Interesting bug caused by getattr

题意：由 getattr 引起的有趣的 bug

问题背景：

I try to train 8 CNN models with the same structures simultaneously. After training a model on a batch, I need to synchronize the weights of the feature extraction layers in other 7 models.

我尝试同时训练8个具有相同结构的卷积神经网络（CNN）模型。在对一个批次的数据训练一个模型后，我需要同步其他7个模型中特征提取层的权重。

This is the model:        这是模型

class GNet(nn.Module):def __init__(self, dim_output, dropout=0.5):super(GNet, self).__init__()self.out_dim = dim_output# Load the pretrained AlexNet modelalexnet = models.alexnet(pretrained=True)self.pre_filtering = nn.Sequential(alexnet.features[:4])# Set requires_grad to False for all parameters in the pre_filtering networkfor param in self.pre_filtering.parameters():param.requires_grad = False# construct the feature extractor# every intermediate feature will be fed to the feature extractor# res: 25 x 25self.feat_ex1 = nn.Conv2d(192, 128, kernel_size=3, stride=1)# res: 25 x 25self.feat_ex2 = nn.Sequential(nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))# res: 25 x 25self.feat_ex3 = nn.Sequential(nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))# res: 13 x 13self.feat_ex4 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=2, padding=1),nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))# res: 13 x 13self.feat_ex5 = nn.Sequential(nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))# res: 13 x 13self.feat_ex6 = nn.Sequential(nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))# res: 13 x 13self.feat_ex7 = nn.Sequential(nn.BatchNorm2d(128),nn.Dropout(p=dropout),nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1))# define the flexible pooling field of each layer# use a full convolution layer here to perform flexible poolingself.fpf13 = nn.Conv2d(in_channels=448, out_channels=448, kernel_size=13, groups=448)self.fpf25 = nn.Conv2d(in_channels=384, out_channels=384, kernel_size=25, groups=384)self.linears = {}for i in range(self.out_dim):self.linears[f'linear_{i+1}'] = nn.Linear(832, 1)self.LogTanh = LogTanh()self.flatten = nn.Flatten()

And this is the function to synchronize the weights:

这是同步权重的函数：

def sync_weights(models, current_sub, sync_seqs):for sub in range(1, 9):if sub != current_sub:# Synchronize the specified layerswith torch.no_grad():for seq_name in sync_seqs:reference_layer = getattr(models[current_sub], seq_name)[2]layer = getattr(models[sub], seq_name)[2]layer.weight.data = reference_layer.weight.data.clone()if layer.bias is not None:layer.bias.data = reference_layer.bias.data.clone()

then an error is raised: 然后出现了一个错误：

'Conv2d' object is not iterable

which means the getattr() returns a Conv2D object. But if I remove [2]:

意思是 getattr() 函数返回了一个 Conv2D 对象。但是，如果我移除了 [2]

def sync_weights(models, current_sub, sync_seqs):for sub in range(1, 9):if sub != current_sub:# Synchronize the specified layerswith torch.no_grad():for seq_name in sync_seqs:reference_layer = getattr(models[current_sub], seq_name)layer = getattr(models[sub], seq_name)layer.weight.data = reference_layer.weight.data.clone()if layer.bias is not None:layer.bias.data = reference_layer.bias.data.clone()

I get another error:        我得到了另一个错误

'Sequential' object has no attribute 'weight'

which means the getattr() returns a Sequential. But previously it returns a Conv2D object. Does anyone know anything about this? For your information, the sync_seqs parameter passed in sync_weights is:

意思是 getattr() 现在返回的是一个 Sequential 模型，但之前它返回的是一个 Conv2D 对象。有人知道这是怎么回事吗？为了提供更多信息，sync_weights 函数中传入的 sync_seqs 参数是：

sync_seqs = ['feat_ex1','feat_ex2','feat_ex3','feat_ex4','feat_ex5','feat_ex6','feat_ex7'
]

问题解决：

In both instances, getattr is returning a Sequential, which in turn contains a bunch of objects. In the second case, you're directly assigning that Sequential to a variable, so reference_layer ends up containing a Sequential.

在这两种情况下，getattr 都返回了一个 Sequential 对象，而这个 Sequential 对象又包含了一系列的其他对象。在第二种情况下，你直接将这个 Sequential 对象赋值给了一个变量，因此 reference_layer 最终包含了一个 Sequential 对象。

In the first case, however, you're not doing that direct assignemnt. You're taking the Sequential object and then indexing it with [2]. That means reference_layer contains the third item in the Sequential, which is a Conv2d object.

在第一种情况下，你没有进行直接的赋值。你是先获取了 Sequential 对象，然后使用 [2] 对其进行索引。这意味着 reference_layer 包含的是 Sequential 中的第三个项目，这个项目是一个 Conv2D 对象。

Take a more simple example. Suppose I had a ListContainer class that did nothing except hold a list. I could then recreate your example as follows, with test1 corresponding to your first test case and vice versa:

以一个更简单的例子来说明。假设我有一个 ListContainer 类，它唯一的作用就是持有一个列表。然后我可以按照以下方式重现你的例子，其中 test1 对应你的第一个测试用例，反之亦然：

class ListContainer:def __init__(self, list_items):self.list_items = list_itemsletters = ["a", "b", "c"]
container = ListContainer(letters)test1 = getattr(container, "list_items")[0]
test2 = getattr(container, "list_items")print(type(test1)) # <class 'str'>
print(type(test2)) # <class 'list'>

In both tests, getattr itself is returning a list - but in the second, we're doing something with that list after we get it, so test2 ends up being a string instead.

在两次测试中，getattr 本身都返回了一个列表——但在第二次测试中，我们在获取到这个列表之后对它进行了某种操作，所以 test2 最终变成了一个字符串而不是列表。