未验证 提交 e8aeb802 编写于 作者: S shangliang Xu 提交者: GitHub

[transformer] add Deformable DETR base code (#3718)

上级 283f5ac7
......@@ -21,9 +21,10 @@ import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
import pycocotools.mask as mask_util
from ..initializer import linear_init_
from ..initializer import linear_init_, constant_
from ..transformers.utils import inverse_sigmoid
__all__ = ['DETRHead']
__all__ = ['DETRHead', 'DeformableDETRHead']
class MLP(nn.Layer):
......@@ -275,3 +276,77 @@ class DETRHead(nn.Layer):
gt_mask=gt_mask)
else:
return (outputs_bbox[-1], outputs_logit[-1], outputs_seg)
@register
class DeformableDETRHead(nn.Layer):
__shared__ = ['num_classes', 'hidden_dim']
__inject__ = ['loss']
def __init__(self,
num_classes=80,
hidden_dim=512,
nhead=8,
num_mlp_layers=3,
loss='DETRLoss'):
super(DeformableDETRHead, self).__init__()
self.num_classes = num_classes
self.hidden_dim = hidden_dim
self.nhead = nhead
self.loss = loss
self.score_head = nn.Linear(hidden_dim, self.num_classes)
self.bbox_head = MLP(hidden_dim,
hidden_dim,
output_dim=4,
num_layers=num_mlp_layers)
self._reset_parameters()
def _reset_parameters(self):
linear_init_(self.score_head)
constant_(self.score_head.bias, -4.595)
constant_(self.bbox_head.layers[-1].weight)
bias = paddle.zeros_like(self.bbox_head.layers[-1].bias)
bias[2:] = -2.0
self.bbox_head.layers[-1].bias.set_value(bias)
@classmethod
def from_config(cls, cfg, hidden_dim, nhead, input_shape):
return {'hidden_dim': hidden_dim, 'nhead': nhead}
def forward(self, out_transformer, body_feats, inputs=None):
r"""
Args:
out_transformer (Tuple): (feats: [num_levels, batch_size,
num_queries, hidden_dim],
memory: [batch_size,
\sum_{l=0}^{L-1} H_l \cdot W_l, hidden_dim],
reference_points: [batch_size, num_queries, 2])
body_feats (List(Tensor)): list[[B, C, H, W]]
inputs (dict): dict(inputs)
"""
feats, memory, reference_points = out_transformer
reference_points = inverse_sigmoid(reference_points.unsqueeze(0))
outputs_bbox = self.bbox_head(feats)
# It's equivalent to "outputs_bbox[:, :, :, :2] += reference_points",
# but the gradient is wrong in paddle.
outputs_bbox = paddle.concat(
[
outputs_bbox[:, :, :, :2] + reference_points,
outputs_bbox[:, :, :, 2:]
],
axis=-1)
outputs_bbox = F.sigmoid(outputs_bbox)
outputs_logit = self.score_head(feats)
if self.training:
assert inputs is not None
assert 'gt_bbox' in inputs and 'gt_class' in inputs
return self.loss(outputs_bbox, outputs_logit, inputs['gt_bbox'],
inputs['gt_class'])
else:
return (outputs_bbox[-1], outputs_logit[-1], None)
......@@ -532,14 +532,25 @@ class DETRBBoxPostProcess(object):
scores = F.sigmoid(logits) if self.use_focal_loss else F.softmax(
logits)[:, :, :-1]
scores, labels = scores.max(-1), scores.argmax(-1)
if not self.use_focal_loss:
scores, labels = scores.max(-1), scores.argmax(-1)
if scores.shape[1] > self.num_top_queries:
scores, index = paddle.topk(scores, self.num_top_queries, axis=-1)
scores, index = paddle.topk(
scores, self.num_top_queries, axis=-1)
labels = paddle.stack(
[paddle.gather(l, i) for l, i in zip(labels, index)])
bbox_pred = paddle.stack(
[paddle.gather(b, i) for b, i in zip(bbox_pred, index)])
else:
scores, index = paddle.topk(
scores.reshape([logits.shape[0], -1]),
self.num_top_queries,
axis=-1)
labels = index % logits.shape[2]
index = index // logits.shape[2]
bbox_pred = paddle.stack(
[paddle.gather(b, i) for b, i in zip(bbox_pred, index)])
bbox_pred = paddle.concat(
[
......
