# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Implementation of Cross-Covariance Image Transformer (XCiT)
Based on timm and DeiT code bases
https://github.com/rwightman/pytorch-image-models/tree/master/timm
https://github.com/facebookresearch/deit/
XCiT Transformer.
Part of the code is borrowed from:
https://github.com/facebookresearch/xcit/blob/master/xcit.py
"""
import math
from functools import partial
import torch
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.vision_transformer import Mlp, _cfg
from easycv.framework.errors import ValueError
from ..registry import BACKBONES
[docs]class PositionalEncodingFourier(nn.Module):
"""
Positional encoding relying on a fourier kernel matching the one used in the
"Attention is all of Need" paper. The implementation builds on DeTR code
https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
"""
[docs] def __init__(self, hidden_dim=32, dim=768, temperature=10000):
super().__init__()
self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
self.scale = 2 * math.pi
self.temperature = temperature
self.hidden_dim = hidden_dim
self.dim = dim
[docs] def forward(self, B, H, W):
mask = torch.zeros(B, H,
W).bool().to(self.token_projection.weight.device)
not_mask = ~mask
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(
self.hidden_dim, dtype=torch.float32, device=mask.device)
dim_t = self.temperature**(2 * (dim_t // 2) / self.hidden_dim)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
pos = self.token_projection(pos)
return pos
[docs]def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return torch.nn.Sequential(
nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False), nn.SyncBatchNorm(out_planes))
[docs]class ConvPatchEmbed(nn.Module):
""" Image to Patch Embedding using multiple convolutional layers
"""
[docs] def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (
img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
if patch_size[0] == 16:
self.proj = torch.nn.Sequential(
conv3x3(3, embed_dim // 8, 2),
nn.GELU(),
conv3x3(embed_dim // 8, embed_dim // 4, 2),
nn.GELU(),
conv3x3(embed_dim // 4, embed_dim // 2, 2),
nn.GELU(),
conv3x3(embed_dim // 2, embed_dim, 2),
)
elif patch_size[0] == 8:
self.proj = torch.nn.Sequential(
conv3x3(3, embed_dim // 4, 2),
nn.GELU(),
conv3x3(embed_dim // 4, embed_dim // 2, 2),
nn.GELU(),
conv3x3(embed_dim // 2, embed_dim, 2),
)
else:
raise ValueError(
'For convolutional projection, patch size has to be in [8, 16]'
)
[docs] def forward(self, x, padding_size=None):
B, C, H, W = x.shape
x = self.proj(x)
Hp, Wp = x.shape[2], x.shape[3]
x = x.flatten(2).transpose(1, 2)
return x, (Hp, Wp)
[docs]class LPI(nn.Module):
"""
Local Patch Interaction module that allows explicit communication between tokens in 3x3 windows
to augment the implicit communcation performed by the block diagonal scatter attention.
