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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN
from mmengine.model import ModuleList
from torch import Tensor
from .detr_layers import (DetrTransformerDecoder, DetrTransformerDecoderLayer,
DetrTransformerEncoder, DetrTransformerEncoderLayer)
from .utils import (MLP, ConditionalAttention, coordinate_to_encoding,
inverse_sigmoid)
class DABDetrTransformerDecoderLayer(DetrTransformerDecoderLayer):
"""Implements decoder layer in DAB-DETR transformer."""
def _init_layers(self):
"""Initialize self-attention, cross-attention, FFN, normalization and
others."""
self.self_attn = ConditionalAttention(**self.self_attn_cfg)
self.cross_attn = ConditionalAttention(**self.cross_attn_cfg)
self.embed_dims = self.self_attn.embed_dims
self.ffn = FFN(**self.ffn_cfg)
norms_list = [
build_norm_layer(self.norm_cfg, self.embed_dims)[1]
for _ in range(3)
]
self.norms = ModuleList(norms_list)
self.keep_query_pos = self.cross_attn.keep_query_pos
def forward(self,
query: Tensor,
key: Tensor,
query_pos: Tensor,
key_pos: Tensor,
ref_sine_embed: Tensor = None,
self_attn_masks: Tensor = None,
cross_attn_masks: Tensor = None,
key_padding_mask: Tensor = None,
is_first: bool = False,
**kwargs) -> Tensor:
"""
Args:
query (Tensor): The input query with shape [bs, num_queries,
dim].
key (Tensor): The key tensor with shape [bs, num_keys,
dim].
query_pos (Tensor): The positional encoding for query in self
attention, with the same shape as `x`.
key_pos (Tensor): The positional encoding for `key`, with the
same shape as `key`.
ref_sine_embed (Tensor): The positional encoding for query in
cross attention, with the same shape as `x`.
Defaults to None.
self_attn_masks (Tensor): ByteTensor mask with shape [num_queries,
num_keys]. Same in `nn.MultiheadAttention.forward`.
Defaults to None.
cross_attn_masks (Tensor): ByteTensor mask with shape [num_queries,
num_keys]. Same in `nn.MultiheadAttention.forward`.
Defaults to None.
key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys].
Defaults to None.
is_first (bool): A indicator to tell whether the current layer
is the first layer of the decoder.
Defaults to False.
Returns:
Tensor: forwarded results with shape
[bs, num_queries, dim].
"""
query = self.self_attn(
query=query,
key=query,
query_pos=query_pos,
key_pos=query_pos,
attn_mask=self_attn_masks,
**kwargs)
query = self.norms[0](query)
query = self.cross_attn(
query=query,
key=key,
query_pos=query_pos,
key_pos=key_pos,
ref_sine_embed=ref_sine_embed,
attn_mask=cross_attn_masks,
key_padding_mask=key_padding_mask,
is_first=is_first,
**kwargs)
query = self.norms[1](query)
query = self.ffn(query)
query = self.norms[2](query)
return query
class DABDetrTransformerDecoder(DetrTransformerDecoder):
"""Decoder of DAB-DETR.
Args:
query_dim (int): The last dimension of query pos,
4 for anchor format, 2 for point format.
Defaults to 4.
query_scale_type (str): Type of transformation applied
to content query. Defaults to `cond_elewise`.
with_modulated_hw_attn (bool): Whether to inject h&w info
during cross conditional attention. Defaults to True.
"""
def __init__(self,
*args,
query_dim: int = 4,
query_scale_type: str = 'cond_elewise',
with_modulated_hw_attn: bool = True,
**kwargs):
self.query_dim = query_dim
self.query_scale_type = query_scale_type
self.with_modulated_hw_attn = with_modulated_hw_attn
super().__init__(*args, **kwargs)
def _init_layers(self):
"""Initialize decoder layers and other layers."""
assert self.query_dim in [2, 4], \
f'{"dab-detr only supports anchor prior or reference point prior"}'
assert self.query_scale_type in [
'cond_elewise', 'cond_scalar', 'fix_elewise'
]
self.layers = ModuleList([
DABDetrTransformerDecoderLayer(**self.layer_cfg)
for _ in range(self.num_layers)
])
embed_dims = self.layers[0].embed_dims
self.embed_dims = embed_dims
self.post_norm = build_norm_layer(self.post_norm_cfg, embed_dims)[1]
if self.query_scale_type == 'cond_elewise':
self.query_scale = MLP(embed_dims, embed_dims, embed_dims, 2)
elif self.query_scale_type == 'cond_scalar':
self.query_scale = MLP(embed_dims, embed_dims, 1, 2)
elif self.query_scale_type == 'fix_elewise':
self.query_scale = nn.Embedding(self.num_layers, embed_dims)
else:
raise NotImplementedError('Unknown query_scale_type: {}'.format(
self.query_scale_type))
self.ref_point_head = MLP(self.query_dim // 2 * embed_dims, embed_dims,
embed_dims, 2)
if self.with_modulated_hw_attn and self.query_dim == 4:
self.ref_anchor_head = MLP(embed_dims, embed_dims, 2, 2)
self.keep_query_pos = self.layers[0].keep_query_pos
if not self.keep_query_pos:
for layer_id in range(self.num_layers - 1):
self.layers[layer_id + 1].cross_attn.qpos_proj = None
def forward(self,
query: Tensor,
key: Tensor,
query_pos: Tensor,
key_pos: Tensor,
reg_branches: nn.Module,
key_padding_mask: Tensor = None,
**kwargs) -> List[Tensor]:
"""Forward function of decoder.
