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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_norm_layer
from mmengine.model import ModuleList
from torch import Tensor
from .deformable_detr_layers import DeformableDetrTransformerEncoder
from .detr_layers import DetrTransformerDecoder, DetrTransformerDecoderLayer
class Mask2FormerTransformerEncoder(DeformableDetrTransformerEncoder):
"""Encoder in PixelDecoder of Mask2Former."""
def forward(self, query: Tensor, query_pos: Tensor,
key_padding_mask: Tensor, spatial_shapes: Tensor,
level_start_index: Tensor, valid_ratios: Tensor,
reference_points: Tensor, **kwargs) -> Tensor:
"""Forward function of Transformer encoder.
Args:
query (Tensor): The input query, has shape (bs, num_queries, dim).
query_pos (Tensor): The positional encoding for query, has shape
(bs, num_queries, dim). If not None, it will be added to the
`query` before forward function. Defaults to None.
key_padding_mask (Tensor): The `key_padding_mask` of `self_attn`
input. ByteTensor, has shape (bs, num_queries).
spatial_shapes (Tensor): Spatial shapes of features in all levels,
has shape (num_levels, 2), last dimension represents (h, w).
level_start_index (Tensor): The start index of each level.
A tensor has shape (num_levels, ) and can be represented
as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
valid_ratios (Tensor): The ratios of the valid width and the valid
height relative to the width and the height of features in all
levels, has shape (bs, num_levels, 2).
reference_points (Tensor): The initial reference, has shape
(bs, num_queries, 2) with the last dimension arranged
as (cx, cy).
Returns:
Tensor: Output queries of Transformer encoder, which is also
called 'encoder output embeddings' or 'memory', has shape
(bs, num_queries, dim)
"""
for layer in self.layers:
query = layer(
query=query,
query_pos=query_pos,
key_padding_mask=key_padding_mask,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
reference_points=reference_points,
**kwargs)
return query
class Mask2FormerTransformerDecoder(DetrTransformerDecoder):
"""Decoder of Mask2Former."""
def _init_layers(self) -> None:
"""Initialize decoder layers."""
self.layers = ModuleList([
Mask2FormerTransformerDecoderLayer(**self.layer_cfg)
for _ in range(self.num_layers)
])
self.embed_dims = self.layers[0].embed_dims
self.post_norm = build_norm_layer(self.post_norm_cfg,
self.embed_dims)[1]
class Mask2FormerTransformerDecoderLayer(DetrTransformerDecoderLayer):
"""Implements decoder layer in Mask2Former transformer."""
def forward(self,
query: Tensor,
key: Tensor = None,
value: Tensor = None,
query_pos: Tensor = None,
key_pos: Tensor = None,
self_attn_mask: Tensor = None,
cross_attn_mask: Tensor = None,
key_padding_mask: Tensor = None,
**kwargs) -> Tensor:
"""
Args:
query (Tensor): The input query, has shape (bs, num_queries, dim).
key (Tensor, optional): The input key, has shape (bs, num_keys,
dim). If `None`, the `query` will be used. Defaults to `None`.
value (Tensor, optional): The input value, has the same shape as
`key`, as in `nn.MultiheadAttention.forward`. If `None`, the
`key` will be used. Defaults to `None`.
query_pos (Tensor, optional): The positional encoding for `query`,
has the same shape as `query`. If not `None`, it will be added
to `query` before forward function. Defaults to `None`.
key_pos (Tensor, optional): The positional encoding for `key`, has
the same shape as `key`. If not `None`, it will be added to
`key` before forward function. If None, and `query_pos` has the
same shape as `key`, then `query_pos` will be used for
`key_pos`. Defaults to None.
self_attn_mask (Tensor, optional): ByteTensor mask, has shape
(num_queries, num_keys), as in `nn.MultiheadAttention.forward`.
Defaults to None.
cross_attn_mask (Tensor, optional): ByteTensor mask, has shape
(num_queries, num_keys), as in `nn.MultiheadAttention.forward`.
Defaults to None.
key_padding_mask (Tensor, optional): The `key_padding_mask` of
`self_attn` input. ByteTensor, has shape (bs, num_value).
Defaults to None.
Returns:
Tensor: forwarded results, has shape (bs, num_queries, dim).
"""
query = self.cross_attn(
query=query,
key=key,
value=value,
query_pos=query_pos,
key_pos=key_pos,
attn_mask=cross_attn_mask,
key_padding_mask=key_padding_mask,
**kwargs)
query = self.norms[0](query)
query = self.self_attn(
query=query,
key=query,
value=query,
query_pos=query_pos,
key_pos=query_pos,
attn_mask=self_attn_mask,
**kwargs)
query = self.norms[1](query)
query = self.ffn(query)
query = self.norms[2](query)
return query
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