Implemented using 2 layers of separable 3x3 convolutions with GeLU and BatchNorm2d
"""
[docs] def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
kernel_size=3):
super().__init__()
out_features = out_features or in_features
padding = kernel_size // 2
self.conv1 = torch.nn.Conv2d(
in_features,
out_features,
kernel_size=kernel_size,
padding=padding,
groups=out_features)
self.act = act_layer()
self.bn = nn.SyncBatchNorm(in_features)
self.conv2 = torch.nn.Conv2d(
in_features,
out_features,
kernel_size=kernel_size,
padding=padding,
groups=out_features)
[docs] def forward(self, x, H, W):
B, N, C = x.shape
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.conv1(x)
x = self.act(x)
x = self.bn(x)
x = self.conv2(x)
x = x.reshape(B, C, N).permute(0, 2, 1)
return x
[docs]class ClassAttention(nn.Module):
"""Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239
"""
[docs] 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_drop = nn.Dropout(proj_drop)
[docs] def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
qkv = qkv.permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[
2] # make torchscript happy (cannot use tensor as tuple)
qc = q[:, :, 0:1] # CLS token
attn_cls = (qc * k).sum(dim=-1) * self.scale
attn_cls = attn_cls.softmax(dim=-1)
attn_cls = self.attn_drop(attn_cls)
cls_tkn = (attn_cls.unsqueeze(2) @ v).transpose(1, 2).reshape(B, 1, C)
cls_tkn = self.proj(cls_tkn)
x = torch.cat([self.proj_drop(cls_tkn), x[:, 1:]], dim=1)
return x
[docs]class ClassAttentionBlock(nn.Module):
"""Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239
"""
[docs] 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,
eta=None,
tokens_norm=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = ClassAttention(
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(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
if eta is not None: # LayerScale Initialization (no layerscale when None)
self.gamma1 = nn.Parameter(
eta * torch.ones(dim), requires_grad=True)
self.gamma2 = nn.Parameter(
eta * torch.ones(dim), requires_grad=True)
else:
self.gamma1, self.gamma2 = 1.0, 1.0
# FIXME: A hack for models pre-trained with layernorm over all the tokens not just the CLS
self.tokens_norm = tokens_norm
[docs] def forward(self, x, H, W, mask=None):
x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x)))
if self.tokens_norm:
x = self.norm2(x)
else:
x[:, 0:1] = self.norm2(x[:, 0:1])
x_res = x
cls_token = x[:, 0:1]
cls_token = self.gamma2 * self.mlp(cls_token)
x = torch.cat([cls_token, x[:, 1:]], dim=1)
x = x_res + self.drop_path(x)
return x
[docs]class XCA(nn.Module):
""" Cross-Covariance Attention (XCA) operation where the channels are updated using a weighted \
sum.
The weights are obtained from the (softmax normalized) Cross-covariance matrix
(Q^T K \\in d_h \\times d_h)
"""
[docs] 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
self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
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_drop = nn.Dropout(proj_drop)
[docs] def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
qkv = qkv.permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[
2] # make torchscript happy (cannot use tensor as tuple)
q = q.transpose(-2, -1)
k = k.transpose(-2, -1)
v = v.transpose(-2, -1)
q = torch.nn.functional.normalize(q, dim=-1)
k = torch.nn.functional.normalize(k, dim=-1)
attn = (q @ k.transpose(-2, -1)) * self.temperature
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
[docs] @torch.jit.ignore
def no_weight_decay(self):
return {'temperature'}
[docs]class XCABlock(nn.Module):
[docs] 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,
num_tokens=196,
eta=None):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = XCA(
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(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
self.norm3 = norm_layer(dim)
self.local_mp = LPI(in_features=dim, act_layer=act_layer)
self.gamma1 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
self.gamma2 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
self.gamma3 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
[docs] def forward(self, x, H, W):
x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x)))
x = x + self.drop_path(
self.gamma3 * self.local_mp(self.norm3(x), H, W))
x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
return x
[docs]@BACKBONES.register_module
class XCiT(nn.Module):
"""
Based on timm and DeiT code bases
https://github.com/rwightman/pytorch-image-models/tree/master/timm
https://github.com/facebookresearch/deit/
"""
[docs] 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=None,
cls_attn_layers=2,
use_pos=True,
patch_proj='linear',
eta=None,
tokens_norm=False):
"""
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer
cls_attn_layers: (int) Depth of Class attention layers
use_pos: (bool) whether to use positional encoding
eta: (float) layerscale initialization value
