--- a
+++ b/mmaction/models/heads/tpn_head.py
@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+
+from ..builder import HEADS
+from .tsn_head import TSNHead
+
+
+@HEADS.register_module()
+class TPNHead(TSNHead):
+    """Class head for TPN.
+
+    Args:
+        num_classes (int): Number of classes to be classified.
+        in_channels (int): Number of channels in input feature.
+        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.
+        multi_class (bool): Determines whether it is a multi-class
+            recognition task. Default: False.
+        label_smooth_eps (float): Epsilon used in label smooth.
+            Reference: https://arxiv.org/abs/1906.02629. Default: 0.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if self.spatial_type == 'avg':
+            # use `nn.AdaptiveAvgPool3d` to adaptively match the in_channels.
+            self.avg_pool3d = nn.AdaptiveAvgPool3d((1, 1, 1))
+        else:
+            self.avg_pool3d = None
+
+        self.avg_pool2d = None
+        self.new_cls = None
+
+    def _init_new_cls(self):
+        self.new_cls = nn.Conv3d(self.in_channels, self.num_classes, 1, 1, 0)
+        if next(self.fc_cls.parameters()).is_cuda:
+            self.new_cls = self.new_cls.cuda()
+        self.new_cls.weight.copy_(self.fc_cls.weight[..., None, None, None])
+        self.new_cls.bias.copy_(self.fc_cls.bias)
+
+    def forward(self, x, num_segs=None, fcn_test=False):
+        """Defines the computation performed at every call.
+
+        Args:
+            x (torch.Tensor): The input data.
+            num_segs (int | None): Number of segments into which a video
+                is divided. Default: None.
+            fcn_test (bool): Whether to apply full convolution (fcn) testing.
+                Default: False.
+
+        Returns:
+            torch.Tensor: The classification scores for input samples.
+        """
+        if fcn_test:
+            if self.avg_pool3d:
+                x = self.avg_pool3d(x)
+            if self.new_cls is None:
+                self._init_new_cls()
+            cls_score_feat_map = self.new_cls(x)
+            return cls_score_feat_map
+
+        if self.avg_pool2d is None:
+            kernel_size = (1, x.shape[-2], x.shape[-1])
+            self.avg_pool2d = nn.AvgPool3d(kernel_size, stride=1, padding=0)
+
+        if num_segs is None:
+            # [N, in_channels, 3, 7, 7]
+            x = self.avg_pool3d(x)
+        else:
+            # [N * num_segs, in_channels, 7, 7]
+            x = self.avg_pool2d(x)
+            # [N * num_segs, in_channels, 1, 1]
+            x = x.reshape((-1, num_segs) + x.shape[1:])
+            # [N, num_segs, in_channels, 1, 1]
+            x = self.consensus(x)
+            # [N, 1, in_channels, 1, 1]
+            x = x.squeeze(1)
+            # [N, in_channels, 1, 1]
+        if self.dropout is not None:
+            x = self.dropout(x)
+            # [N, in_channels, 1, 1]
+        x = x.view(x.size(0), -1)
+        # [N, in_channels]
+        cls_score = self.fc_cls(x)
+        # [N, num_classes]
+        return cls_score