def _run(work):
try:
(idx, images, args) = work
chanIdx = idx % Dispatcher.nWorkers
token = pid2TokenStr()
shape = Dispatcher.inshape
image_shapes = []
for i, img in enumerate(images):
cached = Dispatcher.inBlobCache.get(img)
if cached is None:
Dispatcher.inBlob[token][i, ...], img_shape = xdnn_io.loadYoloImageBlobFromFile(img, shape[2], shape[3])
Dispatcher.inBlobCache.set(img, (Dispatcher.inBlob[token][i].copy(), img_shape))
image_shapes.append(img_shape)
else:
Dispatcher.inBlob[token][i, ...] = cached[0]
image_shapes.append(cached[1])
meta = { 'id': idx, 'from': token, 'shape': shape, 'images': images, 'image_shapes': image_shapes }
if idx % 1000 == 0:
print("Put query %d to objstore" % idx)
sys.stdout.flush()
Dispatcher.xspub[token].put_blob(chanIdx2Str(chanIdx), Dispatcher.inBlob[token], meta)
Dispatcher.xstoken[token].get_msg()
except Exception as e:
logging.error("Producer exception " + str(e))
def run(self, inum_chunk):
write_slot = self._shared_trans_arrs.openWriteId()
write_arrs = self._shared_trans_arrs.accessNumpyBuffer(write_slot)
if not self._args['benchmarkmode']:
for i, inum in enumerate(inum_chunk):
write_arrs[0][i][:], shape = xdnn_io.loadYoloImageBlobFromFile(self._imgpaths[inum], self._firstInputShape[2], self._firstInputShape[3])
write_arrs[-1][i][0] = inum
write_arrs[-1][i][1:4] = shape
# Fill -1 for unfilled image slots in whole batch
write_arrs[-1][len(inum_chunk):][:] = -1
self._shared_trans_arrs.closeWriteId(write_slot)
def _run(self, objId):
(meta, inbuf) = self._xsIn.obj_get(objId)
# print("PRE get input : {} ".format(meta))
try:
if 'path' in meta:
img = str(meta['path'])
else:
img = np.frombuffer(inbuf, getattr(np, meta['dtype']))
img = img.reshape(int(meta['image_height']),
int(meta['image_width']),
int(meta['image_channels']))
meta[
'path'] = 'dummy_path' # TODO remove the dependency on this key
# put encoded image in meta for return to client
thumb = xdnn_io.makeThumbnail(
img, max(self._firstInputShape[1],
self._firstInputShape[2]))
meta['resized_shape'] = thumb.shape
retval, img_str = cv2.imencode(".jpg", thumb)
if retval:
base64_str = base64.b64encode(img_str)
meta['img'] = base64_str.decode('utf-8')
np_arr = np.zeros(tuple(self._firstInputShape[1:]),
dtype=np.float32,
order='C')
if not self._args['benchmarkmode']:
# print("PRE loading img : {}".format(img))
np_arr[:], meta[
'image_shape'] = xdnn_io.loadYoloImageBlobFromFile(
img, int(self._args['net_h']),
int(self._args['net_w']))
# np_arr has the shape (C,H,W)
# print("PRE put input : {} ".format(meta))
self.put(meta, np_arr)
except Exception as e:
print("ERROR : {}".format(str(e)))
logger.exception("pre error %s - %s" % (meta['id']), str(e))
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)
