def __init__(self, img_size=224, patch_size=(2,4,4), in_chans=3, embed_dim=96, norm_layer=None): super().__init__() self.patch_size = patch_size img_size = to_2tuple(img_size) self.in_chans = in_chans self.embed_dim = embed_dim patches_resolution = [img_size[0] // patch_size[1], img_size[1] // patch_size[2]] self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None

这是一个 PyTorch 的模型初始化函数，它定义了一个 3D 卷积层和一个归一化层。主要参数包括：

- img_size：图像的尺寸。
- patch_size：划分图像的 patch 的大小。
- in_chans：输入图像的通道数。
- embed_dim：嵌入向量的维度。
- norm_layer：归一化层的类型。

该函数的作用是将输入的图像分为多个 patch，并且将每个 patch 映射到一个嵌入向量。这是 ViT 模型（Vision Transformer）的核心思想。ViT 模型将图像分为多个 patch，然后通过 Transformer 来处理这些 patch，最后输出整个图像的特征向量。

相关问题

transformer模型patch

Transformer模型中的patch是指将输入图像分成多个小块，每个小块称为一个patch。在Vision Transformer模型中，输入图像被分成了若干个大小相等的patch，每个patch都被看作是一个token，然后通过Transformer Encoder进行处理。这种方式可以使得模型更好地处理大尺寸的图像，同时也可以减少模型的计算量。

在基于patch交互分析的新视角中，作者提出了一种新的Transformer结构，通过在训练期间引入patch-wise的responsive field作为交互窗口指导监督模型训练，从而提高模型的性能。这种方法可以使得模型更好地捕捉图像中的局部特征，从而提高模型的准确率。

下面是一个使用PyTorch实现的Transformer模型中的patch处理的例子：

import torch
import torch.nn as nn
import torch.nn.functional as F

class PatchEmbedding(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.n_patches = (img_size // patch_size) ** 2
        self.patch_embed = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        
    def forward(self, x):
        x = self.patch_embed(x)  # (B, embed_dim, n_patches ** 0.5, n_patches ** 0.5)
        x = x.flatten(2)  # (B, embed_dim, n_patches)
        x = x.transpose(1, 2)  # (B, n_patches, embed_dim)
        return x

pytorch写一个vit

非常感谢您的提问。以下是使用 PyTorch 实现 ViT 的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class PatchEmbedding(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = (img_size, img_size) if isinstance(img_size, int) else img_size
        patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else 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.projection = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        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.projection(x).flatten(2).transpose(1, 2)
        return x

class MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, dropout=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = nn.GELU()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x

class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        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 = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = nn.Dropout(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = nn.LayerNorm(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, dropout=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

class VisionTransformer(nn.Module):
    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,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.):
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = PatchEmbedding(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i])
            for i in range(depth)])

        self.norm = nn.LayerNorm(embed_dim)
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward(self, x):
        x = self.patch_embed(x)
        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_token, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(x)

        x = self.norm(x[:, 0])
        x = self.head(x)
        return x

希望这个代码示例能够帮助您实现 ViT。如果您有任何问题，请随时问我。