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import torch
import torch.nn as nn
from mmcv.cnn.bricks.registry import TRANSFORMER_LAYER_SEQUENCE
from mmcv.cnn.bricks.transformer import (
TransformerLayerSequence,
build_transformer_layer_sequence,
)
from mmcv.runner.base_module import BaseModule
from mmdet.models.utils.builder import TRANSFORMER
def inverse_sigmoid(x, eps=1e-5):
"""Inverse function of sigmoid.
Args:
x (Tensor): The tensor to do the
inverse.
eps (float): EPS avoid numerical
overflow. Defaults 1e-5.
Returns:
Tensor: The x has passed the inverse
function of sigmoid, has same
shape with input.
"""
x = x.clamp(min=0, max=1)
x1 = x.clamp(min=eps)
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
@TRANSFORMER.register_module()
class Detr3DCamTransformerPlus(BaseModule):
"""Implements the DeformableDETR transformer.
Args:
as_two_stage (bool): Generate query from encoder features.
Default: False.
num_feature_levels (int): Number of feature maps from FPN:
Default: 4.
"""
def __init__(
self,
num_feature_levels=4,
num_cams=6,
decoder=None,
reference_points_aug=False,
**kwargs
):
super(Detr3DCamTransformerPlus, self).__init__(**kwargs)
self.decoder = build_transformer_layer_sequence(decoder)
self.embed_dims = self.decoder.embed_dims
self.num_feature_levels = num_feature_levels
self.num_cams = num_cams
self.reference_points_aug = reference_points_aug
self.init_layers()
def init_layers(self):
"""Initialize layers of the DeformableDetrTransformer."""
# self.level_embeds = nn.Parameter(
# torch.Tensor(self.num_feature_levels, self.embed_dims))
# self.cam_embeds = nn.Parameter(
# torch.Tensor(self.num_cams, self.embed_dims))
# move ref points to tracker
# self.reference_points = nn.Linear(self.embed_dims, 3)
pass
def init_weights(self):
"""Initialize the transformer weights."""
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
# xavier_init(self.reference_points, distribution='uniform', bias=0.)
# normal_(self.level_embeds)
# normal_(self.cam_embeds)
def forward(
self, mlvl_feats, query_embed, reference_points, reg_branches=None, **kwargs
):
"""Forward function for `Transformer`.
Args:
mlvl_feats (list(Tensor)): Input queries from
different level. Each element has shape
[bs, embed_dims, h, w].
query_embed (Tensor): The query embedding for decoder,
with shape [num_query, 2*embed_dim], can be splitted into
query_feat and query_positional_encoding.
reference_points (Tensor): The corresponding 3d ref points
for the query with shape (num_query, 3)
value is in inverse sigmoid space
reg_branches (obj:`nn.ModuleList`): Regression heads for
feature maps from each decoder layer. Only would
be passed when
`with_box_refine` is True. Default to None.
Returns:
tuple[Tensor]: results of decoder containing the following tensor.
- inter_states: Outputs from decoder, has shape \
(num_dec_layers, num_query, bs, embed_dims)
- init_reference_out: The initial value of reference \
points, has shape (bs, num_queries, 3).
- inter_references_out: The internal value of reference \
points in decoder, has shape \
(num_dec_layers, bs, num_query, 3)
"""
assert query_embed is not None
bs = mlvl_feats[0].size(0)
query_pos, query = torch.split(query_embed, self.embed_dims, dim=1)
query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1)
query = query.unsqueeze(0).expand(bs, -1, -1)
reference_points = reference_points.unsqueeze(dim=0).expand(bs, -1, -1)
if self.training and self.reference_points_aug:
reference_points = reference_points + torch.randn_like(reference_points)
reference_points = reference_points.sigmoid()
init_reference_out = reference_points
# decoder
query = query.permute(1, 0, 2)
# memory = memory.permute(1, 0, 2)
query_pos = query_pos.permute(1, 0, 2)
inter_states, inter_references = self.decoder(
query=query,
key=None,
value=mlvl_feats,
query_pos=query_pos,
reference_points=reference_points,
reg_branches=reg_branches,
**kwargs
)
inter_references_out = inter_references
return inter_states, init_reference_out, inter_references_out
@TRANSFORMER.register_module()
class Detr3DCamTrackTransformer(BaseModule):
"""Implements the DeformableDETR transformer.
Specially designed for track: keep xyz trajectory, and
kep bbox size(which should be consisten across frames)
Args:
num_feature_levels (int): Number of feature maps from FPN:
Default: 4.
"""
def __init__(
self,
num_feature_levels=4,
num_cams=6,
decoder=None,
reference_points_aug=False,
**kwargs
):
super(Detr3DCamTrackTransformer, self).__init__(**kwargs)
self.decoder = build_transformer_layer_sequence(decoder)
self.embed_dims = self.decoder.embed_dims
self.num_feature_levels = num_feature_levels
self.num_cams = num_cams
self.reference_points_aug = reference_points_aug
self.init_layers()
def init_layers(self):
"""Initialize layers of the DeformableDetrTransformer."""
