# 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)