MVIT图像分类 学习笔记 (附代码)

2024-01-10 16:17:06

论文地址:https://arxiv.org/pdf/2104.11227.pdf

代码地址:https://github.com/facebookresearch/SlowFast

1.是什么?

MViT(Multiscale Vision Transformers)是一种多尺度视觉Transformer模型。它的关键概念是逐步增加通道分辨率(即维度),同时降低整个网络的时空分辨率(即序列长度)。通过这种设计,MViT在早期层具有精细的时空(和粗通道)分辨率,在后期层中上/下采样到粗时空(和精细通道)分辨率。
与在整个网络中保持恒定通道容量和分辨率的传统transformer不同,多尺度transformer具有几个通道分辨率“尺度”阶段。从图像分辨率和小通道维度出发,逐级扩展通道容量,同时降低空间分辨率。这在transformer网络内部创建了一个特征激活的多尺度金字塔,有效地将transformer的原理与多尺度特征层次联系起来。

2.为什么?

  • 视觉信号的极度密集性。前期层信道容量较轻,可以在高空间分辨率下运行,模拟简单的低级视觉信息。反过来,深层可以关注复杂的高级特征。
  • 多尺度模型充分利用时间信息。在自然视频上训练的ViT在具有混洗帧的视频上测试时,不会出现性能衰减,表明这些模型没有有效地使用时间信息,而是严重依赖于外观。相比之下,当在随机帧上测试MViT模型时,有显著的精度衰减,这表明十分依赖于时间信息。
  • MViT在没有任何外部预训练数据的情况下,相较于并发视频转换器有显著的性能提升。

3.怎么样?

通用 Multiscale Vision Transformer 架构建立在 stages 这个核心概念之上。每个 stage 都包含多个具有特定时空分辨率和通道维度的 transformer block。 Multiscale Transformers 的主要思想是逐步扩展通道容量,同时汇集网络从输入到输出的分辨率。

3.1网络结构

3.11.多头池化注意力(Multi Head Pooling Attention)

首先对MHPA作出解释,这是本文的核心,它使得多尺度变换器以逐渐变化的时空分辨率进行操作。与原始的多头注意力(MHA)不同,在原始的多头注意力中,通道维度和时空分辨率保持不变,MHPA将潜在张量序列合并,以减少参与输入的序列长度(分辨率)。如图3所示

具体地说,考虑一个序列长度为L的D维输入张量X, X∈RL×D。在MHA之后,MHPA将输入的X通过线性运算投影到中间查询张量(Q∈RL×D)、键张量(K∈RL×D)和值张量(V∈RL×D)?

使用维数为D×D的权重WQ、WK、WV?,然后使用池操作符 P将获得的中间张量按序列长度进行合并

Pooling Operator

在参与输入之前,中间张量Q\hat{},K\hat{},V\hat{}参与运算符P ( ? ; Θ ) P(·;Θ)P(?;Θ),这是MHPA的基石,也是多尺度变换器架构的基础。运算符P ( ? ; Θ ) P(·;Θ)P(?;Θ)沿每个维度对输入张量执行池化计算。
Θ=(k,s,p),使用大小为k_{K}*k_{H}*k_{W}的池核k、大小为s_{K}*s_{H}*s_{W}的步长s和大小为p_{K}*p_{H}*p_{W}填充p,来减少尺寸L=T×H×W的输入张量,经过下面的公式池化之后

?

用坐标方向的方程。池化张量再次被平面化,得到P(Y;Θ)∈R ~ L×D,序列长度减少

Pooling Attention

P(?;Θ) 分别地应用于所有中间张量Q\hat{},K\hat{},V\hat{},由此产生预注意向量Q=P(Q\widehat{};\Theta _{Q}),K=P(K\widehat{};\Theta _{K})V=P(V\widehat{};\Theta _{V})通过操作对Q,K,V进行计算

其中\sqrt{D}是按行对内积矩阵进行规范化。因此,随着P(?;Θ)中查询向量 Q 的缩减,最后的输出结果是输出序列缩减了s_{T^{Q}}s_{H^{Q}}s_{W^{Q}}。并且我们从图3可以注意到,s_{k}=s_{v}必须成立,因为他们缩减的幅度必须一致,否则不能进行计算。

总结,以上公式可以用下面的公式来详细表达
?