......@@ -16,8 +16,10 @@ from . import detr_transformer
from . import utils
from . import matchers
from . import position_encoding
from . import deformable_transformer
from .detr_transformer import *
from .utils import *
from .matchers import *
from .position_encoding import *
from .deformable_transformer import *
# Copyright (c) 2021 PaddlePaddle 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from ppdet.core.workspace import register
from ..layers import MultiHeadAttention
from .position_encoding import PositionEmbedding
from .utils import _get_clones, deformable_attention_core_func
from ..initializer import linear_init_, constant_, xavier_uniform_, normal_
__all__ = ['DeformableTransformer']
class MSDeformableAttention(nn.Layer):
def __init__(self,
embed_dim=256,
num_heads=8,
num_levels=4,
num_points=4,
lr_mult=0.1):
"""
Multi-Scale Deformable Attention Module
"""
super(MSDeformableAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.num_levels = num_levels
self.num_points = num_points
self.total_points = num_heads * num_levels * num_points
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.sampling_offsets = nn.Linear(
embed_dim,
self.total_points * 2,
weight_attr=ParamAttr(learning_rate=lr_mult),
bias_attr=ParamAttr(learning_rate=lr_mult))
self.attention_weights = nn.Linear(embed_dim, self.total_points)
self.value_proj = nn.Linear(embed_dim, embed_dim)
self.output_proj = nn.Linear(embed_dim, embed_dim)
self._reset_parameters()
def _reset_parameters(self):
# sampling_offsets
constant_(self.sampling_offsets.weight)
thetas = paddle.arange(
self.num_heads,
dtype=paddle.float32) * (2.0 * math.pi / self.num_heads)
grid_init = paddle.stack([thetas.cos(), thetas.sin()], -1)
grid_init = grid_init / grid_init.abs().max(-1, keepdim=True)
grid_init = grid_init.reshape([self.num_heads, 1, 1, 2]).tile(
[1, self.num_levels, self.num_points, 1])
scaling = paddle.arange(
1, self.num_points + 1,
dtype=paddle.float32).reshape([1, 1, -1, 1])
grid_init *= scaling
self.sampling_offsets.bias.set_value(grid_init.flatten())
# attention_weights
constant_(self.attention_weights.weight)
constant_(self.attention_weights.bias)
# proj
xavier_uniform_(self.value_proj.weight)
constant_(self.value_proj.bias)
xavier_uniform_(self.output_proj.weight)
constant_(self.output_proj.bias)
def forward(self,
query,
reference_points,
value,
value_spatial_shapes,
value_mask=None):
"""
Args:
query (Tensor): [bs, query_length, C]
reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
bottom-right (1, 1), including padding area
value (Tensor): [bs, value_length, C]
value_spatial_shapes (Tensor): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements
Returns:
output (Tensor): [bs, Length_{query}, C]
"""
bs, Len_q = query.shape[:2]
Len_v = value.shape[1]
assert int(value_spatial_shapes.prod(1).sum()) == Len_v
value = self.value_proj(value)
if value_mask is not None:
value_mask = value_mask.astype(value.dtype).unsqueeze(-1)
value *= value_mask
value = value.reshape([bs, Len_v, self.num_heads, self.head_dim])
sampling_offsets = self.sampling_offsets(query).reshape(
[bs, Len_q, self.num_heads, self.num_levels, self.num_points, 2])
attention_weights = self.attention_weights(query).reshape(
[bs, Len_q, self.num_heads, self.num_levels * self.num_points])
attention_weights = F.softmax(attention_weights, -1).reshape(
[bs, Len_q, self.num_heads, self.num_levels, self.num_points])
offset_normalizer = value_spatial_shapes.flip([1]).reshape(
[1, 1, 1, self.num_levels, 1, 2])
sampling_locations = reference_points.reshape([
bs, Len_q, 1, self.num_levels, 1, 2
]) + sampling_offsets / offset_normalizer
output = deformable_attention_core_func(
value, value_spatial_shapes, sampling_locations, attention_weights)
output = self.output_proj(output)
return output
class DeformableTransformerEncoderLayer(nn.Layer):
def __init__(self,
d_model=256,
n_head=8,
dim_feedforward=1024,
dropout=0.1,
activation="relu",
n_levels=4,
n_points=4,
weight_attr=None,
bias_attr=None):
super(DeformableTransformerEncoderLayer, self).__init__()
# self attention
self.self_attn = MSDeformableAttention(d_model, n_head, n_levels,
n_points)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, dim_feedforward, weight_attr,
bias_attr)
self.activation = getattr(F, activation)
self.dropout2 = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr,
bias_attr)
self.dropout3 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
self._reset_parameters()
def _reset_parameters(self):
linear_init_(self.linear1)
linear_init_(self.linear2)
xavier_uniform_(self.linear1.weight)
