I followed the instructions at section 3.5.5 of https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html#using-metagraph-checkpoint to run image_classification.py and check the results of TensorRT graph in Tensorboard.
TensorBoard seems not to read/display the fused graph as the instruction explains.
Here are the steps:
- check out https://github.com/tensorflow/tensorrt
- convert the data dataset into TFRecord
- python image_classification.py --model resnet_v1_50 --data_dir ~/dataset/faceTFR --use_trt --precision fp16 --mode validation
- tensorboard --logdir=./data --port 6006
- open tensorboard in browser and check ‘rasnet_model’ module. Hope to see the nodes reduced from 459 to 4.
Here are issues:
- there is no ‘model_dir’ created by ‘image_classification’. So there is no logs when running Tensorboard on ‘model_dir’.
- where running ‘TensorBoard’ on ‘data’, Tensorboard shows the original model as expected.
Is there anything overlooked in the above procedure?