Multiple heads
假设有h个头部,计算可以并行化,其中每个头部在D维输入张量X的D/h的非重叠子集上执行池化注意力。

3.1.2多尺度变换器网络(Multiscale Transformer Networks)

基于多头集中注意力(MHPA),本文创造了专门使用MHPA和MLP层进行视觉表征学习的多尺度变换器模型。在此之前,了解一下ViT模型

Vision Transformer (ViT)
  • 首先将分辨率为 T × H × W 的输入视频,其中 T 为帧数、 H 为高度、 W 为宽度,分割成尺寸为1 × 16 × 16的非重叠块,然后在平坦图像块上逐点运用线性层,将其投影到潜在尺寸 D 中。就是1 × 16 × 16的核大小和步长的卷积,如表1中patch1阶段所示

  • 位置嵌入E\epsilon R^{L*D}添加到长度为L且维数为D的投影序列的每个元素。
  • 通过N个变换器块的顺序处理,产生的长度为L+1的序列,每个变换器块执行注意力(MHA)、多层感知机(MLP)和层规范化(LN)操作。通过以下公式计算:

此处产生长度为L+1的序列是因为spacetime resolution + class token

  • N个连续块之后的结果序列被层规范化,通过线性层来预测输出。此处需要注意,默认情况下,MLP的输入是4D。

?Multiscale Vision Transformers (MViT)

逐步增加信道维度,同时降低整个网络的时空分辨率(即序列长度)。MViT在早期层中具有精细的时空分辨率和低信道维度,而在后期层中,变为粗略的时空分辨率和高信道维度。MViT如表2所示。

Scale stages
尺度阶段定义为一组N个变换器块,在相同的尺度上跨信道和时空维度以相同的分辨率运行。在阶段转换时,信道维度上采样,而序列的长度下采样。

Channel expansion
当从一个阶段过渡到下一个阶段时,通过增加前一阶段最终MLP层的输出来扩展通道维数,增加的因素与该阶段引入的分辨率变化相关。举个例子来说,时空分辨率降低4倍,那么通道维数需要增大2倍。

Query pooling
池操作使得查询向量方面有更高的灵活性,从而可以改变输出序列的长度。将查询向量 P(Q;k;p;s)与核函数s=(s^{Q_{T}},s^{Q_{H}},s^{Q_{W}})结合起来,使序列减少了s^{Q_{T}},s^{Q_{H}},s^{Q_{W}}。这里有个点需要注意,一个阶段的开始降低分辨率,然后在整个阶段保持分辨率,所以只有每个阶段的第一个P在 s^{Q}> 1,其他都是 s^{Q}≡(1,1,1)

Key-Value pooling
与查询池不同,更改键K和值V张量的序列长度不会更改输出序列长度(时空分辨率),所以对于所有的K和V都执行了池化。上面说过,最后为了能够执行计算,K和V池化后的各个维度必须一致,所以本文默认情况下,取\Theta _{K}= \Theta _{V}

Skip connections
由于通道尺寸和序列长度发生变化,对skip connection 进行pool 以适应其两端的尺寸不匹配。由图3可以看出,MHPA通过使用查询池操作符Q=P(Q\widehat{};\Theta _{Q})来处理这种不匹配

3.2代码实现

MultiHeadedAttention

class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        "Take in model size and number of heads."
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        # We assume d_v always equals d_k
        self.d_k = d_model // h
        self.h = h
        self.linears = clones(nn.Linear(d_model, d_model), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)
 
    def forward(self, query, key, value, mask=None):
        "Implements Figure 2"
        if mask is not None:
            # Same mask applied to all h heads.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)
 
