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import math
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init, xavier_init
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.runner.base_module import BaseModule
from torch.nn.init import normal_
from .detr3d_transformer import inverse_sigmoid, feature_sampling
@ATTENTION.register_module()
class Detr3DCamRadarCrossAtten(BaseModule):
"""An attention module used in Detr3d.
Args:
embed_dims (int): The embedding dimension of Attention.
Default: 256.
num_heads (int): Parallel attention heads. Default: 64.
num_levels (int): The number of feature map used in
Attention. Default: 4.
num_points (int): The number of sampling points for
each query in each head. Default: 4.
im2col_step (int): The step used in image_to_column.
Default: 64.
dropout (float): A Dropout layer on `inp_residual`.
Default: 0..
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Default: None.
"""
def __init__(
self,
embed_dims=256,
num_heads=8,
num_levels=4,
num_points=5,
num_cams=6,
radar_dims=3,
radar_topk=8,
im2col_step=64,
pc_range=None,
dropout=0.1,
norm_cfg=None,
init_cfg=None,
batch_first=False,
):
super(Detr3DCamRadarCrossAtten, self).__init__(init_cfg)
if embed_dims % num_heads != 0:
raise ValueError(
f"embed_dims must be divisible by num_heads, "
f"but got {embed_dims} and {num_heads}"
)
dim_per_head = embed_dims // num_heads
self.norm_cfg = norm_cfg
self.init_cfg = init_cfg
self.dropout = nn.Dropout(dropout)
self.pc_range = pc_range
# you'd better set dim_per_head to a power of 2
# which is more efficient in the CUDA implementation
def _is_power_of_2(n):
if (not isinstance(n, int)) or (n < 0):
raise ValueError(
"invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))
)
return (n & (n - 1) == 0) and n != 0
if not _is_power_of_2(dim_per_head):
warnings.warn(
"You'd better set embed_dims in "
"MultiScaleDeformAttention to make "
"the dimension of each attention head a power of 2 "
"which is more efficient in our CUDA implementation."
)
self.im2col_step = im2col_step
self.embed_dims = embed_dims
self.num_levels = num_levels
self.num_heads = num_heads
self.num_points = num_points
self.num_cams = num_cams
self.attention_weights = nn.Linear(
embed_dims, num_cams * num_levels * num_points
)
self.radar_dims = radar_dims
self.attention_weights_radar = nn.Linear(embed_dims, radar_topk)
self.radar_topk = radar_topk
self.img_output_proj = nn.Linear(embed_dims, embed_dims)
self.radar_output_proj = nn.Linear(self.radar_dims, self.radar_dims)
self.img_radar_fusion = nn.Sequential(
nn.Linear(embed_dims + radar_dims, embed_dims),
nn.LayerNorm(self.embed_dims),
nn.ReLU(inplace=True),
nn.Linear(self.embed_dims, self.embed_dims),
nn.LayerNorm(self.embed_dims),
)
self.position_encoder = nn.Sequential(
nn.Linear(3, self.embed_dims),
nn.LayerNorm(self.embed_dims),
nn.ReLU(inplace=True),
nn.Linear(self.embed_dims, self.embed_dims),
nn.LayerNorm(self.embed_dims),
nn.ReLU(inplace=True),
)
self.batch_first = batch_first
self.init_weight()
def init_weight(self):
"""Default initialization for Parameters of Module."""
constant_init(self.attention_weights, val=0.0, bias=0.0)
constant_init(self.attention_weights_radar, val=0.0, bias=0.0)
xavier_init(self.img_output_proj, distribution="uniform", bias=0.0)
xavier_init(self.radar_output_proj, distribution="uniform", bias=0.0)
xavier_init(self.img_radar_fusion, distribution="uniform", bias=0.0)
def forward(
self,
query,
key,
value,
residual=None,
query_pos=None,
key_padding_mask=None,
reference_points=None,
ref_size=None,
spatial_shapes=None,
level_start_index=None,
radar_feats=None,
**kwargs,
):
"""Forward Function of Detr3DCrossAtten.
