Please provide complete information as applicable to your setup.
**• Hardware Platform (Jetson / GPU)**jetson • DeepStream Version 6.0.1 • JetPack Version (valid for Jetson only) 4.6 • TensorRT Version 8.0.1.6
Hello,
I have a segmentation UNET model that I have trained with TAO on VOC datasets 22 classes,
this is my TAO train space: unet_train_resnet_unet_isbi.txt (3.0 KB)
The TAO evaluation process went good, providing a Segmentation accuracy of 94% on validation data. and inference right.
I have converted the model using the following command for the tlt-converter:
/data/tao-converter -k nvidia_tlt -c isbi_cal.bin -e trt_int8.engine -i nchw -t int8 -p input_1:0,1x3x640x640,4x3x640x640,16x3x640x640 model.int8.etlt
Can you please explain to me why the reasoning is correct under TAO tool and fails under deepstream？
How can I get the correct output under deepstream？
Looking forward to your reply thank you！
Thanks for your reply, I’m not very familiar with deepstream, can you be more specific?
how can get logs?
./ds-tao-segmentation -c config_infer.txt -i sample_720p.mjpeg -d
There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.
Thanks
please refer to osd_sink_pad_buffer_probe in deepstream-test1, you can get classes from obj_meta->class_id.
There are 22 class, but there is only one. I replace my model with the official model at /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-segmentation-test. The number of classes is consistent with the official model
there are two models in deepstream-segmentation-test, which model are you testing? please refer to readme of deepstream-segmentation-test,
for semantic segmentation, it needs semantic model which can get 4 classes map,
including backgroud, car, person, bicycle.
“nvsegvidsual” plugin chooses 4 different colors for them and to display.
I mean that I can get correct results with the official deepstream-segment-test semantic segmentation model and the number of classes is 4, but the number of classes in my own data training model (tao training and class label is 22) is inconsistent. The actual deepstream operation found that the class is 1；
The inference result in tao is correct, but the deepstream result is wrong
if segmentation_output.classes is 1, it should be preprocess and model 's issue, please make sure label file is right, please refer to link to convert model, PeopleSemSegnet | NVIDIA NGC
if still not work, can you provide the TAO model and label file?
after testing in deepstream, seemed the model have one output class, need to verify the model has 22 output classes.
here is the logs:
0:04:14.849637060 42732 0xaaaad71910f0 INFO nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2126> [UID = 1]: serialize cuda engine to file: /home/nvidia/rec/model.fp16.etlt_b1_gpu0_fp16.engine successfully
INFO: [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT input_1:0 3x640x640
1 OUTPUT kINT32 argmax_1 640x640x1 pgie_peopleSemSegVanillaUnet_tao_config.txt (2.5 KB)
This is the question I want to ask. I use the TAO training model and use the TAO convert tool to convert it into an engine file for infer and I can get good results. but only one class is found when I deploy it to the deep stream.
The following figure shows the inference result of using the. engine model file on tao:
used tao-converter to generate engine, and used trtexec to test, the output classes still is 1, here is the test logs:
[09/22/2022-09:26:18] [I] Created output binding for argmax_1 with dimensions 1x640x640x1,
here is the command:
tao-converter -k nvidia_tlt -e svae.engine -i nchw -t fp16 -p input_1:0,1x3x640x640,4x3x640x640,16x3x640x640 model.fp16.etlt
trtexec --loadEngine=saved.engine --fp16
Is your official deep stream segment test model trained with TAO? It seems to be trained with caffe. Have you tested the segmentation model trained with TAO and successfully deployed it to deep stream?
