• Hardware Platform (Jetson)
• DeepStream Version 7.1
• JetPack Version (6.2)
• Issue Type( Question)
Hello everyone,
I’m building a DeepStream Python pipeline based on the multi-input multi-output python sample with an integrated tracker. My objective is to run a GoogleNet age classifier ONNX model Model Repo on each face detected by a PeopleNet primary GIE. While the gaze estimation works, adding age classification causes a double free or corruption (out) crash when attaching custom metadata. This occurs when I attempt to attach custom metadata for age.
This is the preprocess config file:
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
[property]
enable=1
target-unique-ids=
operate-on-gie-id=1
network-input-order=0
process-on-frame=0
unique-id=7
gpu-id=0
maintain-aspect-ratio=1
symmetric-padding=1
processing-width=224
processing-height=224
scaling-buf-pool-size=8
tensor-buf-pool-size=8
network-input-shape=1;3;224;224
network-color-format=1
tensor-data-type=0
tensor-name=input
scaling-pool-memory-type=0
scaling-pool-compute-hw=1
scaling-filter=1
custom-lib-path=/opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so
custom-tensor-preparation-function=CustomTensorPreparation
[user-configs]
# Valores medios a restar (BGR)
offsets=104.0;117.0;123.0
pixel-normalization-factor=1.0;1.0;1.0
[group-0]
src-ids=0
operate-on-class-ids=2
process-on-all-objects=1
custom-input-transformation-function=CustomAsyncTransformation
input-object-min-width=50
input-object-min-height=50
input-object-max-width=1000
input-object-max-height=1000
and the model config file:
[property]
gpu-id=0
batch-size=1
network-type=1
network-mode=0
onnx-file=/opt/nvidia/deepstream/deepstream-7.1/solipsis/AdGaze/jetson/models/age_gender/age_googlenet.onnx
model-engine-file=/opt/nvidia/deepstream/deepstream-7.1/solipsis/AdGaze/jetson/models/age_gender/age_googlenet.onnx_b1_gpu0_fp32.engine
process-mode=2
gie-unique-id=5
operate-on-gie-id=1
operate-on-class-ids=2
output-tensor-meta=1
input-tensor-from-meta=1
offsets=104.0;117.0;123.0
[class-attrs-all]
threshold=0.0
I’m trying to access the metadata through this probe:
def sgie_age_src_pad_buffer_probe(pad, info, u_data):
gst_buffer = info.get_buffer()
if not gst_buffer:
return Gst.PadProbeReturn.OK
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
pyds.nvds_acquire_meta_lock(batch_meta)
while l_frame:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
l_obj = frame_meta.obj_meta_list
while l_obj:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
if obj_meta.class_id == PGIE_CLASS_ID_FACE:
l_user = obj_meta.obj_user_meta_list
age_meta_added = False
while l_user and not age_meta_added:
um = pyds.NvDsUserMeta.cast(l_user.data)
# Buscar metadatos de tensor de edad
if um.base_meta.meta_type == pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META:
infer_meta = pyds.NvDsInferTensorMeta.cast(um.user_meta_data)
# Procesar tensor de edad
age = process_age_tensor(infer_meta)
if age is not None:
# Crear metadatos de usuario
user_meta = pyds.nvds_acquire_user_meta_from_pool(batch_meta)
payload = json.dumps({
"age": int(age)})
# Crear estructura de datos personalizada
data = pyds.alloc_custom_struct(user_meta)
data.message = payload
data.structId = frame_meta.frame_num
# Configurar metadatos
user_meta.user_meta_data = data
user_meta.base_meta.meta_type = pyds.NvDsMetaType.NVDS_USER_META
# Adjuntar a los metadatos del objeto
pyds.nvds_add_user_meta_to_obj(obj_meta, user_meta)
age_meta_added = True
try:
l_user = l_user.next
except StopIteration:
break
l_obj = l_obj.next
l_frame = l_frame.next
pyds.nvds_release_meta_lock(batch_meta)
return Gst.PadProbeReturn.OK
Finally, in the On-Screen-Display probe, I access and read the custom user metadata to draw age and gaze info.
