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import warnings
import torch
import torch.nn as nn
from mmcv.cnn import xavier_init, constant_init
from mmcv.cnn.bricks.registry import (ATTENTION)
from mmcv.cnn.bricks.transformer import build_attention
from mmcv.runner import force_fp32
from mmcv.runner.base_module import BaseModule
from .ops.geometric_kernel_attn import GeometricKernelAttentionFunc
@ATTENTION.register_module()
class GeometrySptialCrossAttention(BaseModule):
"""An attention module used in BEVFormer.
Args:
embed_dims (int): The embedding dimension of Attention.
Default: 256.
num_cams (int): The number of cameras
dropout (float): A Dropout layer on `inp_residual`.
Default: 0..
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Default: None.
deformable_attention: (dict): The config for the deformable attention used in SCA.
"""
def __init__(self,
embed_dims=256,
num_cams=6,
pc_range=None,
dropout=0.1,
init_cfg=None,
batch_first=False,
attention=dict(
type='MSDeformableAttention3D',
embed_dims=256,
num_levels=4),
**kwargs
):
super(GeometrySptialCrossAttention, self).__init__(init_cfg)
self.init_cfg = init_cfg
self.dropout = nn.Dropout(dropout)
self.pc_range = pc_range
self.fp16_enabled = False
self.attention = build_attention(attention)
self.embed_dims = embed_dims
self.num_cams = num_cams
self.output_proj = nn.Linear(embed_dims, embed_dims)
self.batch_first = batch_first
self.init_weight()
def init_weight(self):
"""Default initialization for Parameters of Module."""
xavier_init(self.output_proj, distribution='uniform', bias=0.)
@force_fp32(apply_to=('query', 'key', 'value', 'query_pos', 'reference_points_cam'))
def forward(self,
query,
key,
value,
residual=None,
query_pos=None,
key_padding_mask=None,
reference_points=None,
spatial_shapes=None,
reference_points_cam=None,
bev_mask=None,
level_start_index=None,
flag='encoder',
**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
slots = torch.zeros_like(query)
if query_pos is not None:
query = query + query_pos
bs, num_query, _ = query.size()
D = reference_points_cam.size(3)
indexes = []
for i, mask_per_img in enumerate(bev_mask):
index_query_per_img = mask_per_img[0].sum(-1).nonzero().squeeze(-1)
indexes.append(index_query_per_img)
max_len = max([len(each) for each in indexes])
# each camera only interacts with its corresponding BEV queries. This step can greatly save GPU memory.
queries_rebatch = query.new_zeros(
[bs, self.num_cams, max_len, self.embed_dims])
reference_points_rebatch = reference_points_cam.new_zeros(
[bs, self.num_cams, max_len, D, 2])
for j in range(bs):
for i, reference_points_per_img in enumerate(reference_points_cam):
index_query_per_img = indexes[i]
queries_rebatch[j, i, :len(
index_query_per_img)] = query[j, index_query_per_img]
reference_points_rebatch[j, i, :len(
index_query_per_img)] = reference_points_per_img[j, index_query_per_img]
num_cams, l, bs, embed_dims = key.shape
key = key.permute(2, 0, 1, 3).reshape(
bs * self.num_cams, l, self.embed_dims)
value = value.permute(2, 0, 1, 3).reshape(
bs * self.num_cams, l, self.embed_dims)
queries = self.attention(query=queries_rebatch.view(bs * self.num_cams, max_len, self.embed_dims), key=key,
value=value,
reference_points=reference_points_rebatch.view(bs * self.num_cams, max_len, D, 2),
spatial_shapes=spatial_shapes,
level_start_index=level_start_index).view(bs, self.num_cams, max_len, self.embed_dims)
for j in range(bs):
for i, index_query_per_img in enumerate(indexes):
slots[j, index_query_per_img] += queries[j,
i, :len(index_query_per_img)]
count = bev_mask.sum(-1) > 0
count = count.permute(1, 2, 0).sum(-1)
count = torch.clamp(count, min=1.0)
slots = slots / count[..., None]
slots = self.output_proj(slots)
return self.dropout(slots) + inp_residual
@ATTENTION.register_module()
class GeometryKernelAttention(BaseModule):
"""An attention module used in BEVFormer based on Deformable-Detr.
`Deformable DETR: Deformable Transformers for End-to-End Object Detection.
<https://arxiv.org/pdf/2010.04159.pdf>`_.
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_identity`.
Default: 0.1.
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Default to False.
norm_cfg (dict): Config dict for normalization layer.
Default: None.
