Hey :)
I successfully implemented my own unet model with image segmentation by changing a few arguments in /isaac_ros_image_segmentation repo and converting my own model.plan.
Now I’m trying to insert my own model trained with yolo v4 into the isaac_ros_object_detection repo
Mainly I want to change these layers :
OUTPUT_LAYERS=“output_cov/Sigmoid,output_bbox/BiasAdd”
into my own BatchedNMS layer.
is it possible to decode it in the same manner as the node decodes the original layers?
Another question:
in my try to insert a custom model I got these errors:
[component_container-1] E1116 13:19:44.344062 34428 model_repository_manager.cc:1355] failed to load ‘yolov4_resnet18_epoch_200’ version 1: Invalid argument: unexpected datatype TYPE_FP32 for inference input ‘Input’, expecting TYPE_FP16 for yolov4_resnet18_epoch_200
[component_container-1] ERROR: Triton: failed to load model yolov4_resnet18_epoch_200, triton_err_str:Invalid argument, err_msg:load failed for model ‘yolov4_resnet18_epoch_200’: version 1: Invalid argument: unexpected datatype TYPE_FP32 for inference input ‘Input’, expecting TYPE_FP16 for yolov4_resnet18_epoch_200;
thats weird, the datatype TYPE_FP32 is not mentioned at all in my config.ptxt:
name: "yolov4_resnet18_epoch_200"
platform: "tensorrt_plan"
max_batch_size: 16
input [
{
name: "Input"
data_type: TYPE_FP16
format: FORMAT_NCHW
dims: [ 3, 384, 1248 ]
}
]
output [
{
name: "BatchedNMS"
data_type: TYPE_FP16
dims: [ 3, 384, 1248]
}
]
dynamic_batching { }
version_policy: {
specific {
versions: [ 1 ]
}
}