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# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
# ------------------------------------------------------------------------------------------------
import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import constant_, xavier_uniform_
# helpers
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
class MultiScaleDeformableAttention(nn.Module):
"""Multi-Scale Deformable Attention Module used in Deformable-DETR
`Deformable DETR: Deformable Transformers for End-to-End Object Detection.
<https://arxiv.org/pdf/2010.04159.pdf>`_.
Args:
embed_dim (int): The embedding dimension of Attention. Default: 256.
num_heads (int): The number of attention heads. Default: 8.
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.
img2col_steps (int): The step used in image_to_column. Defualt: 64.
dropout (float): Dropout layer used in output. Default: 0.1.
batch_first (bool): if ``True``, then the input and output tensor will be
provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
"""
def __init__(
self,
embed_dim: int = 256,
num_heads: int = 8,
num_levels: int = 4,
num_points: int = 4,
# img2col_step: int = 64,
dropout: float = 0.1,
batch_first: bool = False,
):
super().__init__()
assert num_heads % 2 == 0, "num_heads must be divisible by 2"
if embed_dim % num_heads != 0:
raise ValueError("embed_dim must be divisible by num_heads, but got {} and {}".format(embed_dim, num_heads))
head_dim = embed_dim // num_heads
self.dropout = nn.Dropout(dropout)
self.batch_first = batch_first
if not _is_power_of_2(head_dim):
warnings.warn(
"""
You'd better set d_model in MSDeformAttn to make sure that
each dim of the attention head a power of 2, which is more efficient.
"""
)
# self.im2col_step = img2col_step
self.embed_dim = embed_dim
self.num_heads = num_heads
self.num_levels = num_levels
self.num_points = num_points
self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points)
self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
self.value_proj = nn.Linear(embed_dim, embed_dim)
self.output_proj = nn.Linear(embed_dim, embed_dim)
self.init_weights()
def init_weights(self):
"""
Default initialization for Parameters of Module.
"""
constant_(self.sampling_offsets.weight.data, 0.0)
# DeformableDETR's implementation
# Initial offsets:
# (1, 0, -1, 0, -1, 0, 1, 0)
thetas = torch.arange(self.num_heads, dtype=torch.float32) * (4.0 * math.pi / self.num_heads)
grid_init = thetas.cos()[:, None]
grid_init = grid_init.view(self.num_heads, 1, 1, 1).repeat(1, self.num_levels, self.num_points, 1)
for i in range(self.num_points):
grid_init[:, :, i, :] *= i + 1
# heads = 2, my implementation
# grid_init = torch.Tensor([-1.0, 1.0])
# grid_init = grid_init.view(2, 1, 1).repeat(1, self.num_levels, self.num_points)
# for i in range(self.num_points):
# grid_init[:, :, i] *= (i + 1) * 0.5
# heads = any, my implementation
# grid_init = torch.arange(self.num_heads, dtype=torch.float32)
# grid_init = (grid_init // 2 + 1) * (-1) ** grid_init * 0.5
# grid_init = grid_init.view(self.num_heads, 1, 1).repeat(1, self.num_levels, self.num_points)
# for i in range(self.num_points):
# grid_init[:, :, i] *= i + 1
# TadTR implementation
# Initial offsets: (1, 0, -1, 0, -1, 0, 1, 0)
# thetas = torch.arange(self.num_heads, dtype=torch.float32) * (4.0 * math.pi / self.num_heads)
# grid_init = thetas.cos()[:, None]
# grid_init = grid_init.view(self.num_heads, 1, 1, 1).repeat(1, self.num_levels, self.num_points, 1)
# for i in range(self.num_points):
# grid_init[:, :, i, :] *= i + 1
with torch.no_grad():
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
constant_(self.attention_weights.weight.data, 0.0)
constant_(self.attention_weights.bias.data, 0.0)
xavier_uniform_(self.value_proj.weight.data)
constant_(self.value_proj.bias.data, 0.0)
xavier_uniform_(self.output_proj.weight.data)
constant_(self.output_proj.bias.data, 0.0)
def forward(
self,
query: torch.Tensor,
key: Optional[torch.Tensor] = None,
value: Optional[torch.Tensor] = None,
identity: Optional[torch.Tensor] = None,
query_pos: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None,
reference_points: Optional[torch.Tensor] = None,
spatial_shapes: Optional[torch.Tensor] = None,
level_start_index: Optional[torch.Tensor] = None,
**kwargs
) -> torch.Tensor:
"""Forward Function of MultiScaleDeformableAttention
Args:
query (torch.Tensor): Query embeddings with shape
`(bs, num_query, embed_dim)`
key (torch.Tensor): Key embeddings with shape
`(bs, num_key, embed_dim)`
value (torch.Tensor): Value embeddings with shape
`(bs, num_key, embed_dim)`
identity (torch.Tensor): The tensor used for addition, with the
same shape as `query`. Default: None. If None, `query` will be
used.
