--- a
+++ b/tools/deployment/pytorch2onnx.py
@@ -0,0 +1,170 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.runner import load_checkpoint
+
+from mmaction.models import build_model
+
+try:
+    import onnx
+    import onnxruntime as rt
+except ImportError as e:
+    raise ImportError(f'Please install onnx and onnxruntime first. {e}')
+
+try:
+    from mmcv.onnx.symbolic import register_extra_symbolics
+except ModuleNotFoundError:
+    raise NotImplementedError('please update mmcv to version>=1.0.4')
+
+
+def _convert_batchnorm(module):
+    """Convert the syncBNs into normal BN3ds."""
+    module_output = module
+    if isinstance(module, torch.nn.SyncBatchNorm):
+        module_output = torch.nn.BatchNorm3d(module.num_features, module.eps,
+                                             module.momentum, module.affine,
+                                             module.track_running_stats)
+        if module.affine:
+            module_output.weight.data = module.weight.data.clone().detach()
+            module_output.bias.data = module.bias.data.clone().detach()
+            # keep requires_grad unchanged
+            module_output.weight.requires_grad = module.weight.requires_grad
+            module_output.bias.requires_grad = module.bias.requires_grad
+        module_output.running_mean = module.running_mean
+        module_output.running_var = module.running_var
+        module_output.num_batches_tracked = module.num_batches_tracked
+    for name, child in module.named_children():
+        module_output.add_module(name, _convert_batchnorm(child))
+    del module
+    return module_output
+
+
+def pytorch2onnx(model,
+                 input_shape,
+                 opset_version=11,
+                 show=False,
+                 output_file='tmp.onnx',
+                 verify=False):
+    """Convert pytorch model to onnx model.
+
+    Args:
+        model (:obj:`nn.Module`): The pytorch model to be exported.
+        input_shape (tuple[int]): The input tensor shape of the model.
+        opset_version (int): Opset version of onnx used. Default: 11.
+        show (bool): Determines whether to print the onnx model architecture.
+            Default: False.
+        output_file (str): Output onnx model name. Default: 'tmp.onnx'.
+        verify (bool): Determines whether to verify the onnx model.
+            Default: False.
+    """
+    model.cpu().eval()
+
+    input_tensor = torch.randn(input_shape)
+
+    register_extra_symbolics(opset_version)
+    torch.onnx.export(
+        model,
+        input_tensor,
+        output_file,
+        export_params=True,
+        keep_initializers_as_inputs=True,
+        verbose=show,
+        opset_version=opset_version)
+
+    print(f'Successfully exported ONNX model: {output_file}')
+    if verify:
+        # check by onnx
+        onnx_model = onnx.load(output_file)
+        onnx.checker.check_model(onnx_model)
+
+        # check the numerical value
+        # get pytorch output
+        pytorch_result = model(input_tensor)[0].detach().numpy()
+
+        # get onnx output
+        input_all = [node.name for node in onnx_model.graph.input]
+        input_initializer = [
+            node.name for node in onnx_model.graph.initializer
+        ]
+        net_feed_input = list(set(input_all) - set(input_initializer))
+        assert len(net_feed_input) == 1
+        sess = rt.InferenceSession(output_file)
+        onnx_result = sess.run(
+            None, {net_feed_input[0]: input_tensor.detach().numpy()})[0]
+        # only compare part of results
+        random_class = np.random.randint(pytorch_result.shape[1])
+        assert np.allclose(
+            pytorch_result[:, random_class], onnx_result[:, random_class]
+        ), 'The outputs are different between Pytorch and ONNX'
+        print('The numerical values are same between Pytorch and ONNX')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert MMAction2 models to ONNX')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--show', action='store_true', help='show onnx graph')
+    parser.add_argument('--output-file', type=str, default='tmp.onnx')
+    parser.add_argument('--opset-version', type=int, default=11)
+    parser.add_argument(
+        '--verify',
+        action='store_true',
+        help='verify the onnx model output against pytorch output')
+    parser.add_argument(
+        '--is-localizer',
+        action='store_true',
+        help='whether it is a localizer')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[1, 3, 8, 224, 224],
+        help='input video size')
+    parser.add_argument(
+        '--softmax',
+        action='store_true',
+        help='wheter to add softmax layer at the end of recognizers')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    assert args.opset_version == 11, 'MMAction2 only supports opset 11 now'
+
+    cfg = mmcv.Config.fromfile(args.config)
+    # import modules from string list.
+
+    if not args.is_localizer:
+        cfg.model.backbone.pretrained = None
+
+    # build the model
+    model = build_model(
+        cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
+    model = _convert_batchnorm(model)
+
+    # onnx.export does not support kwargs
+    if hasattr(model, 'forward_dummy'):
+        from functools import partial
+        model.forward = partial(model.forward_dummy, softmax=args.softmax)
+    elif hasattr(model, '_forward') and args.is_localizer:
+        model.forward = model._forward
+    else:
+        raise NotImplementedError(
+            'Please implement the forward method for exporting.')
+
+    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
+
+    # convert model to onnx file
+    pytorch2onnx(
+        model,
+        args.shape,
+        opset_version=args.opset_version,
+        show=args.show,
+        output_file=args.output_file,
+        verify=args.verify)