det_map/map/bevformer_utils/bevformer_utils.py

import copy
import warnings
from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER)
import torch
from mmcv.runner.base_module import BaseModule, ModuleList
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import build_feedforward_network, build_attention
from mmcv import ConfigDict

@TRANSFORMER_LAYER.register_module()
class MyCustomBaseTransformerLayer(BaseModule):
    """Base `TransformerLayer` for vision transformer.
    It can be built from `mmcv.ConfigDict` and support more flexible
    customization, for example, using any number of `FFN or LN ` and
    use different kinds of `attention` by specifying a list of `ConfigDict`
    named `attn_cfgs`. It is worth mentioning that it supports `prenorm`
    when you specifying `norm` as the first element of `operation_order`.
    More details about the `prenorm`: `On Layer Normalization in the
    Transformer Architecture <https://arxiv.org/abs/2002.04745>`_ .
    Args:
        attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
            Configs for `self_attention` or `cross_attention` modules,
            The order of the configs in the list should be consistent with
            corresponding attentions in operation_order.
            If it is a dict, all of the attention modules in operation_order
            will be built with this config. Default: None.
        ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
            Configs for FFN, The order of the configs in the list should be
            consistent with corresponding ffn in operation_order.
            If it is a dict, all of the attention modules in operation_order
            will be built with this config.
        operation_order (tuple[str]): The execution order of operation
            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
            Support `prenorm` when you specifying first element as `norm`.
            Default：None.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN').
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
        batch_first (bool): Key, Query and Value are shape
            of (batch, n, embed_dim)
            or (n, batch, embed_dim). Default to False.
    """

    def __init__(self,
                 attn_cfgs=None,
                 ffn_cfgs=dict(
                     type='FFN',
                     embed_dims=256,
                     feedforward_channels=1024,
                     num_fcs=2,
                     ffn_drop=0.,
                     act_cfg=dict(type='ReLU', inplace=True),
                 ),
                 operation_order=None,
                 norm_cfg=dict(type='LN'),
                 init_cfg=None,
                 batch_first=True,
                 **kwargs):

        deprecated_args = dict(
            feedforward_channels='feedforward_channels',
            ffn_dropout='ffn_drop',
            ffn_num_fcs='num_fcs')
        for ori_name, new_name in deprecated_args.items():
            if ori_name in kwargs:
                warnings.warn(
                    f'The arguments `{ori_name}` in BaseTransformerLayer '
                    f'has been deprecated, now you should set `{new_name}` '
                    f'and other FFN related arguments '
                    f'to a dict named `ffn_cfgs`. ')
                ffn_cfgs[new_name] = kwargs[ori_name]

        super(MyCustomBaseTransformerLayer, self).__init__(init_cfg)

        self.batch_first = batch_first

        assert set(operation_order) & set(
            ['self_attn', 'norm', 'ffn', 'cross_attn']) == \
               set(operation_order), f'The operation_order of' \
                                     f' {self.__class__.__name__} should ' \
                                     f'contains all four operation type ' \
                                     f"{['self_attn', 'norm', 'ffn', 'cross_attn']}"

        num_attn = operation_order.count('self_attn') + operation_order.count(
            'cross_attn')
        if isinstance(attn_cfgs, dict):
            attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)]
        else:
            assert num_attn == len(attn_cfgs), f'The length ' \
                                               f'of attn_cfg {num_attn} is ' \
                                               f'not consistent with the number of attention' \
                                               f'in operation_order {operation_order}.'

        self.num_attn = num_attn
        self.operation_order = operation_order
        self.norm_cfg = norm_cfg
        self.pre_norm = operation_order[0] == 'norm'
        self.attentions = ModuleList()

        index = 0
        for operation_name in operation_order:
            if operation_name in ['self_attn', 'cross_attn']:
                if 'batch_first' in attn_cfgs[index]:
                    assert self.batch_first == attn_cfgs[index]['batch_first']
                else:
                    attn_cfgs[index]['batch_first'] = self.batch_first
                attention = build_attention(attn_cfgs[index])
                # Some custom attentions used as `self_attn`
                # or `cross_attn` can have different behavior.
                attention.operation_name = operation_name
                self.attentions.append(attention)
                index += 1

