Hello,
I’m trying to run the TLT Gazenet example.
I followed the Gazenet notebook instructions, I think it worked, I can train the model. However I was unable to get good results when running the inference.
I was using the pretrained tlt model and the provided MPIIFaceGaze subset. These are what I got:
I have tried different epoch_num, learning rate, but still can’t get good results.
Is it suppose to be like this, or did I miss anything?
Thank you so much.
Did you follow the jupyter notebook without any change? More, how about inference result with other images?
Are above two images your own images or part of the MPIIFaceGaze dataset?
They are part of the MPIIFaceGaze dataset.
OK, I will try to reproduce your result. Thanks for the info.
Hello,
Do you have any update on this? Thank you
Sorry, I have not went through the gaznet training with jupyter notebook yet.
Could you please try to download the pretrained trainable version of gazenet model and run inference ( In step 7 Visualize Inference) again?
wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_gazenet/versions/trainable_v1.0/files/model.tlt
Or download the deployable version of gazenet model and run on the inference pipeline.
Hello,
If I understand correctly, do you mean skip the training and evaluation steps? I just did that and got an error ‘Please provide a valid gazenet file path for evaluation.’ Or is there anything need to be changed in the spec yaml file? Thank you.
Traceback (most recent call last):
File “/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/mitgazenet/scripts/inference.py”, line 121, in
File “/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/mitgazenet/scripts/inference.py”, line 117, in main
File “/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/mitgazenet/inferencer/gazenet_inferencer.py”, line 97, in build_model
ValueError: Please provide a valid gazenet file path for evaluation.
Traceback (most recent call last):
File “/usr/local/bin/gazenet”, line 8, in
sys.exit(main())
File “/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/mitgazenet/entrypoint/gazenet.py”, line 13, in main
File “/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/driveix/build_wheel.runfiles/ai_infra/driveix/common/entrypoint/entrypoint.py”, line 300, in launch_job
AssertionError: Process run failed.
2021-08-18 11:01:34,342 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
Yes, skip the training and evaluation steps. Please use the pretrained trainable tlt model and directly run inference with it.
I try on my side, the inference result looks ok.
Please refer to below command in notebook.
#add by morganh
!tlt gazenet inference -e $SPECS_DIR/gazenet_tlt_pretrain.yaml
-i $DATA_DOWNLOAD_DIR/MPIIFaceGaze/sample-dataset/inference-set
-m /workspace/demo_3.0/public_data/tlt_cv_samples_v1.1.0/gazenet/pretrain_models/tlt_gazenet_vtrainable_v1.0/model.tlt
-o $USER_EXPERIMENT_DIR/experiment_result/exp1
-k $KEY
Hello,
I tried it again with the command:
!tlt gazenet inference -e $SPECS_DIR/gazenet_tlt_pretrain.yaml
-i $DATA_DOWNLOAD_DIR/MPIIFaceGaze/sample-dataset/inference-set
-m $LOCAL_EXPERIMENT_DIR/pretrain_models/tlt_gazenet_vtrainable_v1.0/model.tlt
-o $USER_EXPERIMENT_DIR/experiment_result/exp1
-k $KEY
But I still got an error ‘ValueError: Please provide a valid gazenet file path for evaluation.’
Please make sure $LOCAL_EXPERIMENT_DIR/pretrain_models/tlt_gazenet_vtrainable_v1.0/model.tlt is available.
You can run below to verify.
$ tlt unet run ls $LOCAL_EXPERIMENT_DIR/pretrain_models/tlt_gazenet_vtrainable_v1.0/model.tlt
Or you can directly run following command in terminal instead of notebook, to login in the docker.
$tlt unet run /bin/bash
Then , run above command to debug.
Hello,
I run the first command it gives me an error no such file or directory. I thought I followed the notebook to download it, can you tell me how to fix it? Thanks for your patience.
I tried again with:
!tlt gazenet inference -e $SPECS_DIR/gazenet_tlt_pretrain.yaml
-i $DATA_DOWNLOAD_DIR/MPIIFaceGaze/sample-dataset/inference-set
-m $USER_EXPERIMENT_DIR/pretrain_models/tlt_gazenet_vtrainable_v1.0/model.tlt
-o $USER_EXPERIMENT_DIR/experiment_result/exp1
-k $KEY
This time it works, thank you so much!
