--- a
+++ b/tests/test_models/test_neck.py
@@ -0,0 +1,87 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import pytest
+import torch
+
+from mmaction.models import TPN
+from .base import generate_backbone_demo_inputs
+
+
+def test_tpn():
+    """Test TPN backbone."""
+
+    tpn_cfg = dict(
+        in_channels=(1024, 2048),
+        out_channels=1024,
+        spatial_modulation_cfg=dict(
+            in_channels=(1024, 2048), out_channels=2048),
+        temporal_modulation_cfg=dict(downsample_scales=(8, 8)),
+        upsample_cfg=dict(scale_factor=(1, 1, 1)),
+        downsample_cfg=dict(downsample_scale=(1, 1, 1)),
+        level_fusion_cfg=dict(
+            in_channels=(1024, 1024),
+            mid_channels=(1024, 1024),
+            out_channels=2048,
+            downsample_scales=((1, 1, 1), (1, 1, 1))),
+        aux_head_cfg=dict(out_channels=400, loss_weight=0.5))
+
+    with pytest.raises(AssertionError):
+        tpn_cfg_ = copy.deepcopy(tpn_cfg)
+        tpn_cfg_['in_channels'] = list(tpn_cfg_['in_channels'])
+        TPN(**tpn_cfg_)
+
+    with pytest.raises(AssertionError):
+        tpn_cfg_ = copy.deepcopy(tpn_cfg)
+        tpn_cfg_['out_channels'] = float(tpn_cfg_['out_channels'])
+        TPN(**tpn_cfg_)
+
+    with pytest.raises(AssertionError):
+        tpn_cfg_ = copy.deepcopy(tpn_cfg)
+        tpn_cfg_['downsample_cfg']['downsample_position'] = 'unsupport'
+        TPN(**tpn_cfg_)
+
+    for k in tpn_cfg:
+        if not k.endswith('_cfg'):
+            continue
+        tpn_cfg_ = copy.deepcopy(tpn_cfg)
+        tpn_cfg_[k] = list()
+        with pytest.raises(AssertionError):
+            TPN(**tpn_cfg_)
+
+    with pytest.raises(ValueError):
+        tpn_cfg_ = copy.deepcopy(tpn_cfg)
+        tpn_cfg_['flow_type'] = 'unsupport'
+        TPN(**tpn_cfg_)
+
+    target_shape = (32, 1)
+    target = generate_backbone_demo_inputs(target_shape).long().squeeze()
+    x0_shape = (32, 1024, 1, 4, 4)
+    x1_shape = (32, 2048, 1, 2, 2)
+    x0 = generate_backbone_demo_inputs(x0_shape)
+    x1 = generate_backbone_demo_inputs(x1_shape)
+    x = [x0, x1]
+
+    # ResNetTPN with 'cascade' flow_type
+    tpn_cfg_ = copy.deepcopy(tpn_cfg)
+    tpn_cascade = TPN(**tpn_cfg_)
+    feat, loss_aux = tpn_cascade(x, target)
+    assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
+    assert len(loss_aux) == 1
+
+    # ResNetTPN with 'parallel' flow_type
+    tpn_cfg_ = copy.deepcopy(tpn_cfg)
+    tpn_parallel = TPN(flow_type='parallel', **tpn_cfg_)
+    feat, loss_aux = tpn_parallel(x, target)
+    assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
+    assert len(loss_aux) == 1
+
+    # ResNetTPN with 'cascade' flow_type and target is None
+    feat, loss_aux = tpn_cascade(x, None)
+    assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
+    assert len(loss_aux) == 0
+
+    # ResNetTPN with 'parallel' flow_type and target is None
+    feat, loss_aux = tpn_parallel(x, None)
+    assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
+    assert len(loss_aux) == 0