Onnx resnet50 export

vision
1

I made very simple python script which loads torchvision ResNet50 model and tries to export to onnx file in two ways (torch.onnx.export and torch.onnx.dynamo_export )

import torch
import torch.onnx

import torchvision

torch_model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2( weights='DEFAULT')
torch_model.eval()
torch_input = torch.randn(1, 3, 32, 32)

is_dynamo_export = False

if (is_dynamo_export):
    onnx_program = torch.onnx.dynamo_export(torch_model, torch_input)
    onnx_program.save("onnx_dynamo_export_ResNET50.onnx")        
else:
    torch.onnx.export(torch_model,               # model being run
                      torch_input,                         # model input (or a tuple for multiple inputs)
                      "onnx_export_ResNET50.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=10,          # 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'}})

The errors were appeared:

torch\onnx\_internal\exporter.py", line 1439, in dynamo_export
  raise OnnxExporterError(

torch.onnx.OnnxExporterError: Failed to export the model to ONNX. Generating SARIF

torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of Pad in opset 9. The sizes of the padding must be constant. Please try opset version 11. [Caused by the value '535 defined in (%535 : int[] = prim::ListConstruct(%405, %534, %405, %533, %405, %532), scope: torchvision.models.detection.faster_rcnn.FasterRCNN::

Both methods works good with extremally simple models such as

class MyModel(nn.Module):

    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x