        self.embed_dims = self.attentions[0].embed_dims

        self.ffns = ModuleList()
        num_ffns = operation_order.count('ffn')
        if isinstance(ffn_cfgs, dict):
            ffn_cfgs = ConfigDict(ffn_cfgs)
        if isinstance(ffn_cfgs, dict):
            ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)]
        assert len(ffn_cfgs) == num_ffns
        for ffn_index in range(num_ffns):
            if 'embed_dims' not in ffn_cfgs[ffn_index]:
                ffn_cfgs['embed_dims'] = self.embed_dims
            else:
                ffn_cfgs[ffn_index]['embed_dims'] = self.embed_dims

            self.ffns.append(
                build_feedforward_network(ffn_cfgs[ffn_index]))

        self.norms = ModuleList()
        num_norms = operation_order.count('norm')
        for _ in range(num_norms):
            self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1])

    def forward(self,
                query,
                key=None,
                value=None,
                query_pos=None,
                key_pos=None,
                attn_masks=None,
                query_key_padding_mask=None,
                key_padding_mask=None,
                **kwargs):
        """Forward function for `TransformerDecoderLayer`.
        **kwargs contains some specific arguments of attentions.
        Args:
            query (Tensor): The input query with shape
                [num_queries, bs, embed_dims] if
                self.batch_first is False, else
                [bs, num_queries embed_dims].
            key (Tensor): The key tensor with shape [num_keys, bs,
                embed_dims] if self.batch_first is False, else
                [bs, num_keys, embed_dims] .
            value (Tensor): The value tensor with same shape as `key`.
            query_pos (Tensor): The positional encoding for `query`.
                Default: None.
            key_pos (Tensor): The positional encoding for `key`.
                Default: None.
            attn_masks (List[Tensor] | None): 2D Tensor used in
                calculation of corresponding attention. The length of
                it should equal to the number of `attention` in
                `operation_order`. Default: None.
            query_key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_queries]. Only used in `self_attn` layer.
                Defaults to None.
            key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_keys]. Default: None.
        Returns:
            Tensor: forwarded results with shape [num_queries, bs, embed_dims].
        """

        norm_index = 0
        attn_index = 0
        ffn_index = 0
        identity = query
        if attn_masks is None:
            attn_masks = [None for _ in range(self.num_attn)]
        elif isinstance(attn_masks, torch.Tensor):
            attn_masks = [
                copy.deepcopy(attn_masks) for _ in range(self.num_attn)
            ]
            warnings.warn(f'Use same attn_mask in all attentions in '
                          f'{self.__class__.__name__} ')
        else:
            assert len(attn_masks) == self.num_attn, f'The length of ' \
                                                     f'attn_masks {len(attn_masks)} must be equal ' \
                                                     f'to the number of attention in ' \
                                                     f'operation_order {self.num_attn}'

        for layer in self.operation_order:
            if layer == 'self_attn':
                temp_key = temp_value = query
                query = self.attentions[attn_index](
                    query,
                    temp_key,
                    temp_value,
                    identity if self.pre_norm else None,
                    query_pos=query_pos,
                    key_pos=query_pos,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=query_key_padding_mask,
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'norm':
                query = self.norms[norm_index](query)
                norm_index += 1

            elif layer == 'cross_attn':
                query = self.attentions[attn_index](
                    query,
                    key,
                    value,
                    identity if self.pre_norm else None,
                    query_pos=query_pos,
                    key_pos=key_pos,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=key_padding_mask,
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'ffn':
                query = self.ffns[ffn_index](
                    query, identity if self.pre_norm else None)
                ffn_index += 1

        return query


@TRANSFORMER_LAYER.register_module()
class BEVFormerLayer(MyCustomBaseTransformerLayer):
    """Implements decoder layer in DETR transformer.
    Args:
        attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )):
            Configs for self_attention or cross_attention, the order
            should be consistent with it in `operation_order`. If it is
            a dict, it would be expand to the number of attention in
            `operation_order`.
        feedforward_channels (int): The hidden dimension for FFNs.
        ffn_dropout (float): Probability of an element to be zeroed
            in ffn. Default 0.0.
        operation_order (tuple[str]): The execution order of operation
            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
            Default：None
        act_cfg (dict): The activation config for FFNs. Default: `LN`
        norm_cfg (dict): Config dict for normalization layer.
            Default: `LN`.
        ffn_num_fcs (int): The number of fully-connected layers in FFNs.
            Default：2.
    """