xavier_uniform_(self.linear2.weight)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, src):
src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
src = src + self.dropout3(src2)
src = self.norm2(src)
return src
def forward(self,
src,
reference_points,
spatial_shapes,
src_mask=None,
pos_embed=None):
# self attention
src2 = self.self_attn(
self.with_pos_embed(src, pos_embed), reference_points, src,
spatial_shapes, src_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
# ffn
src = self.forward_ffn(src)
return src
class DeformableTransformerEncoder(nn.Layer):
def __init__(self, encoder_layer, num_layers):
super(DeformableTransformerEncoder, self).__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios):
valid_ratios = valid_ratios.unsqueeze(1)
reference_points = []
for i, (H, W) in enumerate(spatial_shapes.tolist()):
ref_y, ref_x = paddle.meshgrid(
paddle.linspace(0.5, H - 0.5, H),
paddle.linspace(0.5, W - 0.5, W))
ref_y = ref_y.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 1] *
H)
ref_x = ref_x.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 0] *
W)
reference_points.append(paddle.stack((ref_x, ref_y), axis=-1))
reference_points = paddle.concat(reference_points, 1).unsqueeze(2)
reference_points = reference_points * valid_ratios
return reference_points
def forward(self,
src,
spatial_shapes,
src_mask=None,
pos_embed=None,
valid_ratios=None):
output = src
if valid_ratios is None:
valid_ratios = paddle.ones(
[src.shape[0], spatial_shapes.shape[0], 2])
reference_points = self.get_reference_points(spatial_shapes,
valid_ratios)
for layer in self.layers:
output = layer(output, reference_points, spatial_shapes, src_mask,
pos_embed)
return output
class DeformableTransformerDecoderLayer(nn.Layer):
def __init__(self,
d_model=256,
n_head=8,
dim_feedforward=1024,
dropout=0.1,
activation="relu",
n_levels=4,
n_points=4,
weight_attr=None,
bias_attr=None):
super(DeformableTransformerDecoderLayer, self).__init__()
# self attention
self.self_attn = MultiHeadAttention(d_model, n_head, dropout=dropout)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# cross attention
self.cross_attn = MSDeformableAttention(d_model, n_head, n_levels,
n_points)
self.dropout2 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, dim_feedforward, weight_attr,
bias_attr)
self.activation = getattr(F, activation)
self.dropout3 = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr,
bias_attr)
self.dropout4 = nn.Dropout(dropout)
self.norm3 = nn.LayerNorm(d_model)
self._reset_parameters()
def _reset_parameters(self):
linear_init_(self.linear1)
linear_init_(self.linear2)
xavier_uniform_(self.linear1.weight)
xavier_uniform_(self.linear2.weight)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, tgt):
tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout4(tgt2)
tgt = self.norm3(tgt)
return tgt
def forward(self,
tgt,
reference_points,
memory,
memory_spatial_shapes,
memory_mask=None,
query_pos_embed=None):
# self attention
q = k = self.with_pos_embed(tgt, query_pos_embed)
tgt2 = self.self_attn(q, k, value=tgt)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
# cross attention
tgt2 = self.cross_attn(
self.with_pos_embed(tgt, query_pos_embed), reference_points, memory,
memory_spatial_shapes, memory_mask)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# ffn
tgt = self.forward_ffn(tgt)
return tgt
class DeformableTransformerDecoder(nn.Layer):
def __init__(self, decoder_layer, num_layers, return_intermediate=False):
super(DeformableTransformerDecoder, self).__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.return_intermediate = return_intermediate
def forward(self,
tgt,
reference_points,
memory,
memory_spatial_shapes,
memory_mask=None,
query_pos_embed=None):
output = tgt
intermediate = []
for lid, layer in enumerate(self.layers):
output = layer(output, reference_points, memory,
memory_spatial_shapes, memory_mask, query_pos_embed)
if self.return_intermediate:
intermediate.append(output)
if self.return_intermediate:
return paddle.stack(intermediate)
return output.unsqueeze(0)
@register
class DeformableTransformer(nn.Layer):
__shared__ = ['hidden_dim']
def __init__(self,
num_queries=300,
position_embed_type='sine',
return_intermediate_dec=True,
backbone_num_channels=[512, 1024, 2048],
num_feature_levels=4,
num_encoder_points=4,
num_decoder_points=4,
hidden_dim=256,
nhead=8,
num_encoder_layers=6,
num_decoder_layers=6,
dim_feedforward=1024,
dropout=0.1,
activation="relu",
lr_mult=0.1,
weight_attr=None,
bias_attr=None):
super(DeformableTransformer, self).__init__()
assert position_embed_type in ['sine', 'learned'], \
f'ValueError: position_embed_type not supported {position_embed_type}!'