Args:
query (Tensor): Query of Transformer with shape
(num_query, bs, embed_dims).
key (Tensor): The key tensor with shape
`(num_key, bs, embed_dims)`.
value (Tensor): The value tensor with shape
`(num_key, bs, embed_dims)`. (B, N, C, H, W)
residual (Tensor): The tensor used for addition, with the
same shape as `x`. Default None. If None, `x` will be used.
query_pos (Tensor): The positional encoding for `query`.
Default: None.
key_pos (Tensor): The positional encoding for `key`. Default
None.
reference_points (Tensor): The normalized reference
points with shape (bs, num_query, 3),
all elements is range in [0, 1], top-left (0,0),
bottom-right (1, 1), including padding area.
ref_size (Tensor): the wlh(bbox size) associated with each query
shape (bs, num_query, 3)
value in log space.
key_padding_mask (Tensor): ByteTensor for `query`, with
shape [bs, num_key].
spatial_shapes (Tensor): Spatial shape of features in
different level. With shape (num_levels, 2),
last dimension represent (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, ...].
Returns:
Tensor: forwarded results with shape [num_query, bs, embed_dims].
"""
if key is None:
key = query
if value is None:
value = key
if residual is None:
inp_residual = query
if query_pos is not None:
query = query + query_pos
# change to (bs, num_query, embed_dims)
query = query.permute(1, 0, 2)
bs, num_query, _ = query.size()
attention_weights = self.attention_weights(query).view(
bs, 1, num_query, self.num_cams, self.num_points, self.num_levels
)
reference_points_3d, output, mask = feature_sampling(
value, reference_points, self.pc_range, kwargs["img_metas"]
)
output = torch.nan_to_num(output)
mask = torch.nan_to_num(mask)
attention_weights = attention_weights.sigmoid() * mask
output = output * attention_weights
# [bs, embed_dim, num_query]
output = output.sum(-1).sum(-1).sum(-1)
# chaneg to [num_query, bs, embed_dims]
output = output.permute(2, 0, 1)
output = self.img_output_proj(output)
radar_feats, radar_mask = radar_feats[:, :, :-1], radar_feats[:, :, -1]
radar_xy = radar_feats[:, :, :2]
ref_xy = reference_points[:, :, :2]
radar_feats = radar_feats[:, :, 2:]
pad_xy = torch.ones_like(radar_xy) * 1000.0
radar_xy = radar_xy + (1.0 - radar_mask.unsqueeze(dim=-1).type(torch.float)) * (
pad_xy
)
# [B, num_query, M]
ref_radar_dist = -1.0 * torch.cdist(ref_xy, radar_xy)
# [B, num_query, topk]
_value, indices = torch.topk(ref_radar_dist, self.radar_topk)
# [B, num_query, M]
radar_mask = radar_mask.unsqueeze(dim=1).repeat(1, num_query, 1)
# [B, num_query, topk]
top_mask = torch.gather(radar_mask, 2, indices)
# [B, num_query, M, radar_dim]
radar_feats = radar_feats.unsqueeze(dim=1).repeat(1, num_query, 1, 1)
radar_dim = radar_feats.size(-1)
# [B, num_query, topk, radar_dim]
indices_pad = indices.unsqueeze(dim=-1).repeat(1, 1, 1, radar_dim)
# [B, num_query, topk, radar_dim]
radar_feats_topk = torch.gather(
radar_feats, dim=2, index=indices_pad, sparse_grad=False
)
attention_weights_radar = self.attention_weights_radar(query).view(
bs, num_query, self.radar_topk
)
# [B, num_query, topk]
attention_weights_radar = attention_weights_radar.sigmoid() * top_mask
# [B, num_query, topk, radar_dim]
radar_out = radar_feats_topk * attention_weights_radar.unsqueeze(dim=-1)
# [bs, num_query, radar_dim]
radar_out = radar_out.sum(dim=2)
# change to (num_query, bs, embed_dims)
radar_out = radar_out.permute(1, 0, 2)
radar_out = self.radar_output_proj(radar_out)
output = torch.cat((output, radar_out), dim=-1)
output = self.img_radar_fusion(output)
# (num_query, bs, embed_dims)
pos_feat = self.position_encoder(inverse_sigmoid(reference_points_3d)).permute(
1, 0, 2
)