def osd_buffer_probe(pad, info, user_data):
global gaze_memory, global_tracking, perf_data
frame_number = 0
num_rects = 0
num_arrows = 0
obj_counter = {
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_BAG: 0,
PGIE_CLASS_ID_FACE: 0
}
gst_buffer = info.get_buffer()
if not gst_buffer:
return Gst.PadProbeReturn.OK
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
stream_idx = frame_meta.pad_index
current_frame = frame_meta.frame_num
stream_id = f"stream{stream_idx}"
fps = max(getattr(perf_data, 'get_fps', lambda x: 30.0)(stream_idx), 1.0)
display_metas = []
current_display_meta = pyds.nvds_acquire_display_meta_from_pool(batch_meta)
current_display_meta.num_labels = 0
display_metas.append(current_display_meta)
l_obj = frame_meta.obj_meta_list
while l_obj:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
object_id = obj_meta.object_id
rect = obj_meta.rect_params
pitch = yaw = age_value = None
found_new = False
l_umeta = obj_meta.obj_user_meta_list
while l_umeta:
try:
um = pyds.NvDsUserMeta.cast(l_umeta.data)
if um.base_meta.meta_type == pyds.NvDsMetaType.NVDS_USER_META:
custom = pyds.CustomDataStruct.cast(um.user_meta_data)
msg = pyds.get_string(custom.message)
data = json.loads(msg)
if 'pitch' in data and 'yaw' in data:
pitch, yaw = data['pitch'], data['yaw']
gaze_memory[object_id] = {'pitch': pitch, 'yaw': yaw, 'expiry': current_frame + 3}
found_new = True
draw_gaze_on_frame(batch_meta, obj_meta, pitch, yaw)
print(f"[OSD] found obj has pitch: {pitch:.2f} yaw: {yaw:.2f} during frame {current_frame}")
if 'age' in data:
age_value = int(data['age'])
except Exception:
pass
l_umeta = l_umeta.next
if not found_new:
rec = gaze_memory.get(object_id)
if rec and rec['expiry'] >= current_frame:
pitch, yaw = rec['pitch'], rec['yaw']
else:
l_obj = l_obj.next
continue
if obj_meta.class_id == PGIE_CLASS_ID_PERSON:
tracker_id = object_id
tracking = global_tracking[stream_idx].setdefault(tracker_id, {
'start_frame': current_frame,
'last_frame': current_frame,
'last_update': time.time()
})
tracking['last_frame'] = current_frame
tracking['last_update'] = time.time()
duration_frames = current_frame - tracking['start_frame']
duration_seconds = duration_frames / fps
if current_display_meta.num_labels >= 16:
current_display_meta = pyds.nvds_acquire_display_meta_from_pool(batch_meta)
current_display_meta.num_labels = 0
display_metas.append(current_display_meta)
text = current_display_meta.text_params[current_display_meta.num_labels]
text.display_text = f"Duration: {duration_seconds:.2f}s"
text.x_offset = int(rect.left)
text.y_offset = int(rect.top) + 50
text.font_params.font_size = 10
text.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
text.set_bg_clr = 1
text.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
current_display_meta.num_labels += 1
display_meta = pyds.nvds_acquire_display_meta_from_pool(batch_meta)
display_meta.num_labels = 1
display_meta.text_params[0].display_text = f"P:{pitch:.2f},Y:{yaw:.2f}"
display_meta.text_params[0].x_offset = int(rect.left)
display_meta.text_params[0].y_offset = int(rect.top) - 10
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
if age_value is not None:
age_meta = pyds.nvds_acquire_display_meta_from_pool(batch_meta)
age_meta.num_labels = 1
age_meta.text_params[0].display_text = f"Age: {age_value}"
age_meta.text_params[0].x_offset = int(rect.left)
age_meta.text_params[0].y_offset = int(rect.top) - 30
age_meta.text_params[0].font_params.font_size = 10
age_meta.text_params[0].font_params.font_color.set(1.0, 1.0, 0.5, 1.0)
age_meta.text_params[0].set_bg_clr = 1
age_meta.text_params[0].text_bg_clr.set(0.0, 0.0, 0.0, 0.7)
pyds.nvds_add_display_meta_to_frame(frame_meta, age_meta)
l_obj = l_obj.next
l_frame = l_frame.next
return Gst.PadProbeReturn.OK
I am running this through the deepstream:7.1-triton-multiarch docker container and ran two sh files that are required for python dev. The container was created under this command:
docker run -it \
--network=host \
--runtime=nvidia \
--privileged \
--device=/dev/video0 \
--device=/dev/video1 \
-e DISPLAY=$DISPLAY \
-w /opt/nvidia/deepstream/deepstream-7.1 \
-v /tmp/.X11-unix/:/tmp/.X11-unix \
[nvcr.io/nvidia/deepstream:7.1-triton-multiarch](http://nvcr.io/nvidia/deepstream:7.1-triton-multiarch)
I suspect the crash is related to either:
- Improper allocation or reuse of
user_meta_datamemory (data.message, etc.) - Conflict between gaze and age metadata management
- Not using a proper cleanup/release mechanism for custom structures
- Container access limitations to hardware.
I would really appreciate guidance or a working pattern to safely attach and display metadata from multiple sources.
Thanks in advance!