assert len(backbone_num_channels) <= num_feature_levels
self.hidden_dim = hidden_dim
self.nhead = nhead
self.num_feature_levels = num_feature_levels
encoder_layer = DeformableTransformerEncoderLayer(
hidden_dim, nhead, dim_feedforward, dropout, activation,
num_feature_levels, num_encoder_points, weight_attr, bias_attr)
self.encoder = DeformableTransformerEncoder(encoder_layer,
num_encoder_layers)
decoder_layer = DeformableTransformerDecoderLayer(
hidden_dim, nhead, dim_feedforward, dropout, activation,
num_feature_levels, num_decoder_points, weight_attr, bias_attr)
self.decoder = DeformableTransformerDecoder(
decoder_layer, num_decoder_layers, return_intermediate_dec)
self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
self.query_pos_embed = nn.Embedding(num_queries, hidden_dim)
self.reference_points = nn.Linear(
hidden_dim,
2,
weight_attr=ParamAttr(learning_rate=lr_mult),
bias_attr=ParamAttr(learning_rate=lr_mult))
self.input_proj = nn.LayerList()
for in_channels in backbone_num_channels:
self.input_proj.append(
nn.Sequential(
nn.Conv2D(
in_channels,
hidden_dim,
kernel_size=1,
weight_attr=weight_attr,
bias_attr=bias_attr),
nn.GroupNorm(32, hidden_dim)))
in_channels = backbone_num_channels[-1]
for _ in range(num_feature_levels - len(backbone_num_channels)):
self.input_proj.append(
nn.Sequential(
nn.Conv2D(
in_channels,
hidden_dim,
kernel_size=3,
stride=2,
padding=1,
weight_attr=weight_attr,
bias_attr=bias_attr),
nn.GroupNorm(32, hidden_dim)))
in_channels = hidden_dim
self.position_embedding = PositionEmbedding(
hidden_dim // 2,
normalize=True if position_embed_type == 'sine' else False,
embed_type=position_embed_type,
offset=-0.5)
self._reset_parameters()
def _reset_parameters(self):
normal_(self.level_embed.weight)
normal_(self.tgt_embed.weight)
normal_(self.query_pos_embed.weight)
xavier_uniform_(self.reference_points.weight)
constant_(self.reference_points.bias)
for l in self.input_proj:
xavier_uniform_(l[0].weight)
constant_(l[0].bias)
@classmethod
def from_config(cls, cfg, input_shape):
return {'backbone_num_channels': [i.channels for i in input_shape], }
def _get_valid_ratio(self, mask):
mask = mask.astype(paddle.float32)
_, H, W = mask.shape
valid_ratio_h = paddle.sum(mask[:, :, 0], 1) / H
valid_ratio_w = paddle.sum(mask[:, 0, :], 1) / W
valid_ratio = paddle.stack([valid_ratio_w, valid_ratio_h], -1)
return valid_ratio
def forward(self, src_feats, src_mask=None):
srcs = []
for i in range(len(src_feats)):
srcs.append(self.input_proj[i](src_feats[i]))
if self.num_feature_levels > len(srcs):
len_srcs = len(srcs)
for i in range(len_srcs, self.num_feature_levels):
if i == len_srcs:
srcs.append(self.input_proj[i](src_feats[-1]))
else:
srcs.append(self.input_proj[i](srcs[-1]))
src_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
valid_ratios = []
for level, src in enumerate(srcs):
bs, c, h, w = src.shape
spatial_shapes.append([h, w])
src = src.flatten(2).transpose([0, 2, 1])
src_flatten.append(src)
if src_mask is not None:
mask = F.interpolate(
src_mask.unsqueeze(0).astype(src.dtype),
size=(h, w))[0].astype('bool')
else:
mask = paddle.ones([bs, h, w], dtype='bool')
valid_ratios.append(self._get_valid_ratio(mask))
pos_embed = self.position_embedding(mask).flatten(2).transpose(
[0, 2, 1])
lvl_pos_embed = pos_embed + self.level_embed.weight[level].reshape(
[1, 1, -1])
lvl_pos_embed_flatten.append(lvl_pos_embed)
mask = mask.astype(src.dtype).flatten(1)
mask_flatten.append(mask)
src_flatten = paddle.concat(src_flatten, 1)
mask_flatten = paddle.concat(mask_flatten, 1)
lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1)
# [l, 2]
spatial_shapes = paddle.to_tensor(spatial_shapes, dtype='int64')
