File size: 9,033 Bytes
5e1f805 |
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 |
import torch
import torch.nn as nn
import utils
from utils import trunc_normal_
class CSyncBatchNorm(nn.SyncBatchNorm):
def __init__(self,
*args,
with_var=False,
**kwargs):
super(CSyncBatchNorm, self).__init__(*args, **kwargs)
self.with_var = with_var
def forward(self, x):
# center norm
self.training = False
if not self.with_var:
self.running_var = torch.ones_like(self.running_var)
normed_x = super(CSyncBatchNorm, self).forward(x)
# udpate center
self.training = True
_ = super(CSyncBatchNorm, self).forward(x)
return normed_x
class PSyncBatchNorm(nn.SyncBatchNorm):
def __init__(self,
*args,
bunch_size,
**kwargs):
procs_per_bunch = min(bunch_size, utils.get_world_size())
assert utils.get_world_size() % procs_per_bunch == 0
n_bunch = utils.get_world_size() // procs_per_bunch
#
ranks = list(range(utils.get_world_size()))
print('---ALL RANKS----\n{}'.format(ranks))
rank_groups = [ranks[i*procs_per_bunch: (i+1)*procs_per_bunch] for i in range(n_bunch)]
print('---RANK GROUPS----\n{}'.format(rank_groups))
process_groups = [torch.distributed.new_group(pids) for pids in rank_groups]
bunch_id = utils.get_rank() // procs_per_bunch
process_group = process_groups[bunch_id]
print('---CURRENT GROUP----\n{}'.format(process_group))
super(PSyncBatchNorm, self).__init__(*args, process_group=process_group, **kwargs)
class CustomSequential(nn.Sequential):
bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
def forward(self, input):
for module in self:
dim = len(input.shape)
if isinstance(module, self.bn_types) and dim > 2:
perm = list(range(dim - 1)); perm.insert(1, dim - 1)
inv_perm = list(range(dim)) + [1]; inv_perm.pop(1)
input = module(input.permute(*perm)).permute(*inv_perm)
else:
input = module(input)
return input
class DINOHead(nn.Module):
def __init__(self, in_dim, out_dim, norm=None, act='gelu', last_norm=None,
nlayers=3, hidden_dim=2048, bottleneck_dim=256, norm_last_layer=True, **kwargs):
super().__init__()
norm = self._build_norm(norm, hidden_dim)
last_norm = self._build_norm(last_norm, out_dim, affine=False, **kwargs)
act = self._build_act(act)
nlayers = max(nlayers, 1)
if nlayers == 1:
if bottleneck_dim > 0:
self.mlp = nn.Linear(in_dim, bottleneck_dim)
else:
self.mlp = nn.Linear(in_dim, out_dim)
else:
layers = [nn.Linear(in_dim, hidden_dim)]
if norm is not None:
layers.append(norm)
layers.append(act)
for _ in range(nlayers - 2):
layers.append(nn.Linear(hidden_dim, hidden_dim))
if norm is not None:
layers.append(norm)
layers.append(act)
if bottleneck_dim > 0:
layers.append(nn.Linear(hidden_dim, bottleneck_dim))
else:
layers.append(nn.Linear(hidden_dim, out_dim))
self.mlp = CustomSequential(*layers)
self.apply(self._init_weights)
if bottleneck_dim > 0:
self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
self.last_layer.weight_g.data.fill_(1)
if norm_last_layer:
self.last_layer.weight_g.requires_grad = False
else:
self.last_layer = None
self.last_norm = last_norm
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.mlp(x)
if self.last_layer is not None:
x = nn.functional.normalize(x, dim=-1, p=2)
x = self.last_layer(x)
if self.last_norm is not None:
x = self.last_norm(x)
return x
def _build_norm(self, norm, hidden_dim, **kwargs):
