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pytorch-image-models/timm/models/cait.py

400 lines
15 KiB

""" Class-Attention in Image Transformers (CaiT)
Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
Original code and weights from https://github.com/facebookresearch/deit, copyright below
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
from functools import partial
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
from .registry import register_model
__all__ = ['Cait', 'ClassAttn', 'LayerScaleBlockClassAttn', 'LayerScaleBlock', 'TalkingHeadAttn']
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 384, 384), 'pool_size': None,
'crop_pct': 1.0, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = dict(
cait_xxs24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS24_224.pth',
input_size=(3, 224, 224),
),
cait_xxs24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS24_384.pth',
),
cait_xxs36_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS36_224.pth',
input_size=(3, 224, 224),
),
cait_xxs36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XXS36_384.pth',
),
cait_xs24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/XS24_384.pth',
),
cait_s24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S24_224.pth',
input_size=(3, 224, 224),
),
cait_s24_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S24_384.pth',
),
cait_s36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/S36_384.pth',
),
cait_m36_384=_cfg(
url='https://dl.fbaipublicfiles.com/deit/M36_384.pth',
),
cait_m48_448=_cfg(
url='https://dl.fbaipublicfiles.com/deit/M48_448.pth',
input_size=(3, 448, 448),
),
)
class ClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to do CA
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.k = nn.Linear(dim, dim, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
q = q * self.scale
v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
x_cls = self.proj(x_cls)
x_cls = self.proj_drop(x_cls)
return x_cls
class LayerScaleBlockClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add CA and LayerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=ClassAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_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_cls):
u = torch.cat((x_cls, x), dim=1)
x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
return x_cls
class TalkingHeadAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_l = nn.Linear(num_heads, num_heads)
self.proj_w = nn.Linear(num_heads, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1))
attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = attn.softmax(dim=-1)
attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScaleBlock(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add layerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_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)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class Cait(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to adapt to our cait models
def __init__(
self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
global_pool=None,
block_layers=LayerScaleBlock,
block_layers_token=LayerScaleBlockClassAttn,
patch_layer=PatchEmbed,
act_layer=nn.GELU,
attn_block=TalkingHeadAttn,
mlp_block=Mlp,
init_scale=1e-4,
attn_block_token_only=ClassAttn,
mlp_block_token_only=Mlp,
depth_token_only=2,
mlp_ratio_clstk=4.0
):
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=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
block_layers(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
act_layer=act_layer, attn_block=attn_block, mlp_block=mlp_block, init_values=init_scale)
for i in range(depth)])
self.blocks_token_only = nn.ModuleList([
block_layers_token(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio_clstk, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=norm_layer,
act_layer=act_layer, attn_block=attn_block_token_only,
mlp_block=mlp_block_token_only, init_values=init_scale)
for i in range(depth_token_only)])
self.norm = norm_layer(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()
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.apply(self._init_weights)
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)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = x + self.pos_embed
x = self.pos_drop(x)
for i, blk in enumerate(self.blocks):
x = blk(x)
for i, blk in enumerate(self.blocks_token_only):
cls_tokens = blk(x, cls_tokens)
x = torch.cat((cls_tokens, x), dim=1)
x = self.norm(x)
return x[:, 0]
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def checkpoint_filter_fn(state_dict, model=None):
if 'model' in state_dict:
state_dict = state_dict['model']
checkpoint_no_module = {}
for k, v in state_dict.items():
checkpoint_no_module[k.replace('module.', '')] = v
return checkpoint_no_module
def _create_cait(variant, pretrained=False, default_cfg=None, **kwargs):
if default_cfg is None:
default_cfg = deepcopy(default_cfgs[variant])
overlay_external_default_cfg(default_cfg, kwargs)
default_num_classes = default_cfg['num_classes']
default_img_size = default_cfg['input_size'][-2:]
num_classes = kwargs.pop('num_classes', default_num_classes)
img_size = kwargs.pop('img_size', default_img_size)
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(
Cait, variant, pretrained,
default_cfg=default_cfg,
img_size=img_size,
num_classes=num_classes,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@register_model
def cait_xxs24_224(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs24_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs36_224(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs36_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xxs36_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xxs36_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_xs24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=288, depth=24, num_heads=6, init_scale=1e-5, **kwargs)
model = _create_cait('cait_xs24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s24_224(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
model = _create_cait('cait_s24_224', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s24_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
model = _create_cait('cait_s24_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_s36_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=384, depth=36, num_heads=8, init_scale=1e-6, **kwargs)
model = _create_cait('cait_s36_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_m36_384(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=768, depth=36, num_heads=16, init_scale=1e-6, **kwargs)
model = _create_cait('cait_m36_384', pretrained=pretrained, **model_args)
return model
@register_model
def cait_m48_448(pretrained=False, **kwargs):
model_args = dict(patch_size=16, embed_dim=768, depth=48, num_heads=16, init_scale=1e-6, **kwargs)
model = _create_cait('cait_m48_448', pretrained=pretrained, **model_args)
return model