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import copy
import torch
import torch.nn as nn
import numpy as np
from torch.nn.init import normal_
from det_map.det.dal.mmdet3d.models.builder import build_fuser
import torch.nn.functional as F
from mmdet.models.utils.builder import TRANSFORMER
from det_map.det.dal.mmdet3d.models.builder import FUSERS
from mmcv.cnn import Linear, bias_init_with_prob, xavier_init, constant_init
from mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from mmcv.cnn.bricks.transformer import build_transformer_layer_sequence, build_positional_encoding
from torchvision.transforms.functional import rotate
from det_map.det.dal.mmdet3d.models.bevformer_modules.temporal_self_attention import TemporalSelfAttention
from det_map.det.dal.mmdet3d.models.bevformer_modules.spatial_cross_attention import MSDeformableAttention3D
from det_map.det.dal.mmdet3d.models.bevformer_modules.decoder import CustomMSDeformableAttention
from typing import List
@FUSERS.register_module()
class ConvFuser(nn.Sequential):
def __init__(self, in_channels: int, out_channels: int) -> None:
self.in_channels = in_channels
self.out_channels = out_channels
super().__init__(
nn.Conv2d(sum(in_channels), out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
)
def forward(self, inputs: List[torch.Tensor]) -> torch.Tensor:
return super().forward(torch.cat(inputs, dim=1))
@TRANSFORMER.register_module()
class MapTRPerceptionTransformer(BaseModule):
"""Implements the Detr3D 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.
two_stage_num_proposals (int): Number of proposals when set
`as_two_stage` as True. Default: 300.
"""
def __init__(self,
bev_h, bev_w,
num_feature_levels=1,
num_cams=2,
z_cfg=dict(
pred_z_flag=False,
gt_z_flag=False,
),
two_stage_num_proposals=300,
fuser=None,
encoder=None,
decoder=None,
embed_dims=256,
rotate_prev_bev=True,
use_shift=True,
use_can_bus=True,
can_bus_norm=True,
use_cams_embeds=True,
rotate_center=[100, 100],
modality='vision',
feat_down_sample_indice=-1,
**kwargs):
super(MapTRPerceptionTransformer, self).__init__(**kwargs)
if modality == 'fusion':
self.fuser = build_fuser(fuser)
# self.use_attn_bev = encoder['type'] == 'BEVFormerEncoder'
self.use_attn_bev = True
self.bev_h = bev_h
self.bev_w = bev_w
self.bev_embedding = nn.Embedding(self.bev_h * self.bev_w, embed_dims)
self.positional_encoding = build_positional_encoding(
dict(
type='CustomLearnedPositionalEncoding',
num_feats=embed_dims // 2,
row_num_embed=self.bev_h,
col_num_embed=self.bev_w,
)
)
self.encoder = build_transformer_layer_sequence(encoder)
self.decoder = build_transformer_layer_sequence(decoder)
self.embed_dims = embed_dims
self.num_feature_levels = num_feature_levels
self.num_cams = num_cams
self.fp16_enabled = False
self.rotate_prev_bev = rotate_prev_bev
self.use_shift = use_shift
self.use_can_bus = use_can_bus
self.can_bus_norm = can_bus_norm
self.use_cams_embeds = use_cams_embeds
self.two_stage_num_proposals = two_stage_num_proposals
self.z_cfg=z_cfg
self.init_layers()
self.rotate_center = rotate_center
self.feat_down_sample_indice = feat_down_sample_indice
def init_layers(self):
"""Initialize layers of the Detr3DTransformer."""
# self.level_embeds = nn.Parameter(torch.Tensor(
# self.num_feature_levels, self.embed_dims))
# self.cams_embeds = nn.Parameter(
# torch.Tensor(self.num_cams, self.embed_dims))
self.reference_points = nn.Linear(self.embed_dims, 2) if not self.z_cfg['gt_z_flag'] \
else nn.Linear(self.embed_dims, 3)
# self.can_bus_mlp = nn.Sequential(
# nn.Linear(18, self.embed_dims // 2),
# nn.ReLU(inplace=True),
# nn.Linear(self.embed_dims // 2, self.embed_dims),
# nn.ReLU(inplace=True),
# )
# if self.can_bus_norm:
# self.can_bus_mlp.add_module('norm', nn.LayerNorm(self.embed_dims))
def init_weights(self):
"""Initialize the transformer weights."""
