def main(args=None):
os.environ['LS_BIND_NOW'] = "1"
args = xdnn_io.processCommandLine()
images = xdnn_io.getFilePaths(args['images'])
# spawn dispatcher
dispatcher = Dispatcher(args['vitis_rundir'], g_nFPGA, g_nDispatchers,
args['batch_sz'])
inshape = dispatcher.inshape
# send work to system
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()
def main():
args = xdnn_io.processCommandLine()
runner = Runner(args['vitis_rundir'])
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = args['batch_sz']
if batch_sz == -1:
# use Runner's suggested batch size
batch_sz = inTensors[0].dims[0]
if args['golden']:
goldenMap = xdnn_io.getGoldenMap(args['golden'])
top5Count = 0
top1Count = 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)
img_paths = xdnn_io.getFilePaths(args['images'])
labels = xdnn_io.get_labels(args['labels'])
xdnnCPUOp = xdnn.XDNNCPUOp("%s/weights.h5" % args['vitis_rundir'])
fcOutput = np.empty((batch_sz, args['outsz'],), dtype=np.float32, order='C')
fpgaInput = fpgaBlobs[0][0]
for i in range(0, len(img_paths), batch_sz):
pl = []
# fill tensor input data from image file
for j, p in enumerate(img_paths[i:i + batch_sz]):
img, _ = xdnn_io.loadImageBlobFromFile(p,
args['img_raw_scale'], args['img_mean'], args['img_input_scale'],
fpgaInput.shape[2], fpgaInput.shape[3])
pl.append(p)
np.copyto(fpgaInput[j], img)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
xdnnCPUOp.computeFC(fpgaBlobs[1][0], fcOutput)
softmaxOut = xdnnCPUOp.computeSoftmax(fcOutput)
if args['golden']:
for j,p in enumerate(img_paths[i:i + batch_sz]):
top1Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 1)
top5Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 5)
else:
xdnn_io.printClassification(softmaxOut, pl, labels)
if args['golden']:
print ( ("\nAverage accuracy (n=%d) Top-1: %.1f%%, Top-5: %.1f%%\n") % (len(img_paths), float(top1Count)/float(len(img_paths))*100., float(top5Count)/float(len(img_paths))*100.) )
def pre_process(q, args):
xclbin_p = str(args['xclbin'] + "/xdnn_v3_96x16_2pe_8b_9mb_bank03.xclbin")
kernelName_p = "pp_pipeline_accel"
deviceIdx_p = args['deviceid']
fpga_pp = waa_rt.PreProcess(xclbin_p, kernelName_p, deviceIdx_p, 1)
batch_sz = args['batch_sz']
img_paths = xdnn_io.getFilePaths(args['images'])
for i in range(0, len(img_paths), batch_sz):
for j, p in enumerate(img_paths[i:i + batch_sz]):
arr, ht = fpga_pp.preprocess_input(p)
q.put(arr)
def pre_process(q,args):
xclbin_p=str(args['xclbin']+"/xdnn_v3_96x16_2pe_8b_9mb_bank03.xclbin")
kernelName_p="pp_pipeline_accel"
deviceIdx_p=args['deviceid']
fpga_pp = xplusml.PreProcess(xclbin_p,kernelName_p,deviceIdx_p)
batch_sz = args['batch_sz']
img_paths = xdnn_io.getFilePaths(args['images'])
print("Pre-processing handle created. Populating Queue")
for i in range(0, len(img_paths), batch_sz):
for j, p in enumerate(img_paths[i:i + batch_sz]):
arr, ht = fpga_pp.preprocess_input(p)
q.put(arr)
print("Queue populated")
def pre_process(q_img, q_shape,args):
xclbin_p=str(args['xclbin']+"/xdnn_v3_96x16_2pe_8b_9mb_bank03.xclbin")
kernelName_p="pp_pipeline_accel"
deviceIdx_p=args['deviceid']
handle_p = waa_rt.PreProcess(xclbin_p,kernelName_p,deviceIdx_p, 1)
if handle_p == -1:
print("Unable to Create handle for pre-processing kernel. Only U200 device is supported")
sys.exit()
batch_sz = args['batch_sz']
img_paths = xdnn_io.getFilePaths(args['images'])
print("Pre-processing handle created. Populating Queue")
for i in range(0, len(img_paths), batch_sz):
for j, p in enumerate(img_paths[i:i + batch_sz]):
arr, shape = handle_p.preprocess_input(p)
q_img.put(arr)
q_shape.put(shape)
def __init__(self):
global pool
global pFpgaRT
self.args = xdnn_io.processCommandLine() # Get command line args
pool = Pool(
self.args['numproc']
) # Depends on new switch, new switch is not added to xdnn_io.py as of yet
# Split Images into batches - list of lists
self.batches = []
self.all_image_paths = xdnn_io.getFilePaths(
self.args['images']) #[0:10000]
for i in range(0, len(self.all_image_paths), self.args['batch_sz']):
self.batches.append(self.all_image_paths[i:i +
self.args['batch_sz']])
pFpgaRT = xdnn.XDNNFPGAOp(self.args) # Parent process gets handle
self.args['inShape'] = (self.args['batch_sz'], ) + tuple(
tuple(pFpgaRT.getInputDescriptors().values())[0]
