Dynamic Input. IShuffleLayer applied to shape tensor must have 0 or 1 reshape dimensions


Dynamic Input. IShuffleLayer applied to shape tensor must have 0 or 1 reshape dimensions


TensorRT Version: 8.5.2-1+cuda11.8
GPU Type: Tesla K80
Nvidia Driver Version: 470.161.03
CUDA Version: 11.7
CUDNN Version:
Operating System + Version: Ubuntu 20.04.5 LTS x86_64
Python Version (if applicable): 3.9
TensorFlow Version (if applicable):
PyTorch Version (if applicable): 1.10.2
Baremetal or Container (if container which image + tag):

Relevant Files

Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. (Github repo, Google Drive, Dropbox, etc.)

Steps To Reproduce


import torch
import torchvision
import torch.nn.functional as F

Set the dummy input to input size

dummy_input = torch.randn(1, 3, 224, 224)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

Providing input and output names sets the display names for values within the model’s graph.

input_names = [ “input” ]
output_names = [ “output” ]

class TraceWrapper(torch.nn.Module):
def init(self, model):
self.model = model

def forward(self, x):
    out = self.model(x)

boxes = out[0][“boxes”].reshape(-1)

    labels = out[0]["labels"].reshape(-1)

scores = out[0][“scores”].reshape(-1)

    return labels
    # return torch.rand(1,2)

model = TraceWrapper(model)

prediction = model(torch.rand(1,3,224,224))


The call to torch.onnx.export runs the model once to trace its execution and then exports the traced model to the specified file.

torch.onnx.export(model, dummy_input, “model.onnx”, verbose=True,
input_names=input_names, output_names=output_names, opset_version=13)

Import tensorrt module to access the Python API

import tensorrt as trt

Create a logger

logger = trt.Logger(trt.Logger.WARNING)

Create a builder

builder = trt.Builder(logger)

Create a build configuration specifying how TensorRT should optimize the model

config = builder.create_builder_config()
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20) # 1 MiB

Create a network definition:

network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))

Create an ONNX parser to populate the network as follows:

parser = trt.OnnxParser(network, logger)

Read the model file and process any errors:

success = parser.parse_from_file(“folded.onnx”)
for idx in range(parser.num_errors):
if not success:
pass # Error handling code here

Engine can be built and serialized with

serialized_engine = builder.build_serialized_network(network, config)

Save the engine to a file for future use

with open(“sample.engine2”, “wb”) as f:


Hope following similar post will help you.

Thank you.