# [b, l, 2]
valid_ratios = paddle.stack(valid_ratios, 1)
# encoder
memory = self.encoder(src_flatten, spatial_shapes, mask_flatten,
lvl_pos_embed_flatten, valid_ratios)
# prepare input for decoder
bs, _, c = memory.shape
query_embed = self.query_pos_embed.weight.unsqueeze(0).tile([bs, 1, 1])
tgt = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1])
reference_points = F.sigmoid(self.reference_points(query_embed))
reference_points_input = reference_points.unsqueeze(
2) * valid_ratios.unsqueeze(1)
# decoder
hs = self.decoder(tgt, reference_points_input, memory, spatial_shapes,
mask_flatten, query_embed)
return (hs, memory, reference_points)
......@@ -32,11 +32,14 @@ class PositionEmbedding(nn.Layer):
normalize=True,
scale=None,
embed_type='sine',
num_embeddings=50):
num_embeddings=50,
offset=0.):
super(PositionEmbedding, self).__init__()
assert embed_type in ['sine', 'learned']
self.embed_type = embed_type
self.offset = offset
self.eps = 1e-6
if self.embed_type == 'sine':
self.num_pos_feats = num_pos_feats
self.temperature = temperature
......@@ -65,9 +68,10 @@ class PositionEmbedding(nn.Layer):
y_embed = mask.cumsum(1, dtype='float32')
x_embed = mask.cumsum(2, dtype='float32')
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
y_embed = (y_embed + self.offset) / (
y_embed[:, -1:, :] + self.eps) * self.scale
x_embed = (x_embed + self.offset) / (
x_embed[:, :, -1:] + self.eps) * self.scale
dim_t = 2 * (paddle.arange(self.num_pos_feats) //
2).astype('float32')
......
......@@ -25,7 +25,8 @@ from ..bbox_utils import bbox_overlaps
__all__ = [
'_get_clones', 'bbox_overlaps', 'bbox_cxcywh_to_xyxy',
'bbox_xyxy_to_cxcywh', 'sigmoid_focal_loss'
'bbox_xyxy_to_cxcywh', 'sigmoid_focal_loss', 'inverse_sigmoid',
'deformable_attention_core_func'
]
......@@ -55,3 +56,51 @@ def sigmoid_focal_loss(logit, label, normalizer=1.0, alpha=0.25, gamma=2.0):
alpha_t = alpha * label + (1 - alpha) * (1 - label)
loss = alpha_t * loss
return loss.mean(1).sum() / normalizer
def inverse_sigmoid(x, eps=1e-6):
x = x.clip(min=0., max=1.)
return paddle.log(x / (1 - x + eps) + eps)
def deformable_attention_core_func(value, value_spatial_shapes,
sampling_locations, attention_weights):
"""
Args:
value (Tensor): [bs, value_length, n_head, c]
value_spatial_shapes (Tensor): [n_levels, 2]
sampling_locations (Tensor): [bs, query_length, n_head, n_levels, n_points, 2]
attention_weights (Tensor): [bs, query_length, n_head, n_levels, n_points]
Returns:
output (Tensor): [bs, Length_{query}, C]
"""
bs, Len_v, n_head, c = value.shape
_, Len_q, n_head, n_levels, n_points, _ = sampling_locations.shape
value_list = value.split(value_spatial_shapes.prod(1).tolist(), axis=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for level, (h, w) in enumerate(value_spatial_shapes.tolist()):
# N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
value_l_ = value_list[level].flatten(2).transpose(
[0, 2, 1]).reshape([bs * n_head, c, h, w])
# N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(
[0, 2, 1, 3, 4]).flatten(0, 1)
# N_*M_, D_, Lq_, P_
sampling_value_l_ = F.grid_sample(
value_l_,
sampling_grid_l_,
mode='bilinear',
padding_mode='zeros',
align_corners=False)
sampling_value_list.append(sampling_value_l_)
# (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_*M_, 1, Lq_, L_*P_)
attention_weights = attention_weights.transpose([0, 2, 1, 3, 4]).reshape(
[bs * n_head, 1, Len_q, n_levels * n_points])
output = (paddle.stack(
sampling_value_list, axis=-2).flatten(-2) *
attention_weights).sum(-1).reshape([bs, n_head * c, Len_q])
return output.transpose([0, 2, 1])
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