query_pos (torch.Tensor): The position embedding for `query`. Default: None.
key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
indicating which elements within `key` to be ignored in attention.
reference_points (torch.Tensor): The normalized reference points
with shape `(bs, num_query, num_levels, 1)`,
all elements is range in [0, 1], top-left (0, 0),
bottom-right (1, 1), including padding are.
or `(N, Length_{query}, num_levels, 2)`, add additional
dimensions `(width)` to form reference boxes.
spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
With shape `(num_levels)`, each element represents length.
level_start_index (torch.Tensor): The start index of each level. A tensor with
shape `(num_levels, )`.
Returns:
torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
"""
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.sum() == num_value
value = self.value_proj(value)
if key_padding_mask is not None:
value = value.masked_fill(key_padding_mask[..., None], float(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,
)
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).view(
bs,
num_query,
self.num_heads,
self.num_levels,
self.num_points,
)
# bs, num_query, num_heads, num_levels, num_points, 2
# reference points
if reference_points.dim() == 4 and reference_points.shape[-1] == 1:
reference_points = reference_points.squeeze(-1)
if reference_points.dim() == 3: # encoder, [bs, num_query, num_levels]
offset_normalizer = spatial_shapes
sampling_locations = (
reference_points[:, :, None, :, None] + sampling_offsets / offset_normalizer[None, None, None, :, None]
)
elif reference_points.dim() == 4: # decoder, [bs, num_query, num_levels, 2]
sampling_locations = (
reference_points[:, :, None, :, None, 0]
+ sampling_offsets / self.num_points * reference_points[:, :, None, :, None, 1] * 0.5
)
else:
raise ValueError(
"Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1])
)
# the original impl for fp32 training
if False: # torch.cuda.is_available() and value.is_cuda:
output = MultiScaleDeformableAttnFunction.apply(
value.to(torch.float32) if value.dtype == torch.float16 else 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 value.dtype == torch.float16:
output = output.to(torch.float16)
output = self.output_proj(output)
if not self.batch_first:
output = output.permute(1, 0, 2)
return self.dropout(output) + identity
def multi_scale_deformable_attn_pytorch(
value: torch.Tensor,
value_spatial_shapes: torch.Tensor,
sampling_locations: torch.Tensor,
attention_weights: torch.Tensor,
) -> torch.Tensor:
bs, _, num_heads, embed_dims = value.shape
_, num_queries, num_heads, num_levels, num_points = sampling_locations.shape
value_list = value.split(value_spatial_shapes.tolist(), dim=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for level, T_ in enumerate(value_spatial_shapes):
# bs, T_, num_heads, embed_dims -> bs*num_heads, embed_dims, T_
value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, T_)
# bs, num_queries, num_heads, num_points -> bs*num_heads, num_queries, num_points
sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
sampling_grid_l_ = torch.stack([-torch.ones_like(sampling_grid_l_), sampling_grid_l_], dim=-1)
# bs*num_heads, embed_dims, num_queries, num_points
sampling_value_l_ = F.grid_sample(
value_l_.unsqueeze(-1),
sampling_grid_l_,
mode="bilinear",
padding_mode="zeros",
align_corners=False,
)
sampling_value_list.append(sampling_value_l_)
# (bs, num_queries, num_heads, num_levels, num_points) -> (bs, num_heads, 1, num_queries, num_levels*num_points)
attention_weights = attention_weights.transpose(1, 2)
attention_weights = attention_weights.reshape(bs * num_heads, 1, num_queries, num_levels * num_points)
output = torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights
output = output.sum(-1).view(bs, num_heads * embed_dims, num_queries)
return output.transpose(1, 2).contiguous()
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