Video classification with UniFormer基于统一分类器的视频分类
本文主要介绍了UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
代码：https://github.com/Sense-X/UniFormer/tree/main/video_classification
UNIFormer
动机
由于视频具有大量的局部冗余和复杂的全局依赖关系，因此从视频中学习丰富的、多尺度的时空语义是一项具有挑战性的任务。
最近的研究主要是由三维卷积神经网络和Vision Transformer驱动的。虽然三维卷积可以有效地聚集局部上下文来抑制来自小三维邻域的局部冗余，但由于感受域有限，它缺乏捕获全局依赖的能力。另外，vision Transformer通过自注意机制可以有效地捕获长时间依赖，但由于各层tokens之间存在盲目的相似性比较，限制了减少局部冗余。
视频transformer对浅层的局部特征编码比较低效，在时空上都只能学习到临近的信息
方法
提出了一种新型的统一Transformer(UniFormer)，它以一种简洁的形式，将三维卷积和时空自注意的优点集成在一起，并在计算和精度之间取得了较好的平衡。与传统的Transformer不同的是，关系聚合器通过在浅层和深层中分别局部和全局tokens相关性来处理时空冗余和依赖关系。
由上图可知，UniFormer模型其中的特色组件是：动态位置嵌入(DPE)、多头关系聚合器(MHRA)和前馈网络(FFN)
动态位置嵌入(DPE)
之前的方法主要采用图像任务的绝对或相对位置嵌入。然而，当测试较长的输入帧时，绝对位置嵌入应该通过微调插值到目标输入大小。相对位置嵌入由于缺乏绝对位置信息而修改了自注意，表现较差。为了克服上述问题，扩展了条件位置编码(CPE)来设计DPE。
其中DWConv表示简单的三维深度卷积与零填充。由于卷积的共享参数和局部性，DPE可以克服置换不变性，并且对任意输入长度都很友好。此外，在CPE中已经证明，零填充可以帮助边界上的token意识到自己的绝对位置，因此所有token都可以通过查询其邻居来逐步编码自己的绝对时空位置信息
class SpeicalPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
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
self.norm = nn.LayerNorm(embed_dim)
self.proj = conv_3xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
def forward(self, x):
B, C, T, H, W = x.shape
# FIXME look at relaxing size constraints
# assert H == self.img_size[0] and W == self.img_size[1], \
# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x)
B, C, T, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
return x
多头关系聚合器(MHRA)
设计了一种替代的关系聚合器(RA)，它可以将三维卷积和时空自注意灵活地统一在一个简洁的Transformer中，分别解决了浅层和深层的视频冗余和依赖问题。具体来说，MHRA通过多头融合进行tokens关系学习：
- 输入张量为 X ∈ R C × T × H × W , r e s h a p e 为 X ∈ R L × C X \in \mathbb{R}^{C \times T \times H \times W} , reshape为 \mathbf{X} \in \mathbb{R}^{L \times C} X∈RC×T×H×W,reshape为X∈RL×C ， L = T × H × W L=T \times H \times W L=T×H×W。
- 通过线性转换，可以将 X \mathbf{X} X 转换为上下文信息 V n ( X ) ∈ R L × C N ， n \mathrm{V}_{n}(\mathbf{X}) \in \mathbb{R}^{L \times \frac{C}{N}} ， \mathrm{n} Vn(X)∈RL×NC，n 表示第几个head。
- 然后关系聚合器 RA通过token affinity A n ∈ R L × L \mathrm{A}_{n} \in \mathbb{R}^{L \times L} An∈RL×L 来融合上下文信息得到 R n ( X ) ∈ R L × C N \mathbf{R}_{n}(\mathbf{X}) \in \mathbb{R}^{L \times \frac{C}{N}} Rn(X)∈RL×NC。
- 最后concat所有的head信息，并通过 U ∈ C L × C \mathbf{U} \in \mathbb{C}^{L \times C} U∈CL×C聚合所有head的信息。
根据上下文的域大小，可以将MHRA分为 local MHRA 和global MHRA
在网络浅层中，目标是学习小三维时空中局部时空背景下的详细视频表示：值仅依赖于token之间的相对3D位置
class CBlock(nn.Module):
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):
super().__init__()
self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
self.norm1 = bn_3d(dim)
self.conv1 = conv_1x1x1(dim, dim, 1)
self.conv2 = conv_1x1x1(dim, dim, 1)
self.attn = conv_5x5x5(dim, dim, groups=dim)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = bn_3d(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.pos_embed(x)
x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
在网络深层中，关注于在全局视频帧中捕获长远token依赖关系：通过比较全局视图中所有token的内容相似性
class SABlock(nn.Module):
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):
super().__init__()
self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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)
def forward(self, x):
x = x + self.pos_embed(x)
B, C, T, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.transpose(1, 2).reshape(B, C, T, H, W)
return x
模型代码
class Uniformer(nn.Module):
"""Vision Transformer
一个PyTorch实现：`一个图像值16x16词：大规模图像识别的Transformer` - https://arxiv.org/abs/2010.11929