if norm == 'bn':
norm = nn.BatchNorm1d(hidden_dim, **kwargs)
elif norm == 'syncbn':
norm = nn.SyncBatchNorm(hidden_dim, **kwargs)
elif norm == 'csyncbn':
norm = CSyncBatchNorm(hidden_dim, **kwargs)
elif norm == 'psyncbn':
norm = PSyncBatchNorm(hidden_dim, **kwargs)
elif norm == 'ln':
norm = nn.LayerNorm(hidden_dim, **kwargs)
else:
assert norm is None, "unknown norm type {}".format(norm)
return norm
def _build_act(self, act):
if act == 'relu':
act = nn.ReLU()
elif act == 'gelu':
act = nn.GELU()
else:
assert False, "unknown act type {}".format(act)
return act
class iBOTHead(DINOHead):
def __init__(self, *args, patch_out_dim=8192, norm=None, act='gelu', last_norm=None,
nlayers=3, hidden_dim=2048, bottleneck_dim=256, norm_last_layer=True,
shared_head=False, **kwargs):
super(iBOTHead, self).__init__(*args,
norm=norm,
act=act,
last_norm=last_norm,
nlayers=nlayers,
hidden_dim=hidden_dim,
bottleneck_dim=bottleneck_dim,
norm_last_layer=norm_last_layer,
**kwargs)
if not shared_head:
if bottleneck_dim > 0:
self.last_layer2 = nn.utils.weight_norm(nn.Linear(bottleneck_dim, patch_out_dim, bias=False))
self.last_layer2.weight_g.data.fill_(1)
if norm_last_layer:
self.last_layer2.weight_g.requires_grad = False
else:
self.mlp2 = nn.Linear(hidden_dim, patch_out_dim)
self.last_layer2 = None
self.last_norm2 = self._build_norm(last_norm, patch_out_dim, affine=False, **kwargs)
else:
if bottleneck_dim > 0:
self.last_layer2 = self.last_layer
else:
self.mlp2 = self.mlp[-1]
self.last_layer2 = None
self.last_norm2 = self.last_norm
def forward(self, x):
if len(x.shape) == 2:
return super(iBOTHead, self).forward(x)
if self.last_layer is not None:
x = self.mlp(x)
x = nn.functional.normalize(x, dim=-1, p=2)
x1 = self.last_layer(x[:, 0])
x2 = self.last_layer2(x[:, 1:])
else:
x = self.mlp[:-1](x)
x1 = self.mlp[-1](x[:, 0])
x2 = self.mlp2(x[:, 1:])
if self.last_norm is not None:
x1 = self.last_norm(x1)
x2 = self.last_norm2(x2)
return x1, x2
class TemporalSideContext(nn.Module):
def __init__(self, D, max_len=64, n_layers=6, n_head=8, dropout=0.1):
super().__init__()
#self.pos_t = nn.Embedding(max_len, D) # learnable embedding for positions
layer = nn.TransformerEncoderLayer(D, n_head, 4*D,
dropout=dropout, batch_first=True)
self.enc = nn.TransformerEncoder(layer, n_layers)
def forward(self, x): # x [B,T,D]
B,T,D = x.shape
device = x.device
# Generate relative frame positions [0, 1, ..., T-1]
#pos_ids = torch.arange(T, device=device).unsqueeze(0) # [1, T]
#pos_embed = self.pos_t(pos_ids) # [1, T, D]
#x = x + pos_embed
return self.enc(x) # [B,T,D]
class TemporalHead(nn.Module):
"""
Converts backbone features [B,T,D] → logits [B,T,1] for Plackett–Luce.
"""
def __init__(self, backbone_dim: int, hidden_mul: float = 0.5, max_len: int = 64):
super().__init__()
hidden_dim = int(backbone_dim * hidden_mul)
self.reduce = nn.Sequential(
nn.Linear(backbone_dim, hidden_dim),
nn.GELU()
)
self.temporal = TemporalSideContext(hidden_dim, max_len=max_len)
self.scorer = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim // 2),
nn.GELU(),
nn.Linear(hidden_dim // 2, 1)
)
def forward(self, x: torch.Tensor): # x : [B,T,D]
x = self.reduce(x) # [B,T,hidden]
x = self.temporal(x) # [B,T,hidden]
return self.scorer(x) # [B,T,1]
|