🐛 [Bug] Encountered bug when using Torch-TensorRT with a pytorch segmentation model

Hi! I have been using torch_tensorrt for speedup of pytorch models and have been loving it. But sometimes i face problems while conversion.

In this case, i was using segmentation-models-pytorch(smp) library.

import segmentation_models_pytorch as smp
import torch_tensorrt as trt
import torch

model = smp.create_model(
    arch="fpn",                     # name of the architecture, e.g. 'Unet'/ 'FPN' / etc. Case INsensitive!
    encoder_name="mit_b0",
    encoder_weights="imagenet",
    in_channels=3,
    classes=3,
).eval().to('cuda')

input_data = torch.randn(1,3,224,224,requires_grad=False).to('cuda')
scripted_model = torch.jit.trace(model, input_data )
trt_model = trt.compile(
                scripted_model,
                inputs = [trt.Input((1,3,736,1280),precision = torch.float32)],
                enabled_precisions={torch.float32},truncate_long_and_double = True)

I got the following error.

WARNING:root:Given dtype that does not have direct mapping to torch (dtype.unknown), defaulting to torch.float
WARNING:torch_tensorrt._compile:Input is a torchscript module but the ir was not specified (default=dynamo), please set ir=torchscript to suppress the warning.
WARNING:root:Given dtype that does not have direct mapping to torch (dtype.unknown), defaulting to torch.float
ERROR: [Torch-TensorRT TorchScript Conversion Context] - [graphShapeAnalyzer.cpp::checkCalculationStatusSanity::1660] Error Code 2: Internal Error (Assertion !isPartialWork(p.second.symbolicRep) failed. )
ERROR: [Torch-TensorRT TorchScript Conversion Context] - [graphShapeAnalyzer.cpp::checkCalculationStatusSanity::1660] Error Code 2: Internal Error (Assertion !isPartialWork(p.second.symbolicRep) failed. )
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/mzcar/miniconda3/envs/tensorrt/lib/python3.10/site-packages/torch_tensorrt/_compile.py", line 208, in compile
    compiled_ts_module: torch.jit.ScriptModule = torchscript_compile(
  File "/home/mzcar/miniconda3/envs/tensorrt/lib/python3.10/site-packages/torch_tensorrt/ts/_compiler.py", line 156, in compile
    compiled_cpp_mod = _C.compile_graph(module._c, _parse_compile_spec(spec))
RuntimeError: [Error thrown at core/conversion/converters/converter_util.cpp:270] Expected const_layer to be true but got false

This especially occurs when im using mit_b0 as backbone. for other resnet based backbone, im getting good speedups.

Hence I am not much dependant on this backbone but i would love to know why this conversion is failing. If anyone could help in this, it would be helpful.

If anyone interested, output for pip freeze is

autocommand==2.2.2
backports.tarfile==1.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
coloredlogs==15.0.1
contourpy==1.3.0
cycler==0.12.1
efficientnet_pytorch==0.7.1
filelock==3.13.1
flatbuffers==24.3.25
fonttools==4.54.1
fsspec==2024.2.0
huggingface-hub==0.25.2
humanfriendly==10.0
idna==3.10
importlib_metadata==8.0.0
importlib_resources==6.4.0
inflect==7.3.1
jaraco.collections==5.1.0
jaraco.context==5.3.0
jaraco.functools==4.0.1
jaraco.text==3.12.1
Jinja2==3.1.3
kiwisolver==1.4.7
Mako==1.3.5
MarkupSafe==2.1.5
matplotlib==3.9.2
more-itertools==10.3.0
mpmath==1.3.0
munch==4.0.0
networkx==3.2.1
numpy==1.26.3
nvidia-cublas-cu11==11.11.3.6
nvidia-cuda-cupti-cu11==11.8.87
nvidia-cuda-nvrtc-cu11==11.8.89
nvidia-cuda-runtime-cu11==11.8.89
nvidia-cuda-runtime-cu12==12.6.77
nvidia-cudnn-cu11==9.1.0.70
nvidia-cufft-cu11==10.9.0.58
nvidia-curand-cu11==10.3.0.86
nvidia-cusolver-cu11==11.4.1.48
nvidia-cusparse-cu11==11.7.5.86
nvidia-nccl-cu11==2.20.5
nvidia-nvtx-cu11==11.8.86
onnx==1.17.0
onnx_tensorrt==10.5.0
onnxruntime-gpu==1.19.2
openvino==2023.1.0.dev20230811
openvino-telemetry==2024.1.0
packaging==24.1
pandas==2.2.3
pillow==10.2.0
platformdirs==4.3.6
pretrainedmodels==0.7.4
protobuf==5.28.2
pycuda==2024.1.2
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytools==2024.1.14
pytz==2024.2
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
safetensors==0.4.5
scipy==1.14.1
seaborn==0.13.2
segmentation-models-pytorch==0.3.4
six==1.16.0
sympy==1.13.1
tensorrt==10.1.0
tensorrt-cu12==10.5.0
tensorrt-cu12-bindings==10.1.0
tensorrt-cu12-libs==10.1.0
timm==0.9.7
tokenizers==0.20.1
tomli==2.0.1
torch==2.4.1+cu118
torch_tensorrt==2.4.0+cu118
torchvision==0.19.1+cu118
tqdm==4.66.5
transformers==4.45.2
triton==3.0.0
typeguard==4.3.0
typing_extensions==4.9.0
tzdata==2024.2
urllib3==2.2.3
zipp==3.19.2

Reproduce the reported script with the mit_b0 and resnet-based backbones, then start in torch_tensorrt/_compile.py and torch_tensorrt/ts/_compiler.py around compile and torchscript_compile. Trace the failing _C.compile_graph path against the graphShapeAnalyzer and converter_util errors; done means the mit_b0 model converts successfully or the unsupported operation and required compatibility are documented.