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# ---------------------------------------------
# Copyright (c) OpenMMLab. All rights reserved.
# ---------------------------------------------
# Modified by Zhiqi Li
# ---------------------------------------------
from .custom_base_transformer_layer import MyCustomBaseTransformerLayer
import copy
import warnings
from mmcv.cnn.bricks.registry import (
ATTENTION,
TRANSFORMER_LAYER,
TRANSFORMER_LAYER_SEQUENCE,
)
from mmcv.cnn.bricks.transformer import TransformerLayerSequence
from mmcv.runner import force_fp32, auto_fp16
import numpy as np
import torch
import cv2 as cv
import mmcv
from mmcv.utils import TORCH_VERSION, digit_version
from mmcv.utils import ext_loader
ext_module = ext_loader.load_ext(
"_ext", ["ms_deform_attn_backward", "ms_deform_attn_forward"]
)
from mmdet3d_plugin.uniad.custom_modules.peft import (LoRALinear, ZeroAdapter, LoRACLAdapter, LoRAMoECLAdapter, MOELoRALinear,
finetuning_detach, frozen_grad, peft_wrapper_forward, lora_wrapper)
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class BEVFormerEncoder(TransformerLayerSequence):
"""
Attention with both self and cross
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,
pc_range=None,
num_points_in_pillar=4,
return_intermediate=False,
dataset_type="nuscenes",
**kwargs,
):
super(BEVFormerEncoder, self).__init__(*args, **kwargs)
self.return_intermediate = return_intermediate
self.num_points_in_pillar = num_points_in_pillar
self.pc_range = pc_range
self.fp16_enabled = False
@staticmethod
def get_reference_points(
H,
W,
Z=8,
num_points_in_pillar=4,
dim="3d",
bs=1,
device="cuda",
dtype=torch.float,
):
"""Get the reference points used in SCA and TSA.
Args:
H, W: spatial shape of bev.
Z: hight of pillar.
D: sample D points uniformly from each pillar.
device (obj:`device`): The device where
reference_points should be.
Returns:
Tensor: reference points used in decoder, has \
shape (bs, num_keys, num_levels, 2).
"""
# reference points in 3D space, used in spatial cross-attention (SCA)
if dim == "3d":
zs = (
torch.linspace(
0.5, Z - 0.5, num_points_in_pillar, dtype=dtype, device=device
)
.view(-1, 1, 1)
.expand(num_points_in_pillar, H, W)
/ Z
)
xs = (
torch.linspace(0.5, W - 0.5, W, dtype=dtype, device=device)
.view(1, 1, W)
.expand(num_points_in_pillar, H, W)
/ W
)
ys = (
torch.linspace(0.5, H - 0.5, H, dtype=dtype, device=device)
.view(1, H, 1)
.expand(num_points_in_pillar, H, W)
/ H
)
ref_3d = torch.stack((xs, ys, zs), -1)
ref_3d = ref_3d.permute(0, 3, 1, 2).flatten(2).permute(0, 2, 1)
ref_3d = ref_3d[None].repeat(bs, 1, 1, 1)
return ref_3d
# reference points on 2D bev plane, used in temporal self-attention (TSA).
elif dim == "2d":
ref_y, ref_x = torch.meshgrid(
torch.linspace(0.5, H - 0.5, H, dtype=dtype, device=device),
torch.linspace(0.5, W - 0.5, W, dtype=dtype, device=device),
)
ref_y = ref_y.reshape(-1)[None] / H
ref_x = ref_x.reshape(-1)[None] / W
ref_2d = torch.stack((ref_x, ref_y), -1)
ref_2d = ref_2d.repeat(bs, 1, 1).unsqueeze(2)
