# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union
import torch
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmcv.ops import MultiScaleDeformableAttention
from mmengine.model import ModuleList
from torch import Tensor, nn
from .detr_layers import (DetrTransformerDecoder, DetrTransformerDecoderLayer,
DetrTransformerEncoder, DetrTransformerEncoderLayer)
from .utils import inverse_sigmoid
class DeformableDetrTransformerEncoder(DetrTransformerEncoder):
"""Transformer encoder of Deformable DETR."""
def _init_layers(self) -> None:
"""Initialize encoder layers."""
self.layers = ModuleList([
DeformableDetrTransformerEncoderLayer(**self.layer_cfg)
for _ in range(self.num_layers)
])
self.embed_dims = self.layers[0].embed_dims
def forward(self, query: Tensor, query_pos: Tensor,
key_padding_mask: Tensor, spatial_shapes: Tensor,
level_start_index: Tensor, valid_ratios: 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).
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).
Returns:
Tensor: Output queries of Transformer encoder, which is also
called 'encoder output embeddings' or 'memory', has shape
(bs, num_queries, dim)
"""
reference_points = self.get_encoder_reference_points(
spatial_shapes, valid_ratios, device=query.device)
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
@staticmethod
def get_encoder_reference_points(
spatial_shapes: Tensor, valid_ratios: Tensor,
device: Union[torch.device, str]) -> Tensor:
"""Get the reference points used in encoder.
Args:
spatial_shapes (Tensor): Spatial shapes of features in all levels,
has shape (num_levels, 2), last dimension represents (h, w).
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).
device (obj:`device` or str): The device acquired by the
`reference_points`.
Returns:
Tensor: Reference points used in decoder, has shape (bs, length,
num_levels, 2).
"""
reference_points_list = []
for lvl, (H, W) in enumerate(spatial_shapes):
ref_y, ref_x = torch.meshgrid(
torch.linspace(
0.5, H - 0.5, H, dtype=torch.float32, device=device),
torch.linspace(
0.5, W - 0.5, W, dtype=torch.float32, device=device))
ref_y = ref_y.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 1] * H)
ref_x = ref_x.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 0] * W)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
# [bs, sum(hw), num_level, 2]
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
class DeformableDetrTransformerDecoder(DetrTransformerDecoder):
"""Transformer Decoder of Deformable DETR."""
def _init_layers(self) -> None:
"""Initialize decoder layers."""
self.layers = ModuleList([
DeformableDetrTransformerDecoderLayer(**self.layer_cfg)
for _ in range(self.num_layers)
])
self.embed_dims = self.layers[0].embed_dims
if self.post_norm_cfg is not None:
raise ValueError('There is not post_norm in '
f'{self._get_name()}')
def forward(self,
query: Tensor,
query_pos: Tensor,
value: Tensor,
key_padding_mask: Tensor,
reference_points: Tensor,
spatial_shapes: Tensor,
level_start_index: Tensor,
valid_ratios: Tensor,
reg_branches: Optional[nn.Module] = None,
**kwargs) -> Tuple[Tensor]:
"""Forward function of Transformer decoder.
Args:
query (Tensor): The input queries, has shape (bs, num_queries,
dim).
query_pos (Tensor): The input positional query, has shape
(bs, num_queries, dim). It will be added to `query` before
forward function.
value (Tensor): The input values, has shape (bs, num_value, dim).
key_padding_mask (Tensor): The `key_padding_mask` of `cross_attn`
input. ByteTensor, has shape (bs, num_value).
reference_points (Tensor): The initial reference, has shape
(bs, num_queries, 4) with the last dimension arranged as
(cx, cy, w, h) when `as_two_stage` is `True`, otherwise has
shape (bs, num_queries, 2) with the last dimension arranged
as (cx, cy).
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).
reg_branches: (obj:`nn.ModuleList`, optional): Used for refining
the regression results. Only would be passed when
`with_box_refine` is `True`, otherwise would be `None`.
Returns:
tuple[Tensor]: Outputs of Deformable Transformer Decoder.
- output (Tensor): Output embeddings of the last decoder, has
shape (num_queries, bs, embed_dims) when `return_intermediate`
is `False`. Otherwise, Intermediate output embeddings of all
decoder layers, has shape (num_decoder_layers, num_queries, bs,
embed_dims).
- reference_points (Tensor): The reference of the last decoder
layer, has shape (bs, num_queries, 4) when `return_intermediate`
is `False`. Otherwise, Intermediate references of all decoder
layers, has shape (num_decoder_layers, bs, num_queries, 4). The
coordinates are arranged as (cx, cy, w, h)
"""
output = query
intermediate = []
intermediate_reference_points = []
for layer_id, layer in enumerate(self.layers):
if reference_points.shape[-1] == 4:
reference_points_input = \
reference_points[:, :, None] * \
torch.cat([valid_ratios, valid_ratios], -1)[:, None]
else:
assert reference_points.shape[-1] == 2
reference_points_input = \
reference_points[:, :, None] * \
valid_ratios[:, None]
output = layer(
output,
query_pos=query_pos,
value=value,
key_padding_mask=key_padding_mask,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
reference_points=reference_points_input,
**kwargs)
if reg_branches is not None:
tmp_reg_preds = reg_branches[layer_id](output)
if reference_points.shape[-1] == 4:
new_reference_points = tmp_reg_preds + inverse_sigmoid(
reference_points)
new_reference_points = new_reference_points.sigmoid()
else:
assert reference_points.shape[-1] == 2
new_reference_points = tmp_reg_preds
new_reference_points[..., :2] = tmp_reg_preds[
..., :2] + inverse_sigmoid(reference_points)
new_reference_points = new_reference_points.sigmoid()
reference_points = new_reference_points.detach()
if self.return_intermediate:
intermediate.append(output)
intermediate_reference_points.append(reference_points)
if self.return_intermediate:
return torch.stack(intermediate), torch.stack(
intermediate_reference_points)
return output, reference_points
class DeformableDetrTransformerEncoderLayer(DetrTransformerEncoderLayer):
"""Encoder layer of Deformable DETR."""
def _init_layers(self) -> None:
"""Initialize self_attn, ffn, and norms."""
self.self_attn = MultiScaleDeformableAttention(**self.self_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(2)
]
self.norms = ModuleList(norms_list)
class DeformableDetrTransformerDecoderLayer(DetrTransformerDecoderLayer):
"""Decoder layer of Deformable DETR."""
def _init_layers(self) -> None:
"""Initialize self_attn, cross-attn, ffn, and norms."""
self.self_attn = MultiheadAttention(**self.self_attn_cfg)
self.cross_attn = MultiScaleDeformableAttention(**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)