Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,182 Bytes
56ef371 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
from typing import Dict
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from detect_tools.upn import ENCODERS, build_encoder
from detect_tools.upn.models.utils import get_activation_fn, get_clones
from detect_tools.upn.ops.modules import MSDeformAttn
@ENCODERS.register_module()
class DeformableTransformerEncoderLayer(nn.Module):
"""Deformable Transformer Encoder Layer.
Args:
d_model (int): The dimension of keys/values/queries in
:class:`MultiheadAttention`.
d_ffn (int): The dimension of the feedforward network model.
dropout (float): Probability of an element to be zeroed.
activation (str): Activation function in the feedforward network.
'relu' and 'gelu' are supported.
n_levels (int): The number of levels in Multi-Scale Deformable Attention.
n_heads (int): Parallel attention heads.
n_points (int): Number of sampling points in Multi-Scale Deformable Attention.
add_channel_attention (bool): If True, add channel attention.
"""
def __init__(
self,
d_model: int = 256,
d_ffn: int = 1024,
dropout: float = 0.1,
activation: str = "relu",
n_levels: int = 4,
n_heads: int = 8,
n_points: int = 4,
add_channel_attention: bool = False,
) -> None:
super().__init__()
# self attention
self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = get_activation_fn(activation, d_model=d_ffn)
self.dropout2 = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_ffn, d_model)
self.dropout3 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
# channel attention
self.add_channel_attention = add_channel_attention
if add_channel_attention:
self.activ_channel = get_activation_fn("dyrelu", d_model=d_model)
self.norm_channel = nn.LayerNorm(d_model)
@staticmethod
def with_pos_embed(tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, src: torch.Tensor) -> torch.Tensor:
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: torch.Tensor,
pos: torch.Tensor,
reference_points: torch.Tensor,
spatial_shapes: torch.Tensor,
level_start_index: torch.Tensor,
key_padding_mask: torch.Tensor = None,
) -> torch.Tensor:
"""Forward function for `DeformableTransformerEncoderLayer`.
Args:
src (torch.Tensor): The input sequence of shape (S, N, E).
pos (torch.Tensor): The position embedding of shape (S, N, E).
reference_points (torch.Tensor): The reference points of shape (N, L, 2).
spatial_shapes (torch.Tensor): The spatial shapes of feature levels.
level_start_index (torch.Tensor): The start index of each level.
key_padding_mask (torch.Tensor): The mask for keys with shape (N, S).
"""
# self attention
# import ipdb; ipdb.set_trace()
src2 = self.self_attn(
self.with_pos_embed(src, pos),
reference_points,
src,
spatial_shapes,
level_start_index,
key_padding_mask,
)
src = src + self.dropout1(src2)
src = self.norm1(src)
# ffn
src = self.forward_ffn(src)
# channel attn
if self.add_channel_attention:
src = self.norm_channel(src + self.activ_channel(src))
return src
@ENCODERS.register_module()
class UPNEncoder(nn.Module):
"""Implementation of UPN Encoder.
Args:
num_layers (int): The number of layers in the TransformerEncoder.
d_model (int, optional): The dimension of the input feature. Defaults to 256.
encoder_layer_cfg (Dict): Config for the DeformableEncoderLayer.
use_checkpoint (bool, optional): Whether to use checkpoint in the fusion layer for
memory saving. Defaults to False.
use_transformer_ckpt (bool, optional): Whether to use checkpoint for the deformableencoder.
enc_layer_share (bool, optional): Whether to share the same memory for the encoder_layer.
Defaults to False. This is used for all the sub-layers in the basic block.
