--- a
+++ b/mmaction/models/heads/tsm_head.py
@@ -0,0 +1,112 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import normal_init
+
+from ..builder import HEADS
+from .base import AvgConsensus, BaseHead
+
+
+@HEADS.register_module()
+class TSMHead(BaseHead):
+    """Class head for TSM.
+
+    Args:
+        num_classes (int): Number of classes to be classified.
+        in_channels (int): Number of channels in input feature.
+        num_segments (int): Number of frame segments. Default: 8.
+        loss_cls (dict): Config for building loss.
+            Default: dict(type='CrossEntropyLoss')
+        spatial_type (str): Pooling type in spatial dimension. Default: 'avg'.
+        consensus (dict): Consensus config dict.
+        dropout_ratio (float): Probability of dropout layer. Default: 0.4.
+        init_std (float): Std value for Initiation. Default: 0.01.
+        is_shift (bool): Indicating whether the feature is shifted.
+            Default: True.
+        temporal_pool (bool): Indicating whether feature is temporal pooled.
+            Default: False.
+        kwargs (dict, optional): Any keyword argument to be used to initialize
+            the head.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_segments=8,
+                 loss_cls=dict(type='CrossEntropyLoss'),
+                 spatial_type='avg',
+                 consensus=dict(type='AvgConsensus', dim=1),
+                 dropout_ratio=0.8,
+                 init_std=0.001,
+                 is_shift=True,
+                 temporal_pool=False,
+                 **kwargs):
+        super().__init__(num_classes, in_channels, loss_cls, **kwargs)
+
+        self.spatial_type = spatial_type
+        self.dropout_ratio = dropout_ratio
+        self.num_segments = num_segments
+        self.init_std = init_std
+        self.is_shift = is_shift
+        self.temporal_pool = temporal_pool
+
+        consensus_ = consensus.copy()
+
+        consensus_type = consensus_.pop('type')
+        if consensus_type == 'AvgConsensus':
+            self.consensus = AvgConsensus(**consensus_)
+        else:
+            self.consensus = None
+
+        if self.dropout_ratio != 0:
+            self.dropout = nn.Dropout(p=self.dropout_ratio)
+        else:
+            self.dropout = None
+        self.fc_cls = nn.Linear(self.in_channels, self.num_classes)
+
+        if self.spatial_type == 'avg':
+            # use `nn.AdaptiveAvgPool2d` to adaptively match the in_channels.
+            self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        else:
+            self.avg_pool = None
+
+    def init_weights(self):
+        """Initiate the parameters from scratch."""
+        normal_init(self.fc_cls, std=self.init_std)
+
+    def forward(self, x, num_segs):
+        """Defines the computation performed at every call.
+
+        Args:
+            x (torch.Tensor): The input data.
+            num_segs (int): Useless in TSMHead. By default, `num_segs`
+                is equal to `clip_len * num_clips * num_crops`, which is
+                automatically generated in Recognizer forward phase and
+                useless in TSM models. The `self.num_segments` we need is a
+                hyper parameter to build TSM models.
+        Returns:
+            torch.Tensor: The classification scores for input samples.
+        """
+        # [N * num_segs, in_channels, 7, 7]
+        if self.avg_pool is not None:
+            x = self.avg_pool(x)
+        # [N * num_segs, in_channels, 1, 1]
+        x = torch.flatten(x, 1)
+        # [N * num_segs, in_channels]
+        if self.dropout is not None:
+            x = self.dropout(x)
+        # [N * num_segs, num_classes]
+        cls_score = self.fc_cls(x)
+
+        if self.is_shift and self.temporal_pool:
+            # [2 * N, num_segs // 2, num_classes]
+            cls_score = cls_score.view((-1, self.num_segments // 2) +
+                                       cls_score.size()[1:])
+        else:
+            # [N, num_segs, num_classes]
+            cls_score = cls_score.view((-1, self.num_segments) +
+                                       cls_score.size()[1:])
+        # [N, 1, num_classes]
+        cls_score = self.consensus(cls_score)
+        # [N, num_classes]
+        return cls_score.squeeze(1)