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import torch
import torch.nn as nn
from .lsnet import LSNet, Conv2d_BN, BN_Linear
from timm.models import register_model
from timm.models import build_model_with_cfg
class LSNetArtist(LSNet):
def __init__(self,
img_size=224,
patch_size=8,
in_chans=3,
num_classes=1000,
embed_dim=[64, 128, 256, 384],
key_dim=[16, 16, 16, 16],
depth=[0, 2, 8, 10],
num_heads=[3, 3, 3, 4],
distillation=False,
feature_dim=None, # 特征向量维度，默认为embed_dim[-1]
use_projection=True, # 是否使用projection层
**kwargs):
default_cfg = kwargs.pop('default_cfg', None)
pretrained_cfg = kwargs.pop('pretrained_cfg', None)
pretrained_cfg_overlay = kwargs.pop('pretrained_cfg_overlay', None)
super().__init__(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
num_classes=num_classes,
embed_dim=embed_dim,
key_dim=key_dim,
depth=depth,
num_heads=num_heads,
distillation=distillation,
default_cfg=default_cfg,
pretrained_cfg=pretrained_cfg,
pretrained_cfg_overlay=pretrained_cfg_overlay,
**kwargs
)
self.feature_dim = feature_dim if feature_dim is not None else embed_dim[-1]
self.use_projection = use_projection
# 如果使用projection层，添加一个映射层来生成固定维度的特征
if self.use_projection and self.feature_dim != embed_dim[-1]:
self.projection = nn.Sequential(
BN_Linear(embed_dim[-1], self.feature_dim),
nn.ReLU(),
)
else:
self.projection = nn.Identity()
# 重新定义分类头（基于特征维度）
if num_classes > 0:
self.head = BN_Linear(self.feature_dim, num_classes)
if distillation:
self.head_dist = BN_Linear(self.feature_dim, num_classes)
def forward_features(self, x):
"""
提取特征，不经过分类头
用于聚类或特征提取
"""
x = self.patch_embed(x)
x = self.blocks1(x)
x = self.blocks2(x)
x = self.blocks3(x)
x = self.blocks4(x)
x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
x = self.projection(x)
return x
def forward(self, x, return_features=False):
"""
x: 输入图像
return_features: 是否只返回特征向量（用于聚类）
False时返回分类logits（用于分类）
如果return_features=True: 返回特征向量 (batch_size, feature_dim)
如果return_features=False: 返回分类logits (batch_size, num_classes)
"""
features = self.forward_features(x)
if return_features:
# 返回特征向量用于聚类
return features
# 返回分类结果
if self.distillation:
x = self.head(features), self.head_dist(features)
if not self.training:
x = (x[0] + x[1]) / 2
else:
x = self.head(features)
return x
def get_features(self, x):
"""
提取特征向量
"""
return self.forward(x, return_features=True)
def classify(self, x):
"""
进行分类
"""
return self.forward(x, return_features=False)
def _cfg_artist(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'input_size': (3, 224, 224),
'pool_size': (4, 4),
'crop_pct': .9,
'interpolation': 'bicubic',
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'first_conv': 'patch_embed.0.c',
'classifier': ('head.linear', 'head_dist.linear'),
**kwargs
}
default_cfgs_artist = dict(
lsnet_t_artist = _cfg_artist(),
lsnet_s_artist = _cfg_artist(),
lsnet_b_artist = _cfg_artist(),
lsnet_l_artist = _cfg_artist(),
lsnet_xl_artist = _cfg_artist(),
)
def _create_lsnet_artist(variant, pretrained=False, **kwargs):
cfg = default_cfgs_artist.get(variant, None)
if cfg is not None:
kwargs.setdefault('default_cfg', cfg)
kwargs.setdefault('pretrained_cfg', cfg)
model = build_model_with_cfg(
LSNetArtist,
variant,
pretrained,
**kwargs,
)
return model
@register_model
def lsnet_t_artist(num_classes=1000, distillation=False, pretrained=False,
feature_dim=None, use_projection=True, **kwargs):
model = _create_lsnet_artist(
"lsnet_t_artist",
pretrained=pretrained,
num_classes=num_classes,
distillation=distillation,
img_size=224,
patch_size=8,
embed_dim=[64, 128, 256, 384],
depth=[0, 2, 8, 10],
num_heads=[3, 3, 3, 4],
feature_dim=feature_dim,
use_projection=use_projection,
**kwargs
)
return model
@register_model
def lsnet_s_artist(num_classes=1000, distillation=False, pretrained=False,
feature_dim=None, use_projection=True, **kwargs):
model = _create_lsnet_artist(
"lsnet_s_artist",
pretrained=pretrained,
num_classes=num_classes,
distillation=distillation,
img_size=224,
patch_size=8,
embed_dim=[96, 192, 320, 448],
depth=[1, 2, 8, 10],
num_heads=[3, 3, 3, 4],
feature_dim=feature_dim,
use_projection=use_projection,
**kwargs
)
return model
@register_model
def lsnet_b_artist(num_classes=1000, distillation=False, pretrained=False,
feature_dim=None, use_projection=True, **kwargs):
model = _create_lsnet_artist(
"lsnet_b_artist",
pretrained=pretrained,
num_classes=num_classes,
distillation=distillation,
img_size=224,
patch_size=8,
embed_dim=[128, 256, 384, 512],
depth=[4, 6, 8, 10],
num_heads=[3, 3, 3, 4],
feature_dim=feature_dim,
use_projection=use_projection,
**kwargs
)
return model
@register_model
def lsnet_l_artist(num_classes=1000, distillation=False, pretrained=False,
feature_dim=None, use_projection=True, **kwargs):
model = _create_lsnet_artist(
"lsnet_l_artist",
pretrained=pretrained,
num_classes=num_classes,
distillation=distillation,
img_size=224,
patch_size=8,
embed_dim=[160, 320, 480, 640],
depth=[6, 8, 12, 14],
num_heads=[4, 4, 4, 4],
feature_dim=feature_dim,
use_projection=use_projection,
**kwargs
)
return model
@register_model
def lsnet_xl_artist(num_classes=1000, distillation=False, pretrained=False,
feature_dim=None, use_projection=True, **kwargs):
model = _create_lsnet_artist(
"lsnet_xl_artist",
pretrained=pretrained,
num_classes=num_classes,
distillation=distillation,
img_size=224,
patch_size=8,
embed_dim=[192, 384, 576, 768],
depth=[8, 12, 16, 20],
num_heads=[6, 6, 6, 6],
feature_dim=feature_dim,
use_projection=use_projection,
**kwargs
)
return model
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