"""
def __init__(self, cfg):
super().__init__()
# 从配置中提取各种参数
depth = cfg.UNIFORMER.DEPTH # 模型深度
num_classes = cfg.MODEL.NUM_CLASSES # 类别数量
img_size = cfg.DATA.TRAIN_CROP_SIZE # 图像尺寸
in_chans = cfg.DATA.INPUT_CHANNEL_NUM[0] # 输入通道数
embed_dim = cfg.UNIFORMER.EMBED_DIM # 嵌入维度
head_dim = cfg.UNIFORMER.HEAD_DIM # 头部维度
mlp_ratio = cfg.UNIFORMER.MLP_RATIO # MLP比例
qkv_bias = cfg.UNIFORMER.QKV_BIAS # QKV偏置
qk_scale = cfg.UNIFORMER.QKV_SCALE # QKV缩放
representation_size = cfg.UNIFORMER.REPRESENTATION_SIZE # 表示维度
drop_rate = cfg.UNIFORMER.DROPOUT_RATE # Dropout率
attn_drop_rate = cfg.UNIFORMER.ATTENTION_DROPOUT_RATE # 注意力Dropout率
drop_path_rate = cfg.UNIFORMER.DROP_DEPTH_RATE # 随机深度衰减率
split = cfg.UNIFORMER.SPLIT # 是否分裂
std = cfg.UNIFORMER.STD # 是否标准化
self.use_checkpoint = cfg.MODEL.USE_CHECKPOINT # 使用检查点
self.checkpoint_num = cfg.MODEL.CHECKPOINT_NUM # 检查点数量
logger.info(f'Use checkpoint: {self.use_checkpoint}') # 日志：使用检查点
logger.info(f'Checkpoint number: {self.checkpoint_num}') # 日志：检查点数量
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # 为了与其他模型保持一致，设置特征数量和嵌入维度
norm_layer = partial(nn.LayerNorm, eps=1e-6) # 层标准化函数
# 创建不同尺寸的Patch嵌入层
self.patch_embed1 = SpeicalPatchEmbed(
img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0]) # Patch嵌入层1
self.patch_embed2 = PatchEmbed(
img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1], std=std) # Patch嵌入层2
self.patch_embed3 = PatchEmbed(
img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2], std=std) # Patch嵌入层3
self.patch_embed4 = PatchEmbed(
img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3], std=std) # Patch嵌入层4
self.pos_drop = nn.Dropout(p=drop_rate) # 位置Dropout层
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))] # 随机深度衰减规则
num_heads = [dim // head_dim for dim in embed_dim] # 头部数量
# 创建Transformer块并组成模型的不同部分
self.blocks1 = nn.ModuleList([
CBlock(
dim=embed_dim[0], num_heads=num_heads[0], 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) for i in range(depth[0])]) # 第一个部分的Transformer块
self.blocks2 = nn.ModuleList([
CBlock(
dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]], norm_layer=norm_layer) for i in range(depth[1])]) # 第二个部分的Transformer块
if split:
self.blocks3 = nn.ModuleList([
SplitSABlock(
dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
for i in range(depth[2])]) # 如果拆分，创建第三个部分的Split Self-Attention块
self.blocks4 = nn.ModuleList([
SplitSABlock(
dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
for i in range(depth[3])]) # 如果拆分，创建第四个部分的Split Self-Attention块
else:
self.blocks3 = nn.ModuleList([
SABlock(
dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
for i in range(depth[2])]) # 创建第三个部分的Self-Attention块
self.blocks4 = nn.ModuleList([
SABlock(
dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
for i in range(depth[3])]) # 创建第四个部分的Self-Attention块
self.norm = bn_3d(embed_dim[-1]) # 3D批标准化层
# 表示层
if representation_size:
self.num_features = representation_size
self.pre_logits = nn.Sequential(OrderedDict([
('fc', nn.Linear(embed_dim, representation_size)), # 全连接层
('act', nn.Tanh()) # Tanh激活函数
]))
else:
self.pre_logits = nn.Identity() # 如果没有设置表示维度，则为恒等映射
# 分类器头部
self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity() # 分类器线性层或恒等映射
self.apply(self._init_weights) # 初始化权重
# 初始化某些参数的权重
for name, p in self.named_parameters():
if 't_attn.qkv.weight' in name:
nn.init.constant_(p, 0) # 初始化t_attn.qkv.weight为常数0
if 't_attn.qkv.bias' in name:
nn.init.constant_(p, 0) # 初始化t_attn.qkv.bias为常数0
if 't_attn.proj.weight' in name:
nn.init.constant_(p, 1) # 初始化t_attn.proj.weight为常数1
if 't_attn.proj.bias' in name:
nn.init.constant_(p, 0) # 初始化t_attn.proj.bias为常数0
def _init_weights(self, m):
"""初始化权重函数"""
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02) # 使用截断正态分布初始化权重
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0) # 初始化偏置为常数0
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0) # 初始化偏置为常数0
nn.init.constant_(m.weight, 1.0) # 初始化权重为常数1.0
@torch.jit.ignore
def no_weight_decay(self):
"""指定不进行权重衰减的参数"""
return {'pos_embed', 'cls_token'}
def get_classifier(self):
"""获取分类器头部"""
return self.head
def reset_classifier(self, num_classes, global_pool=''):
"""重置分类器
Args:
num_classes (int): 新的类别数量
global_pool (str): 全局池化方式
"""
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() # 重新设置分类器头部
def inflate_weight(self, weight_2d, time_dim, center=False):
"""权重膨胀
Args:
weight_2d: 二维权重张量
time_dim: 时间维度
center (bool): 是否中心化
Returns:
Tensor: 膨胀后的三维权重张量
"""
if center:
weight_3d = torch.zeros(*weight_2d.shape)
weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
middle_idx = time_dim // 2
weight_3d[:, :, middle_idx, :, :] = weight_2d
else:
weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
weight_3d = weight_3d / time_dim
return weight_3d
def get_pretrained_model(self, cfg):
"""获取预训练模型
Args:
cfg: 配置文件
Returns:
dict: 预训练模型参数字典
"""
if cfg.UNIFORMER.PRETRAIN_NAME:
checkpoint = torch.load(model_path[cfg.UNIFORMER.PRETRAIN_NAME], map_location='cpu')
if 'model' in checkpoint:
checkpoint = checkpoint['model']
elif 'model_state' in checkpoint:
checkpoint = checkpoint['model_state']
state_dict_3d = self.state_dict()
for k in checkpoint.keys():
if checkpoint[k].shape != state_dict_3d[k].shape:
if len(state_dict_3d[k].shape) <= 2:
logger.info(f'Ignore: {k}') # 忽略不匹配的参数
continue
logger.info(f'Inflate: {k}, {checkpoint[k].shape} => {state_dict_3d[k].shape}') # 膨胀参数形状
time_dim = state_dict_3d[k].shape[2]
checkpoint[k] = self.inflate_weight(checkpoint[k], time_dim)
if self.num_classes != checkpoint['head.weight'].shape[0]:
del checkpoint['head.weight']
del checkpoint['head.bias']
return checkpoint
else:
return None
def forward_features(self, x):
"""前向传播特征提取
Args:
x (tensor): 输入张量
Returns:
tensor: 特征提取结果
"""
x = self.patch_embed1(x)
x = self.pos_drop(x)
for i, blk in enumerate(self.blocks1):
if self.use_checkpoint and i < self.checkpoint_num[0]:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
x = self.patch_embed2(x)
for i, blk in enumerate(self.blocks2):
if self.use_checkpoint and i < self.checkpoint_num[1]:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
x = self.patch_embed3(x)
for i, blk in enumerate(self.blocks3):
if self.use_checkpoint and i < self.checkpoint_num[2]:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
x = self.patch_embed4(x)
for i, blk in enumerate(self.blocks4):
if self.use_checkpoint and i < self.checkpoint_num[3]:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
x = self.norm(x)
x = self.pre_logits(x)
return x
def forward(self, x):
"""前向传播
Args:
x (tensor): 输入张量
Returns:
tensor: 输出结果
"""
x = x[0]
x = self.forward_features(x)
x = x.flatten(2).mean(-1)
x = self.head(x)
return x
总结
原文提出了一种新的UniFormer，它可以有效地统一3D卷积和时空自注意力在一个简洁的Transformer格式，以克服视频冗余和依赖。我们在浅层采用局部MHRA，大大减少了计算负担，在深层采用全局MHRA，学习全局令牌关系。大量的实验表明，我们的UniFormer在流行的视频基准测试Kinetics-400/600和Something-Something V1/V2上实现了准确性和效率之间的较好平衡。