return ref_2d
# This function must use fp32!!!
@force_fp32(apply_to=("reference_points", "img_metas"))
def point_sampling(self, reference_points, pc_range, img_metas):
lidar2img = []
for img_meta in img_metas:
lidar2img.append(img_meta["lidar2img"])
lidar2img = np.asarray(lidar2img)
lidar2img = reference_points.new_tensor(lidar2img) # (B, N, 4, 4)
reference_points = reference_points.clone()
reference_points[..., 0:1] = (
reference_points[..., 0:1] * (pc_range[3] - pc_range[0]) + pc_range[0]
)
reference_points[..., 1:2] = (
reference_points[..., 1:2] * (pc_range[4] - pc_range[1]) + pc_range[1]
)
reference_points[..., 2:3] = (
reference_points[..., 2:3] * (pc_range[5] - pc_range[2]) + pc_range[2]
)
reference_points = torch.cat(
(reference_points, torch.ones_like(reference_points[..., :1])), -1
)
reference_points = reference_points.permute(1, 0, 2, 3)
D, B, num_query = reference_points.size()[:3]
num_cam = lidar2img.size(1)
reference_points = (
reference_points.view(D, B, 1, num_query, 4)
.repeat(1, 1, num_cam, 1, 1)
.unsqueeze(-1)
)
lidar2img = lidar2img.view(1, B, num_cam, 1, 4, 4).repeat(
D, 1, 1, num_query, 1, 1
)
reference_points_cam = torch.matmul(
lidar2img.to(torch.float32), reference_points.to(torch.float32)
).squeeze(-1)
eps = 1e-5
bev_mask = reference_points_cam[..., 2:3] > eps
reference_points_cam = reference_points_cam[..., 0:2] / torch.maximum(
reference_points_cam[..., 2:3],
torch.ones_like(reference_points_cam[..., 2:3]) * eps,
)
reference_points_cam[..., 0] /= img_metas[0]["img_shape"][0][1]
reference_points_cam[..., 1] /= img_metas[0]["img_shape"][0][0]
bev_mask = (
bev_mask
& (reference_points_cam[..., 1:2] > 0.0)
& (reference_points_cam[..., 1:2] < 1.0)
& (reference_points_cam[..., 0:1] < 1.0)
& (reference_points_cam[..., 0:1] > 0.0)
)
if digit_version(TORCH_VERSION) >= digit_version("1.8"):
bev_mask = torch.nan_to_num(bev_mask)
else:
bev_mask = bev_mask.new_tensor(np.nan_to_num(bev_mask.cpu().numpy()))
reference_points_cam = reference_points_cam.permute(2, 1, 3, 0, 4)
bev_mask = bev_mask.permute(2, 1, 3, 0, 4).squeeze(-1)
return reference_points_cam, bev_mask
@auto_fp16()
def forward(
self,
bev_query,
key,
value,
*args,
bev_h=None,
bev_w=None,
bev_pos=None,
spatial_shapes=None,
level_start_index=None,
valid_ratios=None,
prev_bev=None,
shift=0.0,
img_metas=None,
**kwargs,
):
"""Forward function for `TransformerDecoder`.
Args:
bev_query (Tensor): Input BEV query with shape
`(num_query, bs, embed_dims)`.
key & value (Tensor): Input multi-cameta features with shape
(num_cam, num_value, bs, embed_dims)
reference_points (Tensor): The reference
points of offset. has shape
(bs, num_query, 4) when as_two_stage,
otherwise has shape ((bs, num_query, 2).
valid_ratios (Tensor): The radios of valid
points on the feature map, has shape
(bs, num_levels, 2)
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 = bev_query
intermediate = []
ref_3d = self.get_reference_points(
bev_h,
bev_w,
self.pc_range[5] - self.pc_range[2],
self.num_points_in_pillar,
dim="3d",
bs=bev_query.size(1),
device=bev_query.device,
dtype=bev_query.dtype,
)
ref_2d = self.get_reference_points(
bev_h,
bev_w,
dim="2d",
bs=bev_query.size(1),
device=bev_query.device,
dtype=bev_query.dtype,
)
reference_points_cam, bev_mask = self.point_sampling(
ref_3d, self.pc_range, img_metas
)
# bug: this code should be 'shift_ref_2d = ref_2d.clone()', we keep this bug for reproducing our results in paper.
shift_ref_2d = ref_2d # .clone()
shift_ref_2d += shift[:, None, None, :]
# (num_query, bs, embed_dims) -> (bs, num_query, embed_dims)
bev_query = bev_query.permute(1, 0, 2)
bev_pos = bev_pos.permute(1, 0, 2)
bs, len_bev, num_bev_level, _ = ref_2d.shape
if prev_bev is not None:
prev_bev = prev_bev.permute(1, 0, 2)
prev_bev = torch.stack([prev_bev, bev_query], 1).reshape(
bs * 2, len_bev, -1
)
hybird_ref_2d = torch.stack([shift_ref_2d, ref_2d], 1).reshape(
bs * 2, len_bev, num_bev_level, 2
)
else:
hybird_ref_2d = torch.stack([ref_2d, ref_2d], 1).reshape(
bs * 2, len_bev, num_bev_level, 2
)
for lid, layer in enumerate(self.layers):
output = layer(
bev_query,
key,
value,
*args,
bev_pos=bev_pos,
ref_2d=hybird_ref_2d,
ref_3d=ref_3d,
bev_h=bev_h,
bev_w=bev_w,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
reference_points_cam=reference_points_cam,
bev_mask=bev_mask,
prev_bev=prev_bev,
**kwargs,
)
bev_query = output
if self.return_intermediate:
intermediate.append(output)
if self.return_intermediate:
return torch.stack(intermediate)
return output
@TRANSFORMER_LAYER.register_module()
class BEVFormerLayer(MyCustomBaseTransformerLayer):
"""Implements decoder layer in DETR transformer.