"""
def __init__(
self,
num_layers: int,
d_model: int = 256,
encoder_layer_cfg: Dict = None,
use_checkpoint: bool = True,
use_transformer_ckpt: bool = True,
enc_layer_share: bool = False,
multi_level_encoder_fusion: str = None,
):
super().__init__()
# prepare layers
self.layers = []
self.refImg_layers = []
self.fusion_layers = []
encoder_layer = build_encoder(encoder_layer_cfg)
self.multi_level_encoder_fusion = multi_level_encoder_fusion
self._initilize_memory_fusion_layers(
multi_level_encoder_fusion, num_layers, d_model
)
if num_layers > 0:
self.layers = get_clones(
encoder_layer, num_layers, layer_share=enc_layer_share
)
else:
self.layers = []
del encoder_layer
self.query_scale = None
self.num_layers = num_layers
self.d_model = d_model
self.use_checkpoint = use_checkpoint
self.use_transformer_ckpt = use_transformer_ckpt
def _initilize_memory_fusion_layers(self, fusion_type, num_layers, d_model):
if fusion_type is None:
self.memory_fusion_layer = None
return
assert fusion_type in ["dense_net_fusion", "stable_dense_fusion"]
if fusion_type == "stable_dense_fusion":
self.memory_fusion_layer = nn.Sequential(
nn.Linear(d_model * (num_layers + 1), d_model),
nn.LayerNorm(d_model),
)
nn.init.constant_(self.memory_fusion_layer[0].bias, 0)
elif fusion_type == "dense_net_fusion":
self.memory_fusion_layer = nn.ModuleList()
for i in range(num_layers):
self.memory_fusion_layer.append(
nn.Sequential(
nn.Linear(
d_model * (i + 2), d_model
), # from second encoder layer, 512 -> 256 / 3rd: 768 -> 256
nn.LayerNorm(d_model),
)
)
for layer in self.memory_fusion_layer:
nn.init.constant_(layer[0].bias, 0)
else:
raise NotImplementedError
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios, device):
reference_points_list = []
for lvl, (H_, W_) in enumerate(spatial_shapes):
ref_y, ref_x = torch.meshgrid(
torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
)
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def forward(
self,
src: torch.Tensor,
pos: torch.Tensor,
spatial_shapes: torch.Tensor,
level_start_index: torch.Tensor,
valid_ratios: torch.Tensor,
key_padding_mask: torch.Tensor = None,
):
"""Forward function
Args:
src (torch.Tensor): Flattened Image features in shape [bs, sum(hi*wi), 256]
pos (torch.Tensor): Position embedding for image feature in shape [bs, sum(hi*wi), 256]
spatial_shapes (torch.Tensor): Spatial shape of each level in shape [num_level, 2]
level_start_index (torch.Tensor): Start index of each level in shape [num_level]
valid_ratios (torch.Tensor): Valid ratio of each level in shape [bs, num_level, 2]
key_padding_mask (torch.Tensor): Padding mask for image feature in shape [bs, sum(hi*wi)]
memory_refImg (torch.Tensor, optional): Text feature in shape [bs, n_ref, 256]. Defaults
to None.
refImg_padding_mask (torch.Tensor, optional): Padding mask for reference image feature
in shape [bs, n_text]. Defaults to None.
pos_refImg (torch.Tensor, optional): Position embedding for reference image in shape
[bs, n_ref, 256]. Defaults to None.
refImg_self_attention_masks (torch.Tensor, optional): Self attention mask for reference
image feature in shape [bs, n_ref, n_ref]. Defaults to None.
Outpus:
torch.Tensor: Encoded image feature in shape [bs, sum(hi*wi), 256]
torch.Tensor: Encoded reference image feature in shape [bs, n_ref, 256]
"""
output = src
# preparation and reshape
if self.num_layers > 0:
reference_points = self.get_reference_points(
spatial_shapes, valid_ratios, device=src.device
)
# multi-level dense fusion
output_list = [output]
# main process
for layer_id, layer in enumerate(self.layers):
# main process
if self.use_transformer_ckpt:
output = checkpoint.checkpoint(
layer,
output,
pos,
reference_points,
spatial_shapes,
level_start_index,
key_padding_mask,
)
else:
output = layer(
src=output,
pos=pos,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
key_padding_mask=key_padding_mask,
)
output_list.append(output)
if (
self.multi_level_encoder_fusion is not None
and self.multi_level_encoder_fusion == "dense_net_fusion"
):
output = self.memory_fusion_layer[layer_id](
torch.cat(output_list, dim=-1)
)
if (
self.multi_level_encoder_fusion is not None
and self.multi_level_encoder_fusion == "stable_dense_fusion"
):
output = self.memory_fusion_layer(torch.cat(output_list, dim=-1))
return output
|