Hi,
I am trying to infer detectenet_v2_vresnet10 on my own dataset. I downloaded the network from ngc. Contrary to peoplenet for instance, this file is a hdf5 file. Can I use it directly on nvinfer?
I get the following error
020-06-05 19:01:31,272 [INFO] iva.detectnet_v2.scripts.inference: Overlain images will be saved in the output path.
2020-06-05 19:01:31,272 [INFO] iva.detectnet_v2.inferencer.build_inferencer: Constructing inferencer
2020-06-05 19:01:31,665 [INFO] iva.detectnet_v2.inferencer.tlt_inferencer: Loading model from /pretrained_models/tlt_pretrained_detectnet_v2_vresnet10/resnet10.hdf5:
Traceback (most recent call last):
File "/usr/local/bin/tlt-infer", line 8, in <module>
sys.exit(main())
File "./common/magnet_infer.py", line 56, in main
File "./detectnet_v2/scripts/inference.py", line 194, in main
File "./detectnet_v2/scripts/inference.py", line 117, in inference_wrapper_batch
File "./detectnet_v2/inferencer/tlt_inferencer.py", line 110, in network_init
File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 358, in get_layer
raise ValueError('No such layer: ' + name)
ValueError: No such layer: output_cov
My config file is the following
inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "Car"
target_classes: "Bycicle"
target_classes: "Person"
target_classes: "RoadSign"
# Inference dimensions.
image_width: 640
image_height: 368
# Must match what the model was trained for.
image_channels: 3
batch_size: 4
gpu_index: 0
# model handler config
tlt_config{
model: "/pretrained_models/tlt_pretrained_detectnet_v2_vresnet10/resnet10.hdf5"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"Car"
value: {
confidence_model: "aggregate_cov"
output_map: "person"
confidence_threshold: 0.2
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"Bycicle"
value: {
confidence_model: "aggregate_cov"
output_map: "person"
confidence_threshold: 0.2
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"Person"
value: {
confidence_model: "aggregate_cov"
output_map: "person"
confidence_threshold: 0.2
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"Roadsign"
value: {
confidence_model: "aggregate_cov"
output_map: "person"
confidence_threshold: 0.2
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
}