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=4,
kernel_size=(3, 3),
dilation=1,
im2col_step=64,
dropout=0.1,
batch_first=True,
norm_cfg=None,
init_cfg=None):
super().__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.batch_first = batch_first
self.output_proj = None
self.fp16_enabled = False
# 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
# 4
self.num_levels = num_levels
# 4 num_heads -> num_z_anchors
self.num_heads = num_heads
self.kernel_size = kernel_size
self.num_points = kernel_size[0] * kernel_size[1]
# self.sampling_offsets = nn.Linear(
# embed_dims, num_heads * num_levels * self.num_points * 2)
self.attention_weights = nn.Linear(
embed_dims, num_levels * self.num_points * self.num_heads)
self.value_proj = nn.Linear(embed_dims, embed_dims)
grid_h, grid_w = kernel_size
y = (torch.arange(grid_h) - grid_h // 2) * dilation
x = (torch.arange(grid_w) - grid_w // 2) * dilation
offsets = torch.stack(
torch.meshgrid(x, y)).permute(1, 2, 0).reshape(grid_h * grid_w, 2)
self.register_buffer("grid_offsets", offsets, persistent=False)
self.init_weights()
def init_weights(self):
"""Default initialization for Parameters of Module."""
# constant_init(self.sampling_offsets, 0.)
# thetas = torch.arange(
# self.num_heads,
# dtype=torch.float32) * (2.0 * math.pi / self.num_heads)
# grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
# grid_init = (grid_init /
# grid_init.abs().max(-1, keepdim=True)[0]).view(
# self.num_heads, 1, 1,
# 2).repeat(1, self.num_levels, self.num_points, 1)
# for i in range(self.num_points):
# grid_init[:, :, i, :] *= i + 1
# self.sampling_offsets.bias.data = grid_init.view(-1)
constant_init(self.attention_weights, val=0., bias=0.)
xavier_init(self.value_proj, distribution='uniform', bias=0.)
xavier_init(self.output_proj, distribution='uniform', bias=0.)
self._is_init = True
def forward_kernel_multihead_attention(self, value, spatial_shapes, sampling_locations, attention_weights):
# value: (bs, n, d)
"""CPU version of multi-scale deformable attention.
Args:
value (Tensor): The value has shape
(bs, num_keys, dim)
spatial_shapes (Tensor): Spatial shape of
each feature map, has shape (num_levels, 2),
last dimension 2 represent (h, w)
sampling_locations (Tensor): The location of sampling points,
has shape
(bs ,num_queries, num_levels, num_points, 2),
the last dimension 2 represent (x, y).
attention_weights (Tensor): The weight of sampling points used
when calculate the attention, has shape
(bs ,num_queries, num_levels, num_points),
Returns:
Tensor: has shape (bs, num_queries, embed_dims)
"""
# print(value.shape, sampling_locations.shape, attention_weights.shape)
# print(value.shape)
bs, num_keys, num_heads, dim = value.shape
# (bs * num_heads * num_keys, d)
# torch.cuda.synchronize()
# start2 = time.perf_counter()
value = value.transpose(1, 2).contiguous().view(
bs * num_heads * num_keys, dim)
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
with torch.no_grad():
sampling_index = sampling_locations.new_zeros(
(bs, num_queries, num_heads, num_levels, num_points)).to(value.device)
start_index = 0
for level, (H_, W_) in enumerate(spatial_shapes):
# xy or yx?
sampling_locations[:, :, :, level,
:, 0].clamp_(min=0, max=W_ - 1)
sampling_locations[:, :, :, level,
:, 1].clamp_(min=0, max=H_ - 1)
sampling_index[:, :, :, level] = start_index + sampling_locations[:, :, :, level, :, 0] \
+ sampling_locations[:, :, :, level, :, 1] * W_
start_index += H_ * W_
# print(start_index)
# head index, (bs, head, num_quries,)
sampling_index = sampling_index.transpose(
1, 2).reshape(bs, num_heads, -1)
sampling_index = sampling_index + \
(torch.arange(num_heads).to(sampling_index)
* num_keys).view(1, num_heads, 1)
# batch index
sampling_index = sampling_index.reshape(
bs, -1) + (torch.arange(bs).to(sampling_index) * num_keys * num_heads).view(bs, 1)
# torch.cuda.synchronize()
# end = time.perf_counter()
# print("geometric kernel attention (index): {:.3f} ms".format(
# (end-start)*1000))
# torch.cuda.synchronize()
# start = time.perf_counter()
sampling_value = value[sampling_index].view(
bs, num_heads, num_queries, num_levels * num_points, dim)
# print(sampling_value.shape)
attention_weights = attention_weights.transpose(1, 2).contiguous().view(
bs, num_heads, num_queries, num_levels * num_points, 1)
# torch.cuda.synchronize()
# end = time.perf_counter()
# print("geometric kernel attention (sample): {:.3f} ms".format(
# (end-start)*1000))
# # (bs*head, num_queries, num_levels * num_points, d) -> (bs, head, num_queries, d)
# torch.cuda.synchronize()
# start = time.perf_counter()
output = (sampling_value *
attention_weights).sum(-2).transpose(1, 2).contiguous()
# torch.cuda.synchronize()
# end = time.perf_counter()
# print("geometric kernel attention (matmul): {:.3f} ms".format(
# (end-start)*1000))
# print('x;', output.shape)
return output.view(bs, num_queries, -1)
def forward(self,
query,
key=None,
value=None,
identity=None,
query_pos=None,
key_padding_mask=None,
reference_points=None,
spatial_shapes=None,
level_start_index=None,
**kwargs):
"""Forward Function of MultiScaleDeformAttention.
