• Hardware Platform: Jetson
• DeepStream Version: 7.1-triton
• Issue Type: SGIE buffer None
Hi everyone,
I’m building a DeepStream 7.1 Python pipeline on Jetson using JetPack 6.2. It’s based on the multi-input multi-output Python sample, and I’m using a custom SGIE model for 2.5D pose estimation (like OpenPose 25 keypoints + Z + confidence). The model outputs a single layer named pose25d with shape [1, 34, 4].
In the C++ code, everything works fine. I’m able to run tensor_meta->out_buf_ptrs_host, visualize the pose keypoints, and use them as expected.
im using the same config infer files:
However, i wanted the same in python..in the Python app, when I try to access the layer data like this:
def parse_pose_from_meta(frame_meta, obj_meta):
##PGIE
class_id = obj_meta.class_id
confidence = obj_meta.confidence
if class_id == 0:
left = int(obj_meta.rect_params.left)
top = int(obj_meta.rect_params.top)
width = int(obj_meta.rect_params.width)
height = int(obj_meta.rect_params.height)
print(left, top, width, height) # outputs 1283 190 270 679 # ✅ Works
##SGIE
# # # print(dir(obj_meta))
# ls = ['base_meta', 'cast', 'class_id', 'classifier_meta_list', 'confidence', 'detector_bbox_info', 'mask_params',
# 'misc_obj_info', 'obj_label', 'obj_user_meta_list', 'object_id', 'parent', 'rect_params', 'reserved',
# 'text_params', 'tracker_bbox_info', 'tracker_confidence', 'unique_component_id']
# for i in ls:
# print(f"Attribute {i}: {getattr(obj_meta, i)}")
user_meta_list = obj_meta.obj_user_meta_list
while user_meta_list is not None:
user_meta = pyds.NvDsUserMeta.cast(user_meta_list.data)
if user_meta and user_meta.base_meta.meta_type == pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META:
tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
# # print(dir(tensor_meta))
# ls2 = ['cast', 'gpu_id', 'network_info', 'num_output_layers', 'out_buf_ptrs_dev', 'out_buf_ptrs_host', 'output_layers_info', 'priv_data']
# for i in ls2:
# print(f"Attribute {i}: {getattr(tensor_meta, i)}")
ptrs = tensor_meta.out_buf_ptrs_dev
print(dir(ptrs))
# print(dir(ptrs.cast))
num_layers = tensor_meta.num_output_layers
print(f"Found tensor meta with {num_layers} layers")
for i in range(num_layers):
layer_info = tensor_meta.output_layers_info(i)
layer_name = layer_info.layerName
print(f"Layer {i}: {layer_name}")
if layer_info.buffer is not None:
dims = layer_info.inferDims
print(f"{layer_name} shape: {[dims.numElements()]} ({dims.d[0]}x{dims.d[1]}x{dims.d[2]})")
else:
print(f"{layer_name} buffer is None")
user_meta_list = user_meta_list.next
# Always prints None...
it outputs all the time, all the way buffer None:
Found tensor meta with 4 layers
Layer 0: pose2d
pose2d buffer is None
Layer 1: pose2d_org_img
pose2d_org_img buffer is None
Layer 2: pose25d
pose25d buffer is None
Layer 3: pose3d
pose3d buffer is None
layer_info.buffer is always None, even though in the C++ version, the buffer has valid values.
Things I’ve Checked:
- SGIE model works in DeepStream C++ with custom parser
- Tensor metadata (
tensor_meta.num_output_layers) is correct - Layer name is correctly matched as
pose25d - Model is ONNX and engine is generated correctly (verified with trtexec and in C++)
out_buf_ptrs_hostin C++ has valid floats for all keypoints
My Question:
Is there an additional step or setting in the DeepStream Python pipeline to ensure layer_info.buffer is filled correctly for SGIE outputs?
Or should I be accessing tensor_meta.out_buf_ptrs_host directly from Python using ctypes or another method?
i want to access the SGIE output tensor values in pyds
Any help would be appreciated. I can also share my config file and parser if needed.
Thanks in advance!