I tried to convert the ssdlite320_mobilenet_v3_large to onnx
the problem is, when I  export the model as onnx i got no error at all, and when checking the validity of the model using
onnx.checker.check_model(onnx_model)
and
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(dummy)}
ort_outs = ort_session.run(None, ort_inputs)
# compare ONNX Runtime and PyTorch results
np.testing.assert_allclose(np.array(torch_out[0]['scores'].detach()), np.array(ort_outs[1]), rtol=1e-03, atol=1e-05)
i got no error as well, as long as I use the same dummy tensor value.
there i thought it works well. 
so I tried to predict another randn tensor with the same size

testTorch = torch.randn(1, 3, 320, 320)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(testTorch)}
ort_outs = ort_session.run(None, ort_inputs) #this lines gives the error]

but it gives me error saying:
Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running TopK node. Name:‘TopK_2630’ Status Message: k argument [300] should not be greater than specified axis dim value [286]
if i changed the tensor to the dummy one it doesn’t give the error.
THE WHOLE CODE:
!pip3 install onnx onnxruntime
import torch
from torch import nn
import torchvision
import numpy as np
import onnx

mobilenetssd = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained= True,pretrained_backbone=True)
mobilenetssd.eval()

dummy = torch.randn(1, 3, 320, 320)
torch_out = mobilenetssd(dummy.detach())
torch.onnx.export(mobilenetssd, # model being run
    dummy, # model input (or a tuple for multiple inputs)
    "mobilenet.onnx", # where to save the model (can be a file or file-like object)
    export_params=True, # store the trained parameter weights inside the model file
    opset_version=11, # the ONNX version to export the model to
    do_constant_folding=True, # whether to execute constant folding for optimization
    input_names = ['input'], # the model's input names
    output_names = ['output'], # the model's output names
    dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes
    'output' : {0 : 'batch_size'}}
)

onnx_model = onnx.load("mobilenet.onnx")
onnx.checker.check_model(onnx_model)
ort_session = onnxruntime.InferenceSession("mobilenet.onnx")

def  to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
    
# compute ONNX Runtime output prediction
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(dummy)}
ort_outs = ort_session.run(None, ort_inputs)
# compare ONNX Runtime and PyTorch results
np.testing.assert_allclose(np.array(torch_out[0]['scores'].detach()), np.array(ort_outs[1]), rtol=1e-03, atol=1e-05)
print("Exported model has been tested with ONNXRuntime, and the result looks good!")

testTorch = torch.randn(1, 3, 320, 320)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(testTorch)}
ort_outs = ort_session.run(None, ort_inputs) #THE ERROR IS HERE WHEVER I USE ANOTHER TENSOR ATHER THAN THE DUMMY ONE