yolo_model'] == 'tiny_yolo_v3')):
num_ouptut_layers = len(fpgaOutput_list)
fpgaOutput = []
for idx in range(num_ouptut_layers):
fpgaOutput.append(
np.frombuffer(fpgaOutput_list[idx],
dtype=np.float32).reshape(
tuple(fpgaOutputShape_list[idx])))
bboxlist_for_images = det_postprocess(fpgaOutput, args, shapeArr)
for i in range(min(self.args['batch_sz'], len(shapeArr))):
print "image: ", imgList[
i], " has num boxes detected : ", len(
bboxlist_for_images[i])
else:
fpgaOutput = fpgaOutput_list[0]
fpgaOutputShape = fpgaOutputShape_list[0]
npout_view = np.frombuffer(fpgaOutput, dtype=np.float32)\
.reshape(tuple(fpgaOutputShape))
npout_view = npout_view.flatten()
fpgaoutsz = fpgaOutputShape[1] * fpgaOutputShape[
2] * fpgaOutputShape[3]
bboxlist_for_images = []
for i in range(min(self.args['batch_sz'], len(shapeArr))):
startidx = i * fpgaoutsz
softmaxout = npout_view[startidx:startidx + fpgaoutsz]
# first activate first two channels of each bbox subgroup (n)
for b in range(self.args['bboxplanes']):
for r in range(\
self.args['batchstride']*b,
self.args['batchstride']*b+2*self.args['groups']):
softmaxout[r] = sigmoid(softmaxout[r])
for r in range(\
self.args['batchstride']*b\
+self.args['groups']*self.args['coords'],
self.args['batchstride']*b\
+self.args['groups']*self.args['coords']+self.args['groups']):
softmaxout[r] = sigmoid(softmaxout[r])
# Now softmax on all classification arrays in image
for b in range(self.args['bboxplanes']):
for g in range(self.args['groups']):
softmax(
self.args['beginoffset'] +
b * self.args['batchstride'] +
g * self.args['groupstride'], softmaxout,
softmaxout, self.args['outsz'],
self.args['groups'])
# NMS
bboxes = nms.do_baseline_nms(
softmaxout, shapeArr[i][1], shapeArr[i][0],
firstInputShape[2], firstInputShape[3], self.args['out_w'],
self.args['out_h'], self.args['bboxplanes'],
self.args['outsz'], self.args['scorethresh'],
self.args['iouthresh'])
bboxlist_for_images.append(bboxes)
print "image: ", imgList[
i], " has num boxes detected : ", len(bboxes)
if self.args['golden'] is None:
return
for i in range(min(self.args['batch_sz'], len(shapeArr))):
filename = imgList[i]
out_file_txt = ((filename.split("/")[-1]).split(".")[0])
out_file_txt = self.args[
'detection_labels'] + "/" + out_file_txt + ".txt"
out_line_list = []
bboxes = bboxlist_for_images[i]
for j in range(len(bboxes)):
x, y, w, h = darknet_style_xywh(shapeArr[i][1], shapeArr[i][0],
bboxes[j]["ll"]["x"],
bboxes[j]["ll"]["y"],
bboxes[j]['ur']['x'],
bboxes[j]['ur']['y'])
line_string = str(bboxes[j]["classid"])
line_string = line_string + " " + str(
round(bboxes[j]['prob'], 3))
line_string = line_string + " " + str(x)
line_string = line_string + " " + str(y)
line_string = line_string + " " + str(w)
line_string = line_string + " " + str(h)
out_line_list.append(line_string + "\n")
log.info("writing this into prediction file at %s" %
(out_file_txt))
with open(out_file_txt, "w") as the_file:
for lines in out_line_list:
the_file.write(lines)
default=5,
help='thresohold on iouthresh across 2 candidate detections')
parser.add_argument(
'--detection_labels',
help="direcotry path detected lable files in darknet style",
default=None,
type=str,
metavar="FILE")
parser.add_argument('--prob_threshold',
type=float,
default=0.1,
help='threshold for calculation of f1 score')
args = parser.parse_args()
args = xdnn_io.make_dict_args(args)
compilerJSONObj = xdnn.CompilerJsonParser(args['netcfg'])
firstInputShape = compilerJSONObj.getInputs().itervalues().next()
firstOutputShape = compilerJSONObj.getOutputs().itervalues().next()
out_w = firstOutputShape[2]
out_h = firstOutputShape[3]
args['net_w'] = int(firstInputShape[2])
args['net_h'] = int(firstInputShape[3])
args['out_w'] = int(out_w)
args['out_h'] = int(out_h)
args['coords'] = 4
args['beginoffset'] = (args['coords'] + 1) * int(out_w * out_h)
args['groups'] = int(out_w * out_h)
args['batchstride'] = args['groups'] * (args['outsz'] + args['coords'] + 1)
args['groupstride'] = 1
args['classes'] = args['outsz']
print('drawing boxes time: {0} seconds'.format(end_time - start_time))
if __name__ == '__main__':
frame_q = mp.Queue()
resize_q = mp.Queue()
trans_q = mp.Queue()
output_q = mp.Queue()
face_q = mp.Queue()
ready_fpga = mp.Queue()
sharedInputArrs = []
compilerJSONObj = xdnn.CompilerJsonParser('deploy.compiler.json')
input_shapes = map(lambda x: tuple(x),
compilerJSONObj.getInputs().itervalues())
output_shapes = map(lambda x: tuple(x),
compilerJSONObj.getOutputs().itervalues())
input_sizes = map(lambda x: np.prod(x), input_shapes)
output_sizes = map(lambda x: np.prod(x), output_shapes)
print input_shapes
