ViT pytorch源码笔记
文章目录
- 链接
- patch embedding
- 注意力机制
- encoder的block
- Transformer组装
- pos embedding的插值
链接
源码地址
本文只列出了一些比较重要的部分。
patch embedding
先将大小为224 × \times × 224 × \times × 3的图像分割成16 × \times × 16 × \times × 3的patches，再展开做线性映射将每个patches的维度变为768。
""" Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on the impl in https://github.com/google-research/vision_transformer
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .helpers import to_2tuple
from .trace_utils import _assert
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
bias=True,
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
# 原图像大小为(224, 224, 3)
self.img_size = img_size
# 每个patch的大小为(16, 16, 3)
self.patch_size = patch_size
# 分割后总共有 (224/16)*(224/16)=14*14=196个patches
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
# patches分割完要展开成一维的
self.flatten = flatten
# 分割成patches并做patch embedding的线性映射：用大小和步长都为patch_size(16*16)的卷积核来做
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
# 原图形状：(B, 3, 224, 224)
B, C, H, W = x.shape
_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
# 分割后线性映射的形状为：(B, 768, 14, 14)
x = self.proj(x)
# 展开后转置变成：(B, 14*14, 768)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
注意力机制
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
# dim是所有头合起来的维度即dim=768，分给每个头就是维度为768/12=64
head_dim = dim // num_heads
# scale是计算attention scores的根号d_k
self.scale = 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)
def forward(self, x):
B, N, C = x.shape
# qkv reshape后形状为：(B, 序列长度=N=14*14+1, 3, head数目=12, 768/12=64)
# 序列长度有个 +1 是加上了 class token
# permute后形状：(3, B, head数=12, 序列长度=N=14*14+1, 768/12=64)
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
# q,k,v形状分别为：(B, head数=12, N=14*14+1, 768/12=64)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
# 计算完attn形状：(B, head数=12, N=14*14+1, N=14*14+1)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
# attn与V相乘转置后形状：(B, N=14*14+1, head=12, 768/12)
# 把12个头的都拼在一起reshape后：(B, N=14*14+1, 768)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
# 再做一个维度不变的线性映射W_o：(B,N=14*14+1,768)
x = self.proj(x)
x = self.proj_drop(x)
return x
encoder的block
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
init_values=None,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm
):
super().__init__()
# 先做一次layerNorm
self.norm1 = norm_layer(dim)
# 计算attention
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
# 别的文章提出的改进方法：layerscale
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
# dropout
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# 再做一次layerNorm
self.norm2 = norm_layer(dim)
# 做MLP，由两个全连接层和一个激活函数组成，先将维度768扩大4倍，再变回768
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
# layerscale
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
# dropout
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
Transformer组装
class VisionTransformer(nn.Module):
""" Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=True,
init_values=None,
class_token=True,
no_embed_class=False,
pre_norm=False,
fc_norm=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
weight_init='',
embed_layer=PatchEmbed,
norm_layer=None,
act_layer=None,
block_fn=Block,
):
"""
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
global_pool (str): type of global pooling for final sequence (default: 'token')
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
init_values: (float): layer-scale init values
class_token (bool): use class token
fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
weight_init (str): weight init scheme
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
act_layer: (nn.Module): MLP activation layer
"""
super().__init__()
# 选择最后分类用cls token或者全局平均池化
assert global_pool in ('', 'avg', 'token')
assert class_token or global_pool != 'token'
use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.num_classes = num_classes
self.global_pool = global_pool
# num_features, embed_dim都是768
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
# 如果使用cls token进行分类，原始序列前面就要加上1个cls token
self.num_prefix_tokens = 1 if class_token else 0
self.no_embed_class = no_embed_class
self.grad_checkpointing = False
# 进行patch embdding的
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
)
num_patches = self.patch_embed.num_patches
# cls token形状为(1,1,768)，为可学习的参数
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
# 序列长度等于patches数加上cls token数
embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
# pos_embedding也使用可学习的参数
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
self.pos_drop = nn.Dropout(p=drop_rate)
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
# depth默认为12，每个encoder有12个blocks
self.blocks = nn.Sequential(*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
init_values=init_values,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer
)
for i in range(depth)])
self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# Classifier Head
# 分类最后是对 cls token对应的输出 或者 global average pooling的输出 用一个线性映射完成的
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode, head_bias), self)
def _init_weights(self, m):
# this fn left here for compat with downstream users
init_weights_vit_timm(m)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix=''):
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token', 'dist_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes: int, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def _pos_embed(self, x):
# 可以选择是否对cls token做pos_embedding
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
x = x + self.pos_embed
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
x = x + self.pos_embed
return self.pos_drop(x)
def forward_features(self, x):
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.norm_pre(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
# 如果global_pool=True，则使用全局平均池化的输出进行分类，否则使用encoder的输出的第一个向量进行分类
if self.global_pool:
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
pos embedding的插值
def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()):
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
# ntok_new是新的序列的长度
ntok_new = posemb_new.shape[1]
if num_prefix_tokens:
# 如果有加入cls token，把cls token与原序列分开处理
posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
ntok_new -= num_prefix_tokens
else:
posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
# 原序列长度为14*14，则原始patches组成的正方形边长gs_old为14
gs_old = int(math.sqrt(len(posemb_grid)))
# 新的序列长度对应的patches组成的正方形边长为 gs_new开根号
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
_logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
# pos embedding变回二维形状：(1, 14, 14, 768)
# permute后：(1, 768, 14, 14)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
# 进行2D插值：(1, 768, gs_new, gs_new)
posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
# 重新变回pos embedding应有的形状：(1, ntok_new/新序列长度，768)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
# 如果有cls token，将cls token和新序列拼接
posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
return posemb