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for m in self.modules():
if isinstance(m, MSDeformableAttention3D) or isinstance(m, TemporalSelfAttention) \
or isinstance(m, CustomMSDeformableAttention):
try:
m.init_weight()
except AttributeError:
m.init_weights()
normal_(self.level_embeds)
normal_(self.cams_embeds)
xavier_init(self.reference_points, distribution='uniform', bias=0.)
# xavier_init(self.can_bus_mlp, distribution='uniform', bias=0.)
# TODO apply fp16 to this module cause grad_norm NAN
# @auto_fp16(apply_to=('mlvl_feats', 'bev_queries', 'prev_bev', 'bev_pos'), out_fp32=True)
def attn_bev_encode(
self,
mlvl_feats,
cam_params=None,
gt_bboxes_3d=None,
pred_img_depth=None,
prev_bev=None,
bev_mask=None,
**kwargs):
bs = mlvl_feats[0].size(0)
dtype = mlvl_feats[0].dtype
feat_flatten = []
spatial_shapes = []
for lvl, feat in enumerate(mlvl_feats):
bs, num_cam, c, h, w = feat.shape
spatial_shape = (h, w)
feat = feat.flatten(3).permute(1, 0, 3, 2)
spatial_shapes.append(spatial_shape)
feat_flatten.append(feat)
feat_flatten = torch.cat(feat_flatten, 2)
spatial_shapes = torch.as_tensor(
spatial_shapes, dtype=torch.long, device=mlvl_feats[0].device)
level_start_index = torch.cat((spatial_shapes.new_zeros(
(1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
feat_flatten = feat_flatten.permute(0, 2, 1, 3) # (num_cam, H*W, bs, embed_dims)
bev_queries = self.bev_embedding.weight.to(dtype)
bev_queries = bev_queries.unsqueeze(1).repeat(1, bs, 1)
bev_pos = self.positional_encoding(bs, self.bev_h, self.bev_w, bev_queries.device).to(dtype)
bev_pos = bev_pos.flatten(2).permute(2, 0, 1)
bev_embed = self.encoder(
bev_queries,
feat_flatten,
feat_flatten,
bev_h=self.bev_h,
bev_w=self.bev_w,
bev_pos=bev_pos,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
cam_params=cam_params,
gt_bboxes_3d=gt_bboxes_3d,
pred_img_depth=pred_img_depth,
prev_bev=prev_bev,
bev_mask=bev_mask,
**kwargs
)
return bev_embed
def lss_bev_encode(
self,
mlvl_feats,
prev_bev=None,
**kwargs):
# import ipdb;ipdb.set_trace()
# assert len(mlvl_feats) == 1, 'Currently we only use last single level feat in LSS'
# import ipdb;ipdb.set_trace()
images = mlvl_feats[self.feat_down_sample_indice]
img_metas = kwargs['img_metas']
encoder_outputdict = self.encoder(images,img_metas)
bev_embed = encoder_outputdict['bev']
depth = encoder_outputdict['depth']
bs, c, _,_ = bev_embed.shape
bev_embed = bev_embed.view(bs,c,-1).permute(0,2,1).contiguous()
ret_dict = dict(
bev=bev_embed,
depth=depth
)
return ret_dict
def get_bev_features(
self,
mlvl_feats,
lidar_feat,
bev_queries,
bev_h,
bev_w,
grid_length=[0.512, 0.512],
bev_pos=None,
prev_bev=None,
**kwargs):
"""
obtain bev features.