tokens_norm: (bool) Whether to normalize all tokens or just the cls_token in the CA
"""
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
self.patch_embed = ConvPatchEmbed(
img_size=img_size, embed_dim=embed_dim, patch_size=patch_size)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
XCABlock(
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,
num_tokens=num_patches,
eta=eta) for i in range(depth)
])
self.cls_attn_blocks = nn.ModuleList([
ClassAttentionBlock(
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,
norm_layer=norm_layer,
eta=eta,
tokens_norm=tokens_norm) for i in range(cls_attn_layers)
])
self.norm = norm_layer(embed_dim)
self.head = nn.Linear(
self.num_features,
num_classes) if num_classes > 0 else nn.Identity()
self.pos_embeder = PositionalEncodingFourier(dim=embed_dim)
self.use_pos = use_pos
# Classifier head
trunc_normal_(self.cls_token, std=.02)
[docs] def init_weights(self):
for m in self.modules():
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)
[docs] @torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token', 'dist_token'}
[docs] def forward_features(self, x):
B, C, H, W = x.shape
x, (Hp, Wp) = self.patch_embed(x)
if self.use_pos:
pos_encoding = self.pos_embeder(B, Hp, Wp).reshape(
B, -1, x.shape[1]).permute(0, 2, 1)
x = x + pos_encoding
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x, Hp, Wp)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
for blk in self.cls_attn_blocks:
x = blk(x, Hp, Wp)
x = self.norm(x)[:, 0]
return x
[docs] def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
if self.training:
return x, x
else:
return x
# # Patch size 16x16 models
# @register_model
# def xcit_nano_12_p16(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=16, embed_dim=128, depth=12, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1.0, tokens_norm=False, **kwargs)
# model.default_cfg = _cfg()
# return model
# @register_model
# def xcit_tiny_12_p16(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=16, embed_dim=192, depth=12, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1.0, tokens_norm=True, **kwargs)
# model.default_cfg = _cfg()
# return model
# @register_model
[docs]def xcit_small_12_p16(pretrained=False, **kwargs):
model = XCiT(
patch_size=16,
embed_dim=384,
depth=12,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1.0,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
# @register_model
# def xcit_tiny_24_p16(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1e-5, tokens_norm=True, **kwargs)
# model.default_cfg = _cfg()
# return model
[docs]def xcit_small_24_p16(pretrained=False, **kwargs):
model = XCiT(
patch_size=16,
embed_dim=384,
depth=24,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1e-5,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
[docs]def xcit_medium_24_p16(pretrained=False, **kwargs):
model = XCiT(
patch_size=16,
embed_dim=512,
depth=24,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1e-5,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
# @register_model
# def xcit_large_24_p16(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=16, embed_dim=768, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1e-5, tokens_norm=True, **kwargs)
# model.default_cfg = _cfg()
# return model
# # Patch size 8x8 models
# @register_model
# def xcit_nano_12_p8(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=8, embed_dim=128, depth=12, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1.0, tokens_norm=False, **kwargs)
# model.default_cfg = _cfg()
# return model
# @register_model
# def xcit_tiny_12_p8(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=8, embed_dim=192, depth=12, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1.0, tokens_norm=True, **kwargs)
# model.default_cfg = _cfg()
# return model
# @register_model
[docs]def xcit_small_12_p8(pretrained=False, **kwargs):
model = XCiT(
patch_size=8,
embed_dim=384,
depth=12,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1.0,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
# @register_model
# def xcit_tiny_24_p8(pretrained=False, **kwargs):
# model = XCiT(
# patch_size=8, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
# norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1e-5, tokens_norm=True, **kwargs)
# model.default_cfg = _cfg()
# return model
[docs]def xcit_small_24_p8(pretrained=False, **kwargs):
model = XCiT(
patch_size=8,
embed_dim=384,
depth=24,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1e-5,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
[docs]def xcit_medium_24_p8(pretrained=False, **kwargs):
model = XCiT(
patch_size=8,
embed_dim=512,
depth=24,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1e-5,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model
# @register_model
[docs]def xcit_large_24_p8(pretrained=False, **kwargs):
model = XCiT(
patch_size=8,
embed_dim=768,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
eta=1e-5,
tokens_norm=True,
**kwargs)
model.default_cfg = _cfg()
return model