I’m currently trying to make some object detection using TRT but I have this recurrent error:
2019-03-14 10:11:56.751652: F tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc:84] input tensor batch larger than max_batch_size: 1
Aborted (core dumped)
To freeze my graph I’m using a script (included file) based on the following repository:
It works perfectly but when I want to do some detection with a home-made code (where there is no mention of batch-size) on either video or single images I’m facing the issue.
To be noted: when I use the script to do detection (the last part of tutorial in the github) I can make it work but without displaying the bbox cause my network isn’t made like his Yolov3. I don’t get why I don’t have the batch-size error with my script.
The error indicates that your max_batch_size is set to 1 but the input size is larger than it.
Please try to increase the max_batch_size value to see if helps:
trt_graph = trt.create_inference_graph(
input_graph_def=frozen_graph,# frozen model
<b>max_batch_size=2,# specify your max batch size</b>
max_workspace_size_bytes=2*(10**9),# specify the max workspace
precision_mode="FP32") # precision, can be "FP32" (32 floating point precision) or "FP16"
yes the output log changes with the batch size I used earlier to train.
How can I print out the input buffer size exactly ?
I had a colleague this week who took a look at it and he had to put max_batch_size = 1000 to make it work…
But we now face another issue we are currently dealing dealing with cause it’s relative to our script not the Jetson.
I have though a question concerning this output log when I launch my inference script:
Using TensorFlow backend.
GPU is available!
[_DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456), _DeviceAttributes(/job:localhost/replica:0/task:0/device:GPU:0, GPU, 3588841472)]
What does it mean ? I saw on other forums guys with the same output but with GPU:1 or even 2 is it the number of GPU available or the index of the GPU ?
I’m pretty sure it uses the GPU to do the inference there is no reason why it wouldn’t but I prefer to ask to be certain.