提取药物embeddings的时候出了问题

Question

大家好，我在用GIN模型将SMILE数据转为图数据后提取embeddings的时候出现了下列问题。主要是shape的问题，但是不知道如何修改？尝试修改了模型，但是没有解决。请大家给我具体修改意见和代码。谢谢。

```

RuntimeError Traceback (most recent call last)
Cell In [12], line 11
8 with torch.no_grad():
9 for idx, data in enumerate(graph_data_list):
10 # 获取模型的输出和嵌入
---> 11 output, embedding = model(data) # 返回分类输出和嵌入
13 # 打印每个batch的output和embedding的shape
14 print(f'Batch {idx} - Output shape: {output.shape}, Embedding shape: {embedding.shape}')

File /shared-libs/python3.9/py/lib/python3.9/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

Cell In [7], line 30, in GINModel.forward(self, data)
28 def forward(self, data):
29 x, edge_index = data.x, data.edge_index
---> 30 x = self.conv1(x, edge_index)
31 x = self.conv2(x, edge_index)
32 return self.fc(x)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

Cell In [7], line 19, in GINConv.forward(self, x, edge_index)
17 row, col = edge_index
18 x_out = self.mlp(x)
---> 19 return F.relu(x_out + x[row])

RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 0

```**

Answer 1

让【道友老李】来帮你解答，本回答参考通义千问大模型编写，并整理提供，如果还有疑问可以点击头像关注私信或评论。

如果答案让您满意，请采纳、关注，非常感谢！

你好！根据你提供的错误信息，问题出在 `GINConv` 层的前向传播过程中。具体来说，`x_out` 和 `x[row]` 的形状不匹配。我们可以通过以下几个步骤来解决这个问题：

1. 检查输入数据的形状：确保输入到模型的数据形状是正确的。
2. 调整 GINConv 层的实现：确保 GINConv 层的实现正确处理了输入数据的形状。

下面是一个详细的解决方案，包括代码示例和解释。

1. 检查输入数据的形状

首先，我们需要确保输入到模型的数据形状是正确的。你可以打印出 data.x 和 data.edge_index 的形状来检查它们是否符合预期。

for idx, data in enumerate(graph_data_list):
    print(f"Batch {idx} - x shape: {data.x.shape}, edge_index shape: {data.edge_index.shape}")

2. 调整 GINConv 层的实现

假设你的 GINConv 层的实现如下：

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

class GINConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(GINConv, self).__init__()
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, out_channels),
            nn.ReLU(),
            nn.Linear(out_channels, out_channels)
        )

    def forward(self, x, edge_index):
        row, col = edge_index
        x_out = self.mlp(x)
        return F.relu(x_out + x[row])

我们需要确保 x_out 和 x[row] 的形状一致。一种可能的解决方案是将 x[row] 与 x_out 进行适当的广播操作。我们可以使用 torch.index_select 来选择 x 中的特定行，以确保形状一致。

修改后的 GINConv 层实现如下：

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

class GINConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(GINConv, self).__init__()
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, out_channels),
            nn.ReLU(),
            nn.Linear(out_channels, out_channels)
        )

    def forward(self, x, edge_index):
        row, col = edge_index
        x_out = self.mlp(x)
        x_row = torch.index_select(x, 0, row)  # 选择 x 中的特定行
        return F.relu(x_out + x_row)

3. 完整的模型实现

假设你的完整模型实现如下：

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

class GINConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(GINConv, self).__init__()
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, out_channels),
            nn.ReLU(),
            nn.Linear(out_channels, out_channels)
        )

    def forward(self, x, edge_index):
        row, col = edge_index
        x_out = self.mlp(x)
        x_row = torch.index_select(x, 0, row)  # 选择 x 中的特定行
        return F.relu(x_out + x_row)

class GINModel(nn.Module):
    def __init__(self, in_channels, hidden_channels, num_classes):
        super(GINModel, self).__init__()
        self.conv1 = GINConv(in_channels, hidden_channels)
        self.conv2 = GINConv(hidden_channels, hidden_channels)
        self.fc = nn.Linear(hidden_channels, num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = self.conv2(x, edge_index)
        return self.fc(x), x  # 返回分类输出和嵌入

# 示例数据
graph_data_list = [your_graph_data_here]

# 创建模型实例
model = GINModel(in_channels=3, hidden_channels=64, num_classes=10)

# 前向传播
with torch.no_grad():
    for idx, data in enumerate(graph_data_list):
        output, embedding = model(data)  # 返回分类输出和嵌入
        print(f'Batch {idx} - Output shape: {output.shape}, Embedding shape: {embedding.shape}')

4. 测试和调试

运行上述代码，确保没有形状不匹配的错误。如果仍然有问题，请检查输入数据的形状，并确保 edge_index 和 x 的形状是正确的。

希望这些修改能帮助你解决问题！如果有任何进一步的问题，请随时告诉我。