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