        # 1) Do all the linear projections in batch from d_model => h x d_k
        query, key, value = [
            lin(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
            for lin, x in zip(self.linears, (query, key, value))
        ]
 
        # 2) Apply attention on all the projected vectors in batch.
        x, self.attn = attention(
            query, key, value, mask=mask, dropout=self.dropout
        )
 
        # 3) "Concat" using a view and apply a final linear.
        x = (
            x.transpose(1, 2)
            .contiguous()
            .view(nbatches, -1, self.h * self.d_k)
        )
        del query
        del key
        del value
        return self.linears[-1](x)

?attention_pool

def attention_pool(tensor, pool, thw_shape, has_cls_embed=True, norm=None):
    if pool is None:
        return tensor, thw_shape
    tensor_dim = tensor.ndim
    if tensor_dim == 4:
        pass
    elif tensor_dim == 3:
        tensor = tensor.unsqueeze(1)
    else:
        raise NotImplementedError(f"Unsupported input dimension {tensor.shape}")
 
    if has_cls_embed:
        cls_tok, tensor = tensor[:, :, :1, :], tensor[:, :, 1:, :]
 
    B, N, L, C = tensor.shape
    T, H, W = thw_shape
    tensor = (
        tensor.reshape(B * N, T, H, W, C).permute(0, 4, 1, 2, 3).contiguous()
    )
 
    tensor = pool(tensor)
 
    thw_shape = [tensor.shape[2], tensor.shape[3], tensor.shape[4]]
    L_pooled = tensor.shape[2] * tensor.shape[3] * tensor.shape[4]
    tensor = tensor.reshape(B, N, C, L_pooled).transpose(2, 3)
    if has_cls_embed:
        tensor = torch.cat((cls_tok, tensor), dim=2)
    if norm is not None:
        tensor = norm(tensor)
    # Assert tensor_dim in [3, 4]
    if tensor_dim == 4:
        pass
    else:  #  tensor_dim == 3:
        tensor = tensor.squeeze(1)
    return tensor, thw_shape

Mlp

class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop_rate=0.0,
    ):
        super().__init__()
        self.drop_rate = drop_rate
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        if self.drop_rate > 0.0:
            self.drop = nn.Dropout(drop_rate)
 
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        if self.drop_rate > 0.0:
            x = self.drop(x)
        x = self.fc2(x)
        if self.drop_rate > 0.0:
            x = self.drop(x)
        return x

ClassificationHead?

 class ClassificationHead(nn.Sequential):
     def __init__(self, emb_size: int = 768, n_classes: int = 1000):
         super().__init__(
             Reduce('b n e -> b e', reduction='mean'),
             nn.LayerNorm(emb_size), 
             nn.Linear(emb_size, n_classes))

MultiScaleAttention?

class MultiScaleAttention(nn.Module):
    def __init__(
        self,
        dim,
        dim_out,
        input_size,
        num_heads=8,
        qkv_bias=False,
        drop_rate=0.0,
        kernel_q=(1, 1, 1),
        kernel_kv=(1, 1, 1),
        stride_q=(1, 1, 1),
        stride_kv=(1, 1, 1),
        norm_layer=nn.LayerNorm,
        has_cls_embed=True,
        # Options include `conv`, `avg`, and `max`.
        mode="conv",
        # If True, perform pool before projection.
        pool_first=False,
        rel_pos_spatial=False,
        rel_pos_temporal=False,
        rel_pos_zero_init=False,
        residual_pooling=False,
        separate_qkv=False,
    ):
        super().__init__()
        self.pool_first = pool_first
        self.separate_qkv = separate_qkv
        self.drop_rate = drop_rate
        self.num_heads = num_heads
        self.dim_out = dim_out
        head_dim = dim_out // num_heads
        self.scale = head_dim**-0.5
        self.has_cls_embed = has_cls_embed
        self.mode = mode
        padding_q = [int(q // 2) for q in kernel_q]
        padding_kv = [int(kv // 2) for kv in kernel_kv]
 
        if pool_first or separate_qkv:
            self.q = nn.Linear(dim, dim_out, bias=qkv_bias)
            self.k = nn.Linear(dim, dim_out, bias=qkv_bias)
            self.v = nn.Linear(dim, dim_out, bias=qkv_bias)
        else:
            self.qkv = nn.Linear(dim, dim_out * 3, bias=qkv_bias)
 
        self.proj = nn.Linear(dim_out, dim_out)
        if drop_rate > 0.0:
            self.proj_drop = nn.Dropout(drop_rate)
 