Args:
query (Tensor): Query of Transformer with shape
( bs, num_query, embed_dims).
key (Tensor): The key tensor with shape
`(bs, num_key, embed_dims)`.
value (Tensor): The value tensor with shape
`(bs, num_key, embed_dims)`.
identity (Tensor): The tensor used for addition, with the
same shape as `query`. Default None. If None,
`query` 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, num_levels, 2),
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 levels. With 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, ...].
Returns:
Tensor: forwarded results with shape [num_query, bs, embed_dims].
"""
if value is None:
value = query
if identity is None:
identity = query
if query_pos is not None:
query = query + query_pos
if not self.batch_first:
# change to (bs, num_query ,embed_dims)
query = query.permute(1, 0, 2)
value = value.permute(1, 0, 2)
bs, num_query, _ = query.shape
bs, num_value, _ = value.shape
assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
value = self.value_proj(value)
if key_padding_mask is not None:
value = value.masked_fill(key_padding_mask[..., None], 0.0)
value = value.view(bs, num_value, self.num_heads, -1)
# sampling_offsets = self.sampling_offsets(query).view(
# bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)
# bs, num_query, num_heads, num_levels, num_points
# bs, q, 4, 4, K^2
attention_weights = self.attention_weights(query).view(
bs, num_query, self.num_heads, self.num_levels * self.num_points)
attention_weights = attention_weights.softmax(-1)
attention_weights = attention_weights.view(bs, num_query,
self.num_heads,
self.num_levels,
self.num_points)
if reference_points.shape[-1] == 2:
"""
For each BEV query, it owns `num_Z_anchors` in 3D space that having different heights.
After proejcting, each BEV query has `num_Z_anchors` reference points in each 2D image.
For each referent point, we sample `num_points` sampling points.
For `num_Z_anchors` reference points, it has overall `num_points * num_Z_anchors` sampling points.
"""
with torch.no_grad():
offset_normalizer = torch.stack(
[spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
bs, num_query, num_Z_anchors, xy = reference_points.shape
# from IPython import embed; embed()
# (K,2) -> (1, 1, 1, 1, k, 2) -> (bs, q, nz, l, k, 2)
offsets = self.grid_offsets[None, None, None, None]
# (bs, q, nz, 1, xy) -> (bs, q, z, l, 2)
reference_points = reference_points[:,
:, :, None, :] * offset_normalizer
# from IPython import embed;embed()
# (bs, q, nz, l, k, xy)
sampling_locations = (
reference_points[:, :, :, :, None, :] + offsets).round().long()
# sampling_offsets = sampling_offsets / \
# offset_normalizer[None, None, None, :, None, :]
# (bs, q, 4(z), 4, K^2, 2)
bs, num_query, num_heads, num_levels, num_all_points, xy = sampling_locations.shape
# sampling_offsets = sampling_offsets.view(
# bs, num_query, num_heads, num_levels, num_all_points // num_Z_anchors, num_Z_anchors, xy)
# sampling_locations = reference_points + sampling_offsets
# bs, num_query, num_heads, num_levels, num_points, num_Z_anchors, xy = sampling_locations.shape
# assert num_all_points == num_points * num_Z_anchors
# sampling_locations = sampling_locations.view(
# bs, num_query, num_heads, num_levels, num_all_points, xy)
elif reference_points.shape[-1] == 4:
assert False
else:
raise ValueError(
f'Last dim of reference_points must be'
f' 2 or 4, but get {reference_points.shape[-1]} instead.')
# sampling_locations.shape: bs, num_query, num_heads, num_levels, num_all_points, 2
# attention_weights.shape: bs, num_query, num_heads, num_levels, num_all_points
# import pdb;pdb.set_trace()
# output = self.forward_kernel_multihead_attention(
# value, spatial_shapes, sampling_locations, attention_weights)
# torch.cuda.synchronize()
# start = time.perf_counter()
output = GeometricKernelAttentionFunc.apply(
value, spatial_shapes, level_start_index, sampling_locations.contiguous(), attention_weights,
self.im2col_step
)
# if torch.cuda.is_available() and value.is_cuda:
# if value.dtype == torch.float16:
# MultiScaleDeformableAttnFunction = MultiScaleDeformableAttnFunction_fp32
# else:
# MultiScaleDeformableAttnFunction = MultiScaleDeformableAttnFunction_fp32
# output = MultiScaleDeformableAttnFunction.apply(
# value, spatial_shapes, level_start_index, sampling_locations,
# attention_weights, self.im2col_step)
# else:
# output = multi_scale_deformable_attn_pytorch(
# value, spatial_shapes, sampling_locations, attention_weights)
if not self.batch_first:
output = output.permute(1, 0, 2)
# torch.cuda.synchronize()
# end = time.perf_counter()
# print("geometric kernel attention: {:.3f} ms".format((end-start)*1000))
return output
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