[1:]) # Save input shape for children
self.mpid = pFpgaRT.getMPID() # Save handle to use in child processes
def main():
args = xdnn_io.processCommandLine()
images = xdnn_io.getFilePaths(args['images'])
# start comms
xserver = xstream.Server()
# acquire resources
fmaster = FpgaMaster(args['vitis_rundir'])
inshape = list(fmaster.inshape)
if args['batch_sz'] != -1:
inshape[0] = args['batch_sz'] # update batch size
# spawn dispatchers
dispatcher = Dispatcher(g_nDispatchers, g_nWorkers, inshape)
# spawn workers
workers = WorkerPool(args['vitis_rundir'] + "_worker", g_nWorkers, args)
# send work to system
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))
# cleanup
del workers
del fmaster
del xserver
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.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')
parser.add_argument('--profile', action='store_true',
help='Print average latencies for preproc/exec/postproc')
args = parser.parse_args()
args = xdnn_io.make_dict_args(args)
sharedInputArrs = []
fpgaOutputs = []
compilerJSONObj = xdnn.CompilerJsonParser(args['netcfg'])
input_shapes = [v for k,v in compilerJSONObj.getInputs().items()]
output_shapes = [v for k,v in compilerJSONObj.getOutputs().items()]
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_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 +[(args['batch_sz'], 4)])
# shared memory from fpga forward to postprocessing
shared_output_arrs = SharedMemoryQueue("output",num_shared_slots, output_shapes + [(args['batch_sz'], 4)])
# Form list of images to chunks of batch_sz
img_paths = xdnn_io.getFilePaths(args['images'])
imgids = list(range(len(img_paths)))
imgid_chunks = [ imgids[i:i+args['batch_sz']] for i in range(0, len(img_paths), args['batch_sz']) ]
# Start all processes
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=(args, len(imgid_chunks), 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()
t1 = timeit.default_timer()
if args['perpetual']:
while True:
res = [p.map_async(run_pre_process, imgid_chunks)]
for j in res:
j.wait()
del j
else:
p.map_async(run_pre_process, imgid_chunks)
xdnnProc.join()
postProc.join()
p.close()
p.join()
t2 = timeit.default_timer()
total_t = t2 - t1
if(args['profile']):
print("Total time taken: {} s\n Total images: {}\nAverage FPS: {}".format(total_t, \
len(img_paths), len(img_paths)/total_t))
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 = 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()
def yolo_gpu_inference(backend_path, image_dir, deploy_model, weights,
out_labels, IOU_threshold, scorethresh, mean_value,
pxscale, transpose, channel_swap, yolo_model,
num_classes, args):
# 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."
labels = xdnn_io.get_labels(args['labels'])
colors = generate_colors(len(labels))
# Select postproc and biases
if args['yolo_version'] == 'v2': yolo_postproc = yolo.yolov2_postproc
elif args['yolo_version'] == 'v3': yolo_postproc = yolo.yolov3_postproc
biases = bias_selector(args)
import caffe
caffe.set_mode_cpu()
print(args)
if (args['gpu'] is not None):
caffe.set_mode_gpu()
caffe.set_device(args['gpu'])
net = caffe.Net(deploy_model, weights, caffe.TEST)
net_h, net_w = net.blobs['data'].data.shape[-2:]
args['net_h'] = net_h
args['net_w'] = net_w
for i, img in enumerate(images):
if ((i + 1) % 100 == 0): print(i + 1, "images processed")
raw_img, img_shape = xdnn_io.loadYoloImageBlobFromFile(
img, net_h, net_w)
net.blobs['data'].data[...] = raw_img
out = net.forward()
caffeOutput = sorted(out.values(), key=lambda item: item.shape[-1])
boxes = yolo_postproc(caffeOutput, args, [img_shape], biases=biases)
print("{}. Detected {} boxes in {}".format(i, len(boxes[0]), img))
# Save the result
boxes = boxes[0]
if (args['results_dir']):
filename = os.path.splitext(os.path.basename(img))[0]
out_file_txt = os.path.join(args['results_dir'], filename + '.txt')
print("Saving {} boxes to {}".format(len(boxes), out_file_txt))
sys.stdout.flush()
saveDetectionDarknetStyle(out_file_txt, boxes, img_shape)
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(img, boxes, labels, colors, out_file_png)
# draw_boxes(images[i],bboxes,class_names,colors=[(0,0,0)]*num_classes)
return len(images)
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()