        # Skip pooling with kernel and stride size of (1, 1, 1).
        if numpy.prod(kernel_q) == 1 and numpy.prod(stride_q) == 1:
            kernel_q = ()
        if numpy.prod(kernel_kv) == 1 and numpy.prod(stride_kv) == 1:
            kernel_kv = ()
 
        if mode in ("avg", "max"):
            pool_op = nn.MaxPool3d if mode == "max" else nn.AvgPool3d
            self.pool_q = (
                pool_op(kernel_q, stride_q, padding_q, ceil_mode=False)
                if len(kernel_q) > 0
                else None
            )
            self.pool_k = (
                pool_op(kernel_kv, stride_kv, padding_kv, ceil_mode=False)
                if len(kernel_kv) > 0
                else None
            )
            self.pool_v = (
                pool_op(kernel_kv, stride_kv, padding_kv, ceil_mode=False)
                if len(kernel_kv) > 0
                else None
            )
        elif mode == "conv" or mode == "conv_unshared":
            if pool_first:
                dim_conv = dim // num_heads if mode == "conv" else dim
            else:
                dim_conv = dim_out // num_heads if mode == "conv" else dim_out
            self.pool_q = (
                nn.Conv3d(
                    dim_conv,
                    dim_conv,
                    kernel_q,
                    stride=stride_q,
                    padding=padding_q,
                    groups=dim_conv,
                    bias=False,
                )
                if len(kernel_q) > 0
                else None
            )
            self.norm_q = norm_layer(dim_conv) if len(kernel_q) > 0 else None
            self.pool_k = (
                nn.Conv3d(
                    dim_conv,
                    dim_conv,
                    kernel_kv,
                    stride=stride_kv,
                    padding=padding_kv,
                    groups=dim_conv,
                    bias=False,
                )
                if len(kernel_kv) > 0
                else None
            )
            self.norm_k = norm_layer(dim_conv) if len(kernel_kv) > 0 else None
            self.pool_v = (
                nn.Conv3d(
                    dim_conv,
                    dim_conv,
                    kernel_kv,
                    stride=stride_kv,
                    padding=padding_kv,
                    groups=dim_conv,
                    bias=False,
                )
                if len(kernel_kv) > 0
                else None
            )
            self.norm_v = norm_layer(dim_conv) if len(kernel_kv) > 0 else None
        else:
            raise NotImplementedError(f"Unsupported model {mode}")
 
        self.rel_pos_spatial = rel_pos_spatial
        self.rel_pos_temporal = rel_pos_temporal
        if self.rel_pos_spatial:
            assert input_size[1] == input_size[2]
            size = input_size[1]
            q_size = size // stride_q[1] if len(stride_q) > 0 else size
            kv_size = size // stride_kv[1] if len(stride_kv) > 0 else size
            rel_sp_dim = 2 * max(q_size, kv_size) - 1
 
            self.rel_pos_h = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
            if not rel_pos_zero_init:
                trunc_normal_(self.rel_pos_h, std=0.02)
                trunc_normal_(self.rel_pos_w, std=0.02)
        if self.rel_pos_temporal:
            self.rel_pos_t = nn.Parameter(
                torch.zeros(2 * input_size[0] - 1, head_dim)
            )
            if not rel_pos_zero_init:
                trunc_normal_(self.rel_pos_t, std=0.02)
 
