How can I implement a custom layer ? I’d appreciate it if you could help me
You can check this sample for how to add an uff-based plugin:
In general, you can find the detail implementation of GPA on the TensorFlow GitHub:
- Here are some details of my model:
1> I have a VGG model implemented by tensorflow, in which the whole connection layer is replaced by the global_max_pooling2d_1/Max layer.
I need to use tensorrt version 4.0.x to accelerate my VGG model in TX2, and some error was reported during the operation:
ERROR: UFFParser: Parser error: global_max_pooling2d_1/Max: Reduce operator not supported
Failure to parsing UFF file
Failure while parsing UFF file
ERROR: Network must have at least one input and one output
Segmentation fault (core dumped)
2> I have read the first example “sampleUffSSD” in your reply
3> I’ve converted a network with a custom layer into a UFF file.
And the highest version on TX2 is version4, so I can only use tensorrt version4 instead of version5
- My problem is that:
1> Can all custom layers be implemented using IpluginV2Ext and IpluginCreator in version4.0.x by TX2?
2> Can I implement my custom layer follow the steps by the following steps in official document?
https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#extending --> 4.1.2 --> four steps in official document
I will very appreciate it .
Is there any special reason to use TensorRT 4.0?
We have release a better plugin interface in TensorRT5.0.
It’s highly recommended to use v5.0 instead.
Becuase we’re going to use TX2. The highest version of this is TensorRT4.0.
Can I use version5.0 on TX2?
Sure, please use JetPack 4.2 to install the TensorRT5.0.