return self.dropout(output) + inp_residual + pos_feat
# @ATTENTION.register_module()
# class Detr3DCrossAtten(BaseModule):
# """An attention module used in Detr3d.
# Args:
# embed_dims (int): The embedding dimension of Attention.
# Default: 256.
# num_heads (int): Parallel attention heads. Default: 64.
# num_levels (int): The number of feature map used in
# Attention. Default: 4.
# num_points (int): The number of sampling points for
# each query in each head. Default: 4.
# im2col_step (int): The step used in image_to_column.
# Default: 64.
# dropout (float): A Dropout layer on `inp_residual`.
# Default: 0..
# init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
# Default: None.
# """
# def __init__(
# self,
# embed_dims=256,
# num_heads=8,
# num_levels=4,
# num_points=5,
# num_cams=6,
# im2col_step=64,
# pc_range=None,
# dropout=0.1,
# norm_cfg=None,
# init_cfg=None,
# batch_first=False,
# ):
# super(Detr3DCrossAtten, self).__init__(init_cfg)
# if embed_dims % num_heads != 0:
# raise ValueError(
# f"embed_dims must be divisible by num_heads, "
# f"but got {embed_dims} and {num_heads}"
# )
# dim_per_head = embed_dims // num_heads
# self.norm_cfg = norm_cfg
# self.init_cfg = init_cfg
# self.dropout = nn.Dropout(dropout)
# self.pc_range = pc_range
# # you'd better set dim_per_head to a power of 2
# # which is more efficient in the CUDA implementation
# def _is_power_of_2(n):
# if (not isinstance(n, int)) or (n < 0):
# raise ValueError(
# "invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))
# )
# return (n & (n - 1) == 0) and n != 0
# if not _is_power_of_2(dim_per_head):
# warnings.warn(
# "You'd better set embed_dims in "
# "MultiScaleDeformAttention to make "
# "the dimension of each attention head a power of 2 "
# "which is more efficient in our CUDA implementation."
# )
# self.im2col_step = im2col_step
# self.embed_dims = embed_dims
# self.num_levels = num_levels
# self.num_heads = num_heads
# self.num_points = num_points
# self.num_cams = num_cams
# self.attention_weights = nn.Linear(
# embed_dims, num_cams * num_levels * num_points
# )
# self.output_proj = nn.Linear(embed_dims, embed_dims)
# self.position_encoder = nn.Sequential(
# nn.Linear(3, self.embed_dims),
# nn.LayerNorm(self.embed_dims),
# nn.ReLU(inplace=True),
# nn.Linear(self.embed_dims, self.embed_dims),
# nn.LayerNorm(self.embed_dims),
# nn.ReLU(inplace=True),
# )
# self.batch_first = batch_first
# self.init_weight()
# def init_weight(self):
# """Default initialization for Parameters of Module."""
# constant_init(self.attention_weights, val=0.0, bias=0.0)
# xavier_init(self.output_proj, distribution="uniform", bias=0.0)
# def forward(
# self,
# query,
# key,
# value,
# residual=None,
# query_pos=None,
# key_padding_mask=None,
# reference_points=None,
# spatial_shapes=None,
# level_start_index=None,
# **kwargs,
# ):
# """Forward Function of Detr3DCrossAtten.
# Args:
# query (Tensor): Query of Transformer with shape
# (num_query, bs, embed_dims).
# key (Tensor): The key tensor with shape
# `(num_key, bs, embed_dims)`.
# value (Tensor): The value tensor with shape
# `(num_key, bs, embed_dims)`. (B, N, C, H, W)
# residual (Tensor): The tensor used for addition, with the
# same shape as `x`. Default None. If None, `x` will be used.
# query_pos (Tensor): The positional encoding for `query`.
# Default: None.
# key_pos (Tensor): The positional encoding for `key`. Default
# None.
