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resmlp_models.py
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resmlp_models.py
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# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial
from timm.models.vision_transformer import Mlp, PatchEmbed , _cfg
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, DropPath
__all__ = [
'resmlp_12', 'resmlp_24', 'resmlp_36', 'resmlpB_24'
]
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
def forward(self, x):
return self.alpha * x + self.beta
class layers_scale_mlp_blocks(nn.Module):
def __init__(self, dim, drop=0., drop_path=0., act_layer=nn.GELU,init_values=1e-4,num_patches = 196):
super().__init__()
self.norm1 = Affine(dim)
self.attn = nn.Linear(num_patches, num_patches)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = Affine(dim)
self.mlp = Mlp(in_features=dim, hidden_features=int(4.0 * dim), act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x).transpose(1,2)).transpose(1,2))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class resmlp_models(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,drop_rate=0.,
Patch_layer=PatchEmbed,act_layer=nn.GELU,
drop_path_rate=0.0,init_scale=1e-4):
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.patch_embed = Patch_layer(
img_size=img_size, patch_size=patch_size, in_chans=int(in_chans), embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
layers_scale_mlp_blocks(
dim=embed_dim,drop=drop_rate,drop_path=dpr[i],
act_layer=act_layer,init_values=init_scale,
num_patches=num_patches)
for i in range(depth)])
self.norm = Affine(embed_dim)
self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
for i , blk in enumerate(self.blocks):
x = blk(x)
x = self.norm(x)
x = x.mean(dim=1).reshape(B,1,-1)
return x[:, 0]
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
@register_model
def resmlp_12(pretrained=False,dist=False, **kwargs):
model = resmlp_models(
patch_size=16, embed_dim=384, depth=12,
Patch_layer=PatchEmbed,
init_scale=0.1,**kwargs)
model.default_cfg = _cfg()
if pretrained:
if dist:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth"
else:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth"
checkpoint = torch.hub.load_state_dict_from_url(
url=url_path,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint)
return model
@register_model
def resmlp_24(pretrained=False,dist=False,dino=False, **kwargs):
model = resmlp_models(
patch_size=16, embed_dim=384, depth=24,
Patch_layer=PatchEmbed,
init_scale=1e-5,**kwargs)
model.default_cfg = _cfg()
if pretrained:
if dist:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth"
elif dino:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth"
else:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth"
checkpoint = torch.hub.load_state_dict_from_url(
url=url_path,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint)
return model
@register_model
def resmlp_36(pretrained=False,dist=False, **kwargs):
model = resmlp_models(
patch_size=16, embed_dim=384, depth=36,
Patch_layer=PatchEmbed,
init_scale=1e-6,**kwargs)
model.default_cfg = _cfg()
if pretrained:
if dist:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth"
else:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth"
checkpoint = torch.hub.load_state_dict_from_url(
url=url_path,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint)
return model
@register_model
def resmlpB_24(pretrained=False,dist=False, in_22k = False, **kwargs):
model = resmlp_models(
patch_size=8, embed_dim=768, depth=24,
Patch_layer=PatchEmbed,
init_scale=1e-6,**kwargs)
model.default_cfg = _cfg()
if pretrained:
if dist:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth"
elif in_22k:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth"
else:
url_path = "https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth"
checkpoint = torch.hub.load_state_dict_from_url(
url=url_path,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint)
return model