    def __init__(self,
                 attn_cfgs,
                 feedforward_channels,
                 ffn_dropout=0.0,
                 operation_order=None,
                 act_cfg=dict(type='ReLU', inplace=True),
                 norm_cfg=dict(type='LN'),
                 ffn_num_fcs=2,
                 **kwargs):
        super(BEVFormerLayer, self).__init__(
            attn_cfgs=attn_cfgs,
            feedforward_channels=feedforward_channels,
            ffn_dropout=ffn_dropout,
            operation_order=operation_order,
            act_cfg=act_cfg,
            norm_cfg=norm_cfg,
            ffn_num_fcs=ffn_num_fcs,
            **kwargs)
        self.fp16_enabled = False
        '''
        assert len(operation_order) == 6
        assert set(operation_order) == set(
            ['self_attn', 'norm', 'cross_attn', 'ffn'])
        '''

    def forward(self,
                query,
                key=None,
                value=None,
                bev_pos=None,
                query_pos=None,
                key_pos=None,
                attn_masks=None,
                query_key_padding_mask=None,
                key_padding_mask=None,
                ref_2d=None,
                ref_3d=None,
                bev_h=None,
                bev_w=None,
                reference_points_cam=None,
                mask=None,
                spatial_shapes=None,
                level_start_index=None,
                prev_bev=None,
                **kwargs):
        """Forward function for `TransformerDecoderLayer`.

        **kwargs contains some specific arguments of attentions.

        Args:
            query (Tensor): The input query with shape
                [num_queries, bs, embed_dims] if
                self.batch_first is False, else
                [bs, num_queries embed_dims].
            key (Tensor): The key tensor with shape [num_keys, bs,
                embed_dims] if self.batch_first is False, else
                [bs, num_keys, embed_dims] .
            value (Tensor): The value tensor with same shape as `key`.
            query_pos (Tensor): The positional encoding for `query`.
                Default: None.
            key_pos (Tensor): The positional encoding for `key`.
                Default: None.
            attn_masks (List[Tensor] | None): 2D Tensor used in
                calculation of corresponding attention. The length of
                it should equal to the number of `attention` in
                `operation_order`. Default: None.
            query_key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_queries]. Only used in `self_attn` layer.
                Defaults to None.
            key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_keys]. Default: None.

        Returns:
            Tensor: forwarded results with shape [num_queries, bs, embed_dims].
        """

        norm_index = 0
        attn_index = 0
        ffn_index = 0
        identity = query
        if attn_masks is None:
            attn_masks = [None for _ in range(self.num_attn)]
        elif isinstance(attn_masks, torch.Tensor):
            attn_masks = [
                copy.deepcopy(attn_masks) for _ in range(self.num_attn)
            ]
            warnings.warn(f'Use same attn_mask in all attentions in '
                          f'{self.__class__.__name__} ')
        else:
            assert len(attn_masks) == self.num_attn, f'The length of ' \
                                                     f'attn_masks {len(attn_masks)} must be equal ' \
                                                     f'to the number of attention in ' \
                                                     f'operation_order {self.num_attn}'

        for layer in self.operation_order:
            # temporal self attention
            if layer == 'self_attn':

                query = self.attentions[attn_index](
                    query,
                    prev_bev,
                    prev_bev,
                    identity if self.pre_norm else None,
                    query_pos=bev_pos,
                    key_pos=bev_pos,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=query_key_padding_mask,
                    reference_points=ref_2d,
                    spatial_shapes=torch.tensor(
                        [[bev_h, bev_w]], device=query.device),
                    level_start_index=torch.tensor([0], device=query.device),
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'norm':
                query = self.norms[norm_index](query)
                norm_index += 1

            # spaital cross attention
            elif layer == 'cross_attn':
                query = self.attentions[attn_index](
                    query,
                    key,
                    value,
                    identity if self.pre_norm else None,
                    query_pos=query_pos,
                    key_pos=key_pos,
                    reference_points=ref_3d,
                    reference_points_cam=reference_points_cam,
                    mask=mask,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=key_padding_mask,
                    spatial_shapes=spatial_shapes,
                    level_start_index=level_start_index,
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'ffn':
                query = self.ffns[ffn_index](
                    query, identity if self.pre_norm else None)
                ffn_index += 1

        return query