# self.level_embeds = nn.Parameter(
# torch.Tensor(self.num_feature_levels, self.embed_dims))
# self.cam_embeds = nn.Parameter(
# torch.Tensor(self.num_cams, self.embed_dims))
# move ref points to tracker
# self.reference_points = nn.Linear(self.embed_dims, 3)
pass
def init_weights(self):
"""Initialize the transformer weights."""
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(
self,
mlvl_feats,
query_embed,
reference_points,
ref_size,
reg_branches=None,
**kwargs
):
"""Forward function for `Transformer`.
Args:
mlvl_feats (list(Tensor)): Input queries from
different level. Each element has shape
[bs, embed_dims, h, w].
query_embed (Tensor): The query embedding for decoder,
with shape [num_query, 2*embed_dim], can be splitted into
query_feat and query_positional_encoding.
reference_points (Tensor): The corresponding 3d ref points
for the query with shape (num_query, 3)
value is in inverse sigmoid space
ref_size (Tensor): the wlh(bbox size) associated with each query
shape (num_query, 3)
value in log space.
reg_branches (obj:`nn.ModuleList`): Regression heads for
feature maps from each decoder layer. Only would
be passed when
Returns:
tuple[Tensor]: results of decoder containing the following tensor.
- inter_states: Outputs from decoder, has shape \
(num_dec_layers, num_query, bs, embed_dims)
- init_reference_out: The initial value of reference \
points, has shape (bs, num_queries, 3).
- inter_references_out: The internal value of reference \
points in decoder, has shape \
(num_dec_layers, bs, num_query, 3)
"""
assert query_embed is not None
bs = mlvl_feats[0].size(0)
query_pos, query = torch.split(query_embed, self.embed_dims, dim=1)
query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1)
query = query.unsqueeze(0).expand(bs, -1, -1)
reference_points = reference_points.unsqueeze(dim=0).expand(bs, -1, -1)
ref_size = ref_size.unsqueeze(dim=0).expand(bs, -1, -1)
# add augmentation to the reference points' location
if self.training and self.reference_points_aug:
reference_points = reference_points + torch.randn_like(reference_points)
reference_points = reference_points.sigmoid()
# decoder
query = query.permute(1, 0, 2)
# memory = memory.permute(1, 0, 2)
query_pos = query_pos.permute(1, 0, 2)
inter_states, inter_references, inter_box_sizes = self.decoder(
query=query,
key=None,
value=mlvl_feats,
query_pos=query_pos,
reference_points=reference_points,
reg_branches=reg_branches,
ref_size=ref_size,
**kwargs
)
return inter_states, inter_references, inter_box_sizes
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class Detr3DCamTrackPlusTransformerDecoder(TransformerLayerSequence):
"""Implements the decoder in DETR transformer.
Args:
return_intermediate (bool): Whether to return intermediate outputs.
coder_norm_cfg (dict): Config of last normalization layer. Default：
`LN`.
"""
def __init__(self, *args, return_intermediate=True, **kwargs):
super(Detr3DCamTrackPlusTransformerDecoder, self).__init__(*args, **kwargs)
self.return_intermediate = return_intermediate
def forward(
self,
query,
*args,
reference_points=None,
reg_branches=None,
ref_size=None,
**kwargs
):
"""Forward function for `TransformerDecoder`.
Args:
query (Tensor): Input query with shape
`(num_query, bs, embed_dims)`.
reference_points (Tensor): The 3d reference points
associated with each query. shape (num_query, 3).
value is in inevrse sigmoid space
reg_branch: (obj:`nn.ModuleList`): Used for
refining the regression results. Only would
be passed when with_box_refine is True,
otherwise would be passed a `None`.
ref_size (Tensor): the wlh(bbox size) associated with each query
shape (bs, num_query, 3)
value in log space.
Returns:
Tensor: Results with shape [1, num_query, bs, embed_dims] when
return_intermediate is `False`, otherwise it has shape
[num_layers, num_query, bs, embed_dims].
"""
output = query
intermediate = []
intermediate_reference_points = []
intermediate_box_sizes = []
for lid, layer in enumerate(self.layers):
reference_points_input = reference_points
output = layer(
output,
*args,
reference_points=reference_points_input,
ref_size=ref_size,
**kwargs
)
output = output.permute(1, 0, 2)
if reg_branches is not None:
tmp = reg_branches[lid](output)
ref_pts_update = torch.cat(
[
tmp[..., :2],
tmp[..., 4:5],
],
dim=-1,
)
ref_size_update = torch.cat([tmp[..., 2:4], tmp[..., 5:6]], dim=-1)
assert reference_points.shape[-1] == 3
new_reference_points = ref_pts_update + inverse_sigmoid(
reference_points
)
new_reference_points = new_reference_points.sigmoid()
reference_points = new_reference_points.detach()
# add in log space
# ref_size = (ref_size.exp() + ref_size_update.exp()).log()
ref_size = ref_size + ref_size_update
if lid > 0:
ref_size = ref_size.detach()
output = output.permute(1, 0, 2)
if self.return_intermediate:
intermediate.append(output)
intermediate_reference_points.append(reference_points)
intermediate_box_sizes.append(ref_size)
if self.return_intermediate:
return (
torch.stack(intermediate),
torch.stack(intermediate_reference_points),
torch.stack(intermediate_box_sizes),
)
return output, reference_points, ref_size
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