"""
assert self.use_attn_bev
if self.use_attn_bev:
img_metas = kwargs['img_metas']
rot = img_metas['sensor2lidar_rotation']
B, T, N, _, _ = rot.shape
cam_params = (img_metas['sensor2lidar_rotation'][:, -1],
img_metas['sensor2lidar_translation'][:, -1],
img_metas['intrinsics'][:, -1],
img_metas['post_rot'][:, -1],
img_metas['post_tran'][:, -1],
torch.eye(3, device=rot.device, dtype=rot.dtype)[None].repeat(B, 1, 1)
)
bev_embed = self.attn_bev_encode(
mlvl_feats,
cam_params=cam_params,
**kwargs)
else:
ret_dict = self.lss_bev_encode(
mlvl_feats,
prev_bev=prev_bev,
**kwargs)
bev_embed = ret_dict['bev']
depth = ret_dict['depth']
if lidar_feat is not None:
bs = mlvl_feats[0].size(0)
bev_embed = bev_embed.view(bs, bev_h, bev_w, -1).permute(0,3,1,2).contiguous()
lidar_feat = lidar_feat.permute(0,1,3,2).contiguous() # B C H W
# lidar_feat = nn.functional.interpolate(lidar_feat, size=(bev_h,bev_w), mode='bicubic', align_corners=False)
fused_bev = self.fuser([bev_embed, lidar_feat])
fused_bev = fused_bev.flatten(2).permute(0,2,1).contiguous()
bev_embed = fused_bev
ret_dict = dict(
bev=bev_embed,
depth=None
)
return ret_dict
def format_feats(self, mlvl_feats):
bs = mlvl_feats[0].size(0)
feat_flatten = []
spatial_shapes = []
for lvl, feat in enumerate(mlvl_feats):
# import pdb; pdb.set_trace()
bs, num_cam, c, h, w = feat.shape
spatial_shape = (h, w)
feat = feat.flatten(3).permute(1, 0, 3, 2)
if self.use_cams_embeds:
feat = feat
feat = feat
spatial_shapes.append(spatial_shape)
feat_flatten.append(feat)
feat_flatten = torch.cat(feat_flatten, 2)
spatial_shapes = torch.as_tensor(
spatial_shapes, dtype=torch.long, device=feat.device)
level_start_index = torch.cat((spatial_shapes.new_zeros(
(1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
feat_flatten = feat_flatten.permute(
0, 2, 1, 3) # (num_cam, H*W, bs, embed_dims)
return feat_flatten, spatial_shapes, level_start_index
# TODO apply fp16 to this module cause grad_norm NAN
# @auto_fp16(apply_to=('mlvl_feats', 'bev_queries', 'object_query_embed', 'prev_bev', 'bev_pos'))
def forward(self,
mlvl_feats,
lidar_feat,
bev_queries,
object_query_embed,
bev_h,
bev_w,
grid_length=[0.512, 0.512],
bev_pos=None,
reg_branches=None,
cls_branches=None,
prev_bev=None,
**kwargs):
"""Forward function for `Detr3DTransformer`.
Args:
mlvl_feats (list(Tensor)): Input queries from
different level. Each element has shape
[bs, num_cams, embed_dims, h, w].
bev_queries (Tensor): (bev_h*bev_w, c)
bev_pos (Tensor): (bs, embed_dims, bev_h, bev_w)
object_query_embed (Tensor): The query embedding for decoder,
with shape [num_query, c].
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.
- bev_embed: BEV features
- inter_states: Outputs from decoder. If
return_intermediate_dec is True output has shape \
(num_dec_layers, bs, num_query, embed_dims), else has \
shape (1, bs, num_query, embed_dims).
- init_reference_out: The initial value of reference \
points, has shape (bs, num_queries, 4).
- inter_references_out: The internal value of reference \
points in decoder, has shape \
(num_dec_layers, bs,num_query, embed_dims)
- enc_outputs_class: The classification score of \
proposals generated from \
encoder's feature maps, has shape \
(batch, h*w, num_classes). \
Only would be returned when `as_two_stage` is True, \
otherwise None.
- enc_outputs_coord_unact: The regression results \
generated from encoder's feature maps., has shape \
(batch, h*w, 4). Only would \
be returned when `as_two_stage` is True, \
otherwise None.
"""
ouput_dic = self.get_bev_features(
mlvl_feats,
lidar_feat,
bev_queries,
bev_h,
bev_w,
grid_length=grid_length,
bev_pos=bev_pos,
prev_bev=prev_bev,
**kwargs) # bev_embed shape: bs, bev_h*bev_w, embed_dims
bev_embed = ouput_dic['bev']
depth = ouput_dic['depth']
bs = mlvl_feats[0].size(0)
query_pos, query = torch.split(
object_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 = self.reference_points(query_pos)
reference_points = reference_points.sigmoid()
init_reference_out = reference_points
query = query.permute(1, 0, 2)
query_pos = query_pos.permute(1, 0, 2)
bev_embed = bev_embed.permute(1, 0, 2)
feat_flatten, feat_spatial_shapes, feat_level_start_index \
= self.format_feats(mlvl_feats)
inter_states, inter_references = self.decoder(
query=query,
key=None,
value=bev_embed,
query_pos=query_pos,
reference_points=reference_points,
reg_branches=reg_branches,
cls_branches=cls_branches,
spatial_shapes=torch.tensor([[bev_h, bev_w]], device=query.device),
level_start_index=torch.tensor([0], device=query.device),
mlvl_feats=mlvl_feats,
feat_flatten=None,
feat_spatial_shapes=feat_spatial_shapes,
feat_level_start_index=feat_level_start_index,
**kwargs)
inter_references_out = inter_references
return bev_embed, depth, inter_states, init_reference_out, inter_references_out
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