Args:
query (Tensor): The input query with shape (bs, num_queries, dim).
key (Tensor): The input key with shape (bs, num_keys, dim).
query_pos (Tensor): The positional encoding for `query`, with the
same shape as `query`.
key_pos (Tensor): The positional encoding for `key`, with the
same shape as `key`.
reg_branches (nn.Module): The regression branch for dynamically
updating references in each layer.
key_padding_mask (Tensor): ByteTensor with shape (bs, num_keys).
Defaults to `None`.
Returns:
List[Tensor]: forwarded results with shape (num_decoder_layers,
bs, num_queries, dim) if `return_intermediate` is True, otherwise
with shape (1, bs, num_queries, dim). references with shape
(num_decoder_layers, bs, num_queries, 2/4).
"""
output = query
unsigmoid_references = query_pos
reference_points = unsigmoid_references.sigmoid()
intermediate_reference_points = [reference_points]
intermediate = []
for layer_id, layer in enumerate(self.layers):
obj_center = reference_points[..., :self.query_dim]
ref_sine_embed = coordinate_to_encoding(
coord_tensor=obj_center, num_feats=self.embed_dims // 2)
query_pos = self.ref_point_head(
ref_sine_embed) # [bs, nq, 2c] -> [bs, nq, c]
# For the first decoder layer, do not apply transformation
if self.query_scale_type != 'fix_elewise':
if layer_id == 0:
pos_transformation = 1
else:
pos_transformation = self.query_scale(output)
else:
pos_transformation = self.query_scale.weight[layer_id]
# apply transformation
ref_sine_embed = ref_sine_embed[
..., :self.embed_dims] * pos_transformation
# modulated height and weight attention
if self.with_modulated_hw_attn:
assert obj_center.size(-1) == 4
ref_hw = self.ref_anchor_head(output).sigmoid()
ref_sine_embed[..., self.embed_dims // 2:] *= \
(ref_hw[..., 0] / obj_center[..., 2]).unsqueeze(-1)
ref_sine_embed[..., : self.embed_dims // 2] *= \
(ref_hw[..., 1] / obj_center[..., 3]).unsqueeze(-1)
output = layer(
output,
key,
query_pos=query_pos,
ref_sine_embed=ref_sine_embed,
key_pos=key_pos,
key_padding_mask=key_padding_mask,
is_first=(layer_id == 0),
**kwargs)
# iter update
tmp_reg_preds = reg_branches(output)
tmp_reg_preds[..., :self.query_dim] += inverse_sigmoid(
reference_points)
new_reference_points = tmp_reg_preds[
..., :self.query_dim].sigmoid()
if layer_id != self.num_layers - 1:
intermediate_reference_points.append(new_reference_points)
reference_points = new_reference_points.detach()
if self.return_intermediate:
intermediate.append(self.post_norm(output))
output = self.post_norm(output)
if self.return_intermediate:
return [
torch.stack(intermediate),
torch.stack(intermediate_reference_points),
]
else:
return [
output.unsqueeze(0),
torch.stack(intermediate_reference_points)
]
class DABDetrTransformerEncoder(DetrTransformerEncoder):
"""Encoder of DAB-DETR."""
def _init_layers(self):
"""Initialize encoder layers."""
self.layers = ModuleList([
DetrTransformerEncoderLayer(**self.layer_cfg)
for _ in range(self.num_layers)
])
embed_dims = self.layers[0].embed_dims
self.embed_dims = embed_dims
self.query_scale = MLP(embed_dims, embed_dims, embed_dims, 2)
def forward(self, query: Tensor, query_pos: Tensor,
key_padding_mask: Tensor, **kwargs):
"""Forward function of encoder.
Args:
query (Tensor): Input queries of encoder, has shape
(bs, num_queries, dim).
query_pos (Tensor): The positional embeddings of the queries, has
shape (bs, num_feat_points, dim).
key_padding_mask (Tensor): ByteTensor, the key padding mask
of the queries, has shape (bs, num_feat_points).
Returns:
Tensor: With shape (num_queries, bs, dim).
"""
for layer in self.layers:
pos_scales = self.query_scale(query)
query = layer(
query,
query_pos=query_pos * pos_scales,
key_padding_mask=key_padding_mask,
**kwargs)
return query
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