Please provide the following information when requesting support.
• Hardware: GeForce RTX 3050
• Network Type: Detectnet_v2
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file:
inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "car"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 1
gpu_index: 0
#model handler config
tlt_config{
model: "/home/tao/resnet18_trafficcamnet.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: true
overlay_linewidth: 4
classwise_bbox_handler_config{
key:"car"
value: {
confidence_model: "aggregate_cov"
output_map: "car"
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 1
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 255
B: 255
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 1
minimum_bounding_box_height: 4
}
}
}
}
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
I’m trying to run inference on trafficcamnet
in a docker container using the following command
docker run --rm --gpus all -v ./:/home/tao/ nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 \
detectnet_v2 inference -e /home/tao/resnet18_trafficcamnet_infer_spec.txt \
-i /home/tao/input_images/ -r /home/tao/input_images/ -k tlt_encode
and i’m getting the following error
INFO: Initialized model
INFO: Commencing inference
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 3, 544, 960) 0
_________________________________________________________________
model_1 (Model) [(None, 4, 34, 60), (None 11558548
=================================================================
Total params: 11,558,548
Trainable params: 11,546,900
Non-trainable params: 11,648
_________________________________________________________________
0%| | 0/7 [00:03<?, ?it/s]
INFO: list index out of range
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.8/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/postprocessor/bbox_handler.py", line 93, in render_single_image_output
bbox_list, confidence_list = _get_bbox_and_confs(class_wise_detections[key][idx],
IndexError: list index out of range
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/scripts/inference.py", line 294, in <module>
raise e
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/scripts/inference.py", line 278, in <module>
main()
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/scripts/inference.py", line 267, in main
inference_wrapper_batch(inferencer_config, bbox_handler_config,
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/scripts/inference.py", line 190, in inference_wrapper_batch
bboxer.render_outputs(classwise_detections,
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/postprocessor/bbox_handler.py", line 434, in render_outputs
pool.map(partial(render_single_image_output,
File "/usr/lib/python3.8/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/usr/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
IndexError: list index out of range
Execution status: FAIL
the inference spec file is listed below.
The model was downloaded from TrafficCamNet | NVIDIA NGC
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