        self.residual_pooling = residual_pooling
 
    def forward(self, x, thw_shape):
        B, N, _ = x.shape
 
        if self.pool_first:
            if self.mode == "conv_unshared":
                fold_dim = 1
            else:
                fold_dim = self.num_heads
            x = x.reshape(B, N, fold_dim, -1).permute(0, 2, 1, 3)
            q = k = v = x
        else:
            assert self.mode != "conv_unshared"
            if not self.separate_qkv:
                qkv = (
                    self.qkv(x)
                    .reshape(B, N, 3, self.num_heads, -1)
                    .permute(2, 0, 3, 1, 4)
                )
                q, k, v = qkv[0], qkv[1], qkv[2]
            else:
                q = k = v = x
                q = (
                    self.q(q)
                    .reshape(B, N, self.num_heads, -1)
                    .permute(0, 2, 1, 3)
                )
                k = (
                    self.k(k)
                    .reshape(B, N, self.num_heads, -1)
                    .permute(0, 2, 1, 3)
                )
                v = (
                    self.v(v)
                    .reshape(B, N, self.num_heads, -1)
                    .permute(0, 2, 1, 3)
                )
 
        q, q_shape = attention_pool(
            q,
            self.pool_q,
            thw_shape,
            has_cls_embed=self.has_cls_embed,
            norm=self.norm_q if hasattr(self, "norm_q") else None,
        )
        k, k_shape = attention_pool(
            k,
            self.pool_k,
            thw_shape,
            has_cls_embed=self.has_cls_embed,
            norm=self.norm_k if hasattr(self, "norm_k") else None,
        )
        v, v_shape = attention_pool(
            v,
            self.pool_v,
            thw_shape,
            has_cls_embed=self.has_cls_embed,
            norm=self.norm_v if hasattr(self, "norm_v") else None,
        )
 
        if self.pool_first:
            q_N = (
                numpy.prod(q_shape) + 1
                if self.has_cls_embed
                else numpy.prod(q_shape)
            )
            k_N = (
                numpy.prod(k_shape) + 1
                if self.has_cls_embed
                else numpy.prod(k_shape)
            )
            v_N = (
                numpy.prod(v_shape) + 1
                if self.has_cls_embed
                else numpy.prod(v_shape)
            )
 
            q = q.permute(0, 2, 1, 3).reshape(B, q_N, -1)
            q = (
                self.q(q)
                .reshape(B, q_N, self.num_heads, -1)
                .permute(0, 2, 1, 3)
            )
 
            v = v.permute(0, 2, 1, 3).reshape(B, v_N, -1)
            v = (
                self.v(v)
                .reshape(B, v_N, self.num_heads, -1)
                .permute(0, 2, 1, 3)
            )
 
            k = k.permute(0, 2, 1, 3).reshape(B, k_N, -1)
            k = (
                self.k(k)
                .reshape(B, k_N, self.num_heads, -1)
                .permute(0, 2, 1, 3)
            )
 
        N = q.shape[2]
        attn = (q * self.scale) @ k.transpose(-2, -1)
        if self.rel_pos_spatial:
            attn = cal_rel_pos_spatial(
                attn,
                q,
                k,
                self.has_cls_embed,
                q_shape,
                k_shape,
                self.rel_pos_h,
                self.rel_pos_w,
            )
 
        if self.rel_pos_temporal:
            attn = cal_rel_pos_temporal(
                attn,
                q,
                self.has_cls_embed,
                q_shape,
                k_shape,
                self.rel_pos_t,
            )
        attn = attn.softmax(dim=-1)
 
        x = attn @ v
 
        if self.residual_pooling:
            if self.has_cls_embed:
                x[:, :, 1:, :] += q[:, :, 1:, :]
            else:
                x = x + q
 
        x = x.transpose(1, 2).reshape(B, -1, self.dim_out)
        x = self.proj(x)
 
        if self.drop_rate > 0.0:
            x = self.proj_drop(x)
        return x, q_shape

参考:

Multiscale Vision Transformers 论文阅读

Multiscale Vision Transformers 论文详解

mvit代码

文章来源:https://blog.csdn.net/PLANTTHESON/article/details/135499051
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