如何修改timm库中vit的输入大小

您可以使用 Resize 类来修改 timm 库中的 vit 模型的输入大小。您可以像下面这样使用 Resize 类：

import torch.nn as nn
from timm.models.vision_transformer import VisionTransformer

class MyVisionTransformer(VisionTransformer):
    def __init__(self, image_size=256, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.patch_embed = nn.Sequential(
            nn.Conv2d(3, self.embed_dim, kernel_size=patch_size, stride=patch_size),
            nn.Flatten(2),
            nn.Linear(patch_size ** 2 * self.embed_dim, self.embed_dim),
        )

        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, self.embed_dim))
        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
        self.dropout = nn.Dropout(self.drop_rate)
        self.layer_norm = nn.LayerNorm(self.embed_dim)

        self.fc = nn.Linear(self.embed_dim, self.num_classes)  # head

        # Resize the input image to match the expected size
        self.input_resizer = nn.Sequential(
            nn.Resize((image_size, image_size)),
            nn.CenterCrop((self.patch_size * self.patch_size, self.patch_size * self.patch_size))
        )

    def forward(self, x):
        x = self.input_resizer(x)  # Resize the input image

        x = self.patch_embed(x)
        x = x.transpose(1, 2)
        x = self.layer_norm(self.pos_embed + x)
        x = self.dropout(x)

        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_token, x), dim=1)
        x = self.transformer(x)
        x = x[:, 0]  # extract the <cls> token
        x = self.fc(x)

        return x

这里的 MyVisionTransformer 类继承了 timm 库中的 VisionTransformer 类，并使用 Resize 类在模型输入之前将图像调整为预期的大小。您可以将图像大小作为参数传递给 MyVisionTransformer 类的构造函数。