print output_shapes
# shared memory from video capture to preprocessing
shared_frame_arrs = SharedMemoryQueue("frame", num_shared_slots,
[(320, 320, 3)])
def fpga_init():
global PORT
global N_STREAMS
# Parse arguments
parser = xdnn_io.default_parser_args()
parser.add_argument('--device-ids',
type=int,
default=[0],
nargs="+",
help='a list of device IDs for FPGA')
parser.add_argument('--port',
type=int,
default=5000,
help='port to listen on')
args = parser.parse_args()
device_ids = args.device_ids
PORT = args.port
N_STREAMS *= len(device_ids)
args = xdnn_io.make_dict_args(args)
# Create manager
if not xdnn.createManager():
raise Exception("Failed to create manager")
compilerJSONObj = xdnn.CompilerJsonParser(args['netcfg'])
# Get input and output shape
input_shapes = list(
map(lambda x: (x),
compilerJSONObj.getInputs().itervalues()))
output_shapes = list(
map(lambda x: (x),
compilerJSONObj.getOutputs().itervalues()))
for in_idx in range(len(input_shapes)):
input_shapes[in_idx][0] = args['batch_sz']
for out_idx in range(len(output_shapes)):
output_shapes[out_idx][0] = args['batch_sz']
input_node_names = list(
map(lambda x: str(x),
compilerJSONObj.getInputs().iterkeys()))
output_node_names = list(
map(lambda x: str(x),
compilerJSONObj.getOutputs().iterkeys()))
num_inputs = len(input_shapes)
num_outputs = len(output_shapes)
# Create runtime
ret, handles = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0",
device_ids)
if ret != 0:
raise Exception(
"Failed to create handle, return value: {error}".format(error=ret))
fpgaRT = xdnn.XDNNFPGAOp(handles, args)
print("Batch size:", args['batch_sz'])
print("Input shapes:", input_shapes)
print("Input nodes:", input_node_names)
print("Ouput shapes:", output_shapes)
print("Ouput nodes:", output_node_names)
print("Using model {path}".format(path=args["netcfg"]))
print("Using FPGA device:", device_ids)
output_buffers = []
for _ in range(N_STREAMS):
buffer = {
name: np.empty(shape=shape, dtype=np.float32)
for name, shape in zip(output_node_names, output_shapes)
}
output_buffers.append(buffer)
# fpgaRT.exec_async({input_node_names[0]: np.zeros(input_shapes[0])},
# output_buffers[0], 0)
# fpgaRT.get_result(0)
(fcWeight, fcBias) = xdnn_io.loadFCWeightsBias(args)
return fpgaRT, output_buffers, output_node_names[0],\
{name: shape for name, shape in zip(input_node_names, input_shapes)},\
fcWeight, fcBias, args['batch_sz']
def run(args=None):
if not args:
parser = xdnn_io.default_parser_args()
parser.add_argument(
'--numprepproc',
type=int,
default=1,
help=
'number of parallel processes used to decode and quantize images')
parser.add_argument('--numstream',
type=int,
default=16,
help='number of FPGA streams')
parser.add_argument(
'--deviceID',
type=int,
default=0,
help='FPGA no. -> FPGA ID to run in case multiple FPGAs')
parser.add_argument('--benchmarkmode',
type=int,
default=0,
help='bypass pre/post processing for benchmarking')
args = parser.parse_args()
args = xdnn_io.make_dict_args(args)
if not xdnn.createManager():
sys.exit(1)
fpgaRT = None
sharedInputArrs = []
fpgaOutputs = []
compilerJSONObj = xdnn.CompilerJsonParser(args['netcfg'])
input_shapes = map(lambda x: (x), compilerJSONObj.getInputs().itervalues())
output_shapes = map(lambda x: (x),
compilerJSONObj.getOutputs().itervalues())
#args['batch_sz'] = 1
for out_idx in range(len(output_shapes)):
output_shapes[out_idx][0] = args['batch_sz']
input_sizes = map(lambda x: np.prod(x), input_shapes)
output_sizes = map(lambda x: np.prod(x), output_shapes)
num_shared_slots = args['numstream']
# shared memory from preprocessing to fpga forward
shared_trans_arrs = SharedMemoryQueue(
"trans", num_shared_slots * (args['numprepproc'] * args['batch_sz']),
input_shapes + [(4)])
# shared memory from fpga forward to postprocessing
shared_output_arrs = SharedMemoryQueue(
"output", num_shared_slots, output_shapes + [(args['batch_sz'], 4)])
img_paths = xdnn_io.getFilePaths(args['images'])
p = mp.Pool(initializer=init_pre_process,
initargs=(
args,
img_paths,
input_shapes,
shared_trans_arrs,
),
processes=args['numprepproc'])
xdnnProc = mp.Process(target=fpga_process,
args=(
fpgaRT,
args,
len(img_paths),
compilerJSONObj,
shared_trans_arrs,
shared_output_arrs,
))
postProc = mp.Process(target=post_process,
args=(
args,
img_paths,
fpgaOutputs,
output_shapes,
shared_output_arrs,
))
xdnnProc.start()
postProc.start()
if args['perpetual']:
while True:
res = [p.map_async(run_pre_process, range(len(img_paths)))]
for j in res:
j.wait()
del j
else:
p.map_async(run_pre_process, range(len(img_paths)))
xdnnProc.join()
postProc.join()
p.close()
p.join()