# reference_points (Tensor): The normalized reference
# points with shape (bs, num_query, 4),
# all elements is range in [0, 1], top-left (0,0),
# bottom-right (1, 1), including padding area.
# or (N, Length_{query}, num_levels, 4), add
# additional two dimensions is (w, h) to
# form reference boxes.
# key_padding_mask (Tensor): ByteTensor for `query`, with
# shape [bs, num_key].
# spatial_shapes (Tensor): Spatial shape of features in
# different level. With shape (num_levels, 2),
# last dimension represent (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, ...].
# Returns:
# Tensor: forwarded results with shape [num_query, bs, embed_dims].
# """
# if key is None:
# key = query
# if value is None:
# value = key
# if residual is None:
# inp_residual = query
# if query_pos is not None:
# query = query + query_pos
# # change to (bs, num_query, embed_dims)
# query = query.permute(1, 0, 2)
# bs, num_query, _ = query.size()
# attention_weights = self.attention_weights(query).view(
# bs, 1, num_query, self.num_cams, self.num_points, self.num_levels
# )
# reference_points_3d, output, mask = feature_sampling(
# value, reference_points, self.pc_range, kwargs["img_metas"]
# )
# output = torch.nan_to_num(output)
# mask = torch.nan_to_num(mask)
# attention_weights = attention_weights.sigmoid() * mask
# output = output * attention_weights
# output = output.sum(-1).sum(-1).sum(-1)
# output = output.permute(2, 0, 1)
# output = self.output_proj(output)
# # (num_query, bs, embed_dims)
# pos_feat = self.position_encoder(inverse_sigmoid(reference_points_3d)).permute(
# 1, 0, 2
# )
# return self.dropout(output) + inp_residual + pos_feat
# def feature_sampling(mlvl_feats, 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_3d = 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 (B, num_queries, 4)
# reference_points = torch.cat(
# (reference_points, torch.ones_like(reference_points[..., :1])), -1
# )
# B, num_query = reference_points.size()[:2]
# num_cam = lidar2img.size(1)
# reference_points = (
# reference_points.view(B, 1, num_query, 4).repeat(1, num_cam, 1, 1).unsqueeze(-1)
# )
# lidar2img = lidar2img.view(B, num_cam, 1, 4, 4).repeat(1, 1, num_query, 1, 1)
# reference_points_cam = torch.matmul(lidar2img, reference_points).squeeze(-1)
# eps = 1e-5
# 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][0][1]
# reference_points_cam[..., 1] /= img_metas[0]["img_shape"][0][0][0]
# reference_points_cam = (reference_points_cam - 0.5) * 2
# mask = (
# mask
# & (reference_points_cam[..., 0:1] > -1.0)
# & (reference_points_cam[..., 0:1] < 1.0)
# & (reference_points_cam[..., 1:2] > -1.0)
# & (reference_points_cam[..., 1:2] < 1.0)
# )
# mask = mask.view(B, num_cam, 1, num_query, 1, 1).permute(0, 2, 3, 1, 4, 5)
# mask = torch.nan_to_num(mask)
# sampled_feats = []
# for lvl, feat in enumerate(mlvl_feats):
# B, N, C, H, W = feat.size()
# feat = feat.view(B * N, C, H, W)
# reference_points_cam_lvl = reference_points_cam.view(B * N, num_query, 1, 2)
# sampled_feat = F.grid_sample(feat, reference_points_cam_lvl)
# sampled_feat = sampled_feat.view(B, N, C, num_query, 1).permute(0, 2, 3, 1, 4)
# sampled_feats.append(sampled_feat)
# sampled_feats = torch.stack(sampled_feats, -1)
# sampled_feats = sampled_feats.view(B, C, num_query, num_cam, 1, len(mlvl_feats))
# return reference_points_3d, sampled_feats, mask
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