Args:
attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )):
Configs for self_attention or cross_attention, the order
should be consistent with it in `operation_order`. If it is
a dict, it would be expand to the number of attention in
`operation_order`.
feedforward_channels (int): The hidden dimension for FFNs.
ffn_dropout (float): Probability of an element to be zeroed
in ffn. Default 0.0.
operation_order (tuple[str]): The execution order of operation
in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
Default: None
act_cfg (dict): The activation config for FFNs. Default: `LN`
norm_cfg (dict): Config dict for normalization layer.
Default: `LN`.
ffn_num_fcs (int): The number of fully-connected layers in FFNs.
Default: 2.
"""
def __init__(
self,
attn_cfgs,
feedforward_channels,
ffn_dropout=0.0,
operation_order=None,
act_cfg=dict(type="ReLU", inplace=True),
norm_cfg=dict(type="LN"),
ffn_num_fcs=2,
**kwargs,
):
super(BEVFormerLayer, self).__init__(
attn_cfgs=attn_cfgs,
feedforward_channels=feedforward_channels,
ffn_dropout=ffn_dropout,
operation_order=operation_order,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
ffn_num_fcs=ffn_num_fcs,
**kwargs,
)
self.fp16_enabled = False
assert len(operation_order) == 6
assert set(operation_order) == set(["self_attn", "norm", "cross_attn", "ffn"])
def forward(
self,
query,
key=None,
value=None,
bev_pos=None,
query_pos=None,
key_pos=None,
attn_masks=None,
query_key_padding_mask=None,
key_padding_mask=None,
ref_2d=None,
ref_3d=None,
bev_h=None,
bev_w=None,
reference_points_cam=None,
mask=None,
spatial_shapes=None,
level_start_index=None,
prev_bev=None,
**kwargs,
):
"""Forward function for `TransformerDecoderLayer`.
**kwargs contains some specific arguments of attentions.
Args:
query (Tensor): The input query with shape
[num_queries, bs, embed_dims] if
self.batch_first is False, else
[bs, num_queries embed_dims].
key (Tensor): The key tensor with shape [num_keys, bs,
embed_dims] if self.batch_first is False, else
[bs, num_keys, embed_dims] .
value (Tensor): The value tensor with same shape as `key`.
query_pos (Tensor): The positional encoding for `query`.
Default: None.
key_pos (Tensor): The positional encoding for `key`.
Default: None.
attn_masks (List[Tensor] | None): 2D Tensor used in
calculation of corresponding attention. The length of
it should equal to the number of `attention` in
`operation_order`. Default: None.
query_key_padding_mask (Tensor): ByteTensor for `query`, with
shape [bs, num_queries]. Only used in `self_attn` layer.
Defaults to None.
key_padding_mask (Tensor): ByteTensor for `query`, with
shape [bs, num_keys]. Default: None.
Returns:
Tensor: forwarded results with shape [num_queries, bs, embed_dims].
"""
norm_index = 0
attn_index = 0
ffn_index = 0
identity = query
if attn_masks is None:
attn_masks = [None for _ in range(self.num_attn)]
elif isinstance(attn_masks, torch.Tensor):
attn_masks = [copy.deepcopy(attn_masks) for _ in range(self.num_attn)]
warnings.warn(
f"Use same attn_mask in all attentions in "
f"{self.__class__.__name__} "
)
else:
assert len(attn_masks) == self.num_attn, (
f"The length of "
f"attn_masks {len(attn_masks)} must be equal "
f"to the number of attention in "
f"operation_order {self.num_attn}"
)
for layer in self.operation_order:
# temporal self attention
if layer == "self_attn":
query = self.attentions[attn_index](
query,
prev_bev,
prev_bev,
identity if self.pre_norm else None,
query_pos=bev_pos,
key_pos=bev_pos,
attn_mask=attn_masks[attn_index],
key_padding_mask=query_key_padding_mask,
reference_points=ref_2d,
spatial_shapes=torch.tensor([[bev_h, bev_w]], device=query.device),
level_start_index=torch.tensor([0], device=query.device),
**kwargs,
)
attn_index += 1
identity = query
elif layer == "norm":
query = self.norms[norm_index](query)
norm_index += 1
# spaital cross attention
elif layer == "cross_attn":
query = self.attentions[attn_index](
query,
key,
value,
identity if self.pre_norm else None,
query_pos=query_pos,
key_pos=key_pos,
reference_points=ref_3d,
reference_points_cam=reference_points_cam,
mask=mask,
attn_mask=attn_masks[attn_index],
key_padding_mask=key_padding_mask,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
**kwargs,
)
attn_index += 1
identity = query
elif layer == "ffn":
query = self.ffns[ffn_index](query, identity if self.pre_norm else None,**kwargs)
ffn_index += 1
return query
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