RuntimeError: shape '[128, 3, 5, 4, 5, 4]' is invalid for input of size 185856

Question

我正在使用以下代码使用 Visual Transformer (vit_b_16) 从图像中提取描述符，但出现错误：RuntimeError: shape '[128, 3, 5, 4, 5, 4 ]' 对于大小为 185856 的输入无效。有谁知道我做错了什么以及如何解决它？

def img_to_patch(x, patch_size, flatten_channels=True):

        B, C, H, W = x.shape
        x = x.reshape(B, C, H//patch_size, patch_size, W//patch_size, patch_size)
        x = x.permute(0, 2, 4, 1, 3, 5) # [B, H', W', C, p_H, p_W]
        x = x.flatten(1,2)              # [B, H'*W', C, p_H, p_W]
        if flatten_channels:
            x = x.flatten(2,4)          # [B, H'*W', C*p_H*p_W]
        return x

class AttentionBlock(nn.Module):

    def __init__(self, embed_dim, hidden_dim, num_heads, dropout=0.0):

        super().__init__()

        self.layer_norm_1 = nn.LayerNorm(embed_dim)
        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
        self.layer_norm_2 = nn.LayerNorm(embed_dim)
        self.linear = nn.Sequential(
            nn.Linear(embed_dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, embed_dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        inp_x = self.layer_norm_1(x)
        x = x + self.attn(inp_x, inp_x, inp_x)[0]
        x = x + self.linear(self.layer_norm_2(x))
        return x


class VisionTransformer(nn.Module):
    
    def __init__(self, embed_dim, hidden_dim, num_channels, num_heads, num_layers, num_classes, patch_size, num_patches, dropout=0.0):

        super().__init__()

        self.patch_size = patch_size

        # Layers/Networks
        self.input_layer = nn.Linear(num_channels*(patch_size**2), embed_dim)
        self.transformer = nn.Sequential(*[AttentionBlock(embed_dim, hidden_dim, num_heads, dropout=dropout) for _ in range(num_layers)])
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(embed_dim),
            nn.Linear(embed_dim, num_classes)
        )
        self.dropout = nn.Dropout(dropout)

        # Parameters/Embeddings
        self.cls_token = nn.Parameter(torch.randn(1,1,embed_dim))
        self.pos_embedding = nn.Parameter(torch.randn(1,1+num_patches,embed_dim))

    def forward(self, x):
        # Preprocess input
        print(x.shape)
        x = img_to_patch(x, self.patch_size)
        print(x.shape)
        B, T, _ = x.shape
        x = self.input_layer(x)

        # Add CLS token and positional encoding
        cls_token = self.cls_token.repeat(B, 1, 1)
        x = torch.cat([cls_token, x], dim=1)
        x = x + self.pos_embedding[:,:T+1]

        # Apply Transforrmer
        x = self.dropout(x)
        x = x.transpose(0, 1)
        x = self.transformer(x)

        # Perform classification prediction
        cls = x[0]
        out = self.mlp_head(cls)
        return out

class ViT(pl.LightningModule):

    def __init__(self, model_kwargs, lr):
        super().__init__()
        self.save_hyperparameters()
        self.model = VisionTransformer(**model_kwargs)
       # self.example_input_array = next(iter(train_loader))[0]

    def forward(self, x):
        return self.model(x)

我正在像这样初始化 Transformer：

if network_variant == 'vb16':
    net = ViT(model_kwargs={
               'embed_dim': 256,
               'hidden_dim': 512,
               'num_heads': 8,
               'num_layers': 6,
               'patch_size': 4,
               'num_channels': 3,
               'num_patches': 64,
               'num_classes': num_classes,
               'dropout': 0.2
                },
                lr=3e-4)

这是我第一次使用 PyTorch 和 Vision Transformers，所以我真的不确定我做错了什么。

Answer 1

错误是说 numpy 正在尝试填充尺寸为 128 * 35454 的矩阵，这需要 153,600元素。但是，整形前的数据有 185,856 个元素。您很可能误算了一些指数。区别是 32256=12843*21，它提供了一些关于您可能误算了哪些索引的提示......您唯一调用 reshape() 的地方是第二行img_to_patch()，所以我认为这就是错误的来源。（在你的问题中包含追溯将有助于确认这一点。）

检查导致该行的变量的大小并确认它们是否符合您的预期。

看看你的代码，most likely 问题是你在划分 H 和 W 通过 patch_size。如果 H 和 W 不是 patch_size 的倍数，您将需要一些东西来决定要丢弃哪些像素：np.reshape() 不会自行做出决定。

RuntimeError: shape '[128, 3, 5, 4, 5, 4]' is invalid for input of size 185856

RuntimeError: shape '[128, 3, 5, 4, 5, 4]' is invalid for input of size 185856

python

pytorch

computer-vision

pytorch-lightning