Convert frcnn model to onnx

[nyanmn]

I am converting pth model to onnx without using configuration discussed here.

My code is as follows.

import argparse
import io
import numpy as np
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.onnx
from torchvision import models
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmaction.models import build_model
from mmcv import Config
from mmaction.utils import (build_ddp, build_dp, default_device)


def parse_args():
    parser = argparse.ArgumentParser(description='MMAction2')
    parser.add_argument(
        '--conf',
        default='/workspace/mmaction2/work_dirs/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py',
        help='This is config file')  
    parser.add_argument(
        '--checkpoint',
        default='/workspace/mmaction2/work_dirs/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/latest.pth',
        help='This is checkpoint file')  
    args = parser.parse_args()
    return args


def turn_off_pretrained(cfg):
    # recursively find all pretrained in the model config,
    # and set them None to avoid redundant pretrain steps for testing
    if 'pretrained' in cfg:
        cfg.pretrained = None

    # recursively turn off pretrained value
    for sub_cfg in cfg.values():
        if isinstance(sub_cfg, dict):
            turn_off_pretrained(sub_cfg)

 
def convert(args): 
    cfg = Config.fromfile(args.conf)
    turn_off_pretrained(cfg.model)
    # build the model and load checkpoint
    model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    model = build_dp(model, default_device, default_args=dict(device_ids=cfg.gpu_ids))
    # set the model to inference mode 
    model.eval() 
    #device = torch.device("cpu" if args.cpu else "cuda")
    #model = model.to(device)

    # Let's create a dummy input tensor  
    dummy_input = torch.randn(1, 3, 1080, 1920).to(default_device) 

    # Export the model   
    torch.onnx.export(model.module,         # model being run 
         dummy_input,       # model input (or a tuple for multiple inputs) 
         "faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.onnx",       # where to save the model  
         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'}}) 
    print(" ") 
    print('Model has been converted to ONNX') 

if __name__ == '__main__':
   args = parse_args()
   convert(args )

In conversion, the errors are

load checkpoint from local path: /workspace/mmaction2/work_dirs/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/latest.pth
Traceback (most recent call last):
  File "convert_to_onnx_1.py", line 72, in <module>
    convert(args )
  File "convert_to_onnx_1.py", line 57, in convert
    torch.onnx.export(model.module,         # model being run
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/__init__.py", line 350, in export
    return utils.export(
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 163, in export
    _export(
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 1074, in _export
    graph, params_dict, torch_out = _model_to_graph(
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 727, in _model_to_graph
    graph, params, torch_out, module = _create_jit_graph(model, args)
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 602, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 517, in _trace_and_get_graph_from_model
    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 1175, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 127, in forward
    graph, out = torch._C._create_graph_by_tracing(
  File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1118, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/workspace/mmcv/mmcv/runner/fp16_utils.py", line 116, in new_func
    return old_func(*args, **kwargs)
  File "/workspace/mmdetection/mmdet/models/detectors/base.py", line 168, in forward
    assert len(img_metas) == 1
  File "/usr/local/lib/python3.8/dist-packages/torch/_tensor.py", line 705, in __len__
    raise TypeError("len() of a 0-d tensor")
TypeError: len() of a 0-d tensor

What could be wrong?