🐛 [Bug] Could not implicitly convert NumPy data type: i64 to TensorRT #3295
Description
Bug Description
TensorRT engine produces error when ran on Jetson for fcn_resnet model. However, it does not produce error when ran on desktop.
Dynamo frontend is used for creating a TensorRT engine.
Error : [TRT] [E] Could not implicitly convert NumPy data type: i64 to TensorRT.
To Reproduce
Steps to reproduce the behavior:
The following are relevant code for loading and converting to a TensorRT model.
input_data = torch.randn(args.input_shape, device=DEVICE)
model = torch.hub.load("pytorch/vision", 'fcn_resnet50', pretrained=True)
model.eval().to(DEVICE)
input_data = input_data.to(torch.float16)
model = model.to(torch.float16)
exp_program = torch.export.export(model, tuple([input_data]))
model = torch_tensorrt.dynamo.compile(
exported_program=exp_program,
inputs=[input_data],
min_block_size=args.min_block_size,
optimization_level=args.optimization_level,
enabled_precisions={dtype},
# Set to True for verbose output
# NOTE: Performance Regression when rich library is available
# https://github.com/pytorch/TensorRT/issues/3215
debug=True,
# Setting it to True returns PythonTorchTensorRTModule which has different profiling approach
use_python_runtime=True,
)
for _ in range(100):
_ = model(input)Expected behavior
Environment
Build information about Torch-TensorRT can be found by turning on debug messages
Jetson Orion Developer Kit
- Torch-TensorRT Version (e.g. 1.0.0): 2.4.0a0
- PyTorch Version (e.g. 1.0):
- CPU Architecture: aarch64
- OS (e.g., Linux): Ubuntu 22.04
- How you installed PyTorch (
conda,pip,libtorch, source):nvcr.io/nvidia/pytorch:24.06-py3-igpu - Build command you used (if compiling from source):
- Are you using local sources or building from archives:
nvcr.io/nvidia/pytorch:24.06-py3-igpu - Python version: 3.10.12
- CUDA version: 12.6.68
- GPU models and configuration:
- Any other relevant information: Jetpack 6.1 L4T 36.4.0
Additional context
Here's a screenshot for relevant comparison
Desktop:
Jetson:
