On my side, it works well.
My step:
# tlt-converter pretrained_models/public/model.etlt -k nvidia_tlt -p input_face_images:0,1x1x80x80,1x1x80x80,1x1x80x80 -b 1 -t fp32 -e fpenet_b1_fp32.trt
[WARNING] onnx2trt_utils.cpp:220: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[INFO] Detected input dimensions from the model: (-1, 1, 80, 80)
[INFO] Model has dynamic shape. Setting up optimization profiles.
[INFO] Using optimization profile min shape: (1, 1, 80, 80) for input: input_face_images:0
[INFO] Using optimization profile opt shape: (1, 1, 80, 80) for input: input_face_images:0
[INFO] Using optimization profile max shape: (1, 1, 80, 80) for input: input_face_images:0
[INFO] Detected 1 inputs and 2 output network tensors.
Modify your code to
fpenet_obj = FpeNet(‘fpenet_b1_fp32.trt’)
# python3 test.py --input test.png