解释每一句super(CustomCSPPAN, self).__init__() out_channels = [max(round(c * width_mult), 1) for c in out_channels] block_num = max(round(block_num * depth_mult), 1) act = get_act_fn( act, trt=trt) if act is None or isinstance(act, (str, dict)) else act self.num_blocks = len(in_channels) self.data_format = data_format self._out_channels = out_channels in_channels = in_channels[::-1] fpn_stages = [] fpn_routes = [] for i, (ch_in, ch_out) in enumerate(zip(in_channels, out_channels)): if i > 0: ch_in += ch_pre // 2 stage = nn.Sequential() for j in range(stage_num): stage.add_sublayer( str(j), eval(stage_fn)(block_fn, ch_in if j == 0 else ch_out, ch_out, block_num, act=act, spp=(spp and i == 0))) if drop_block: stage.add_sublayer('drop', DropBlock(block_size, keep_prob))
时间: 2024-02-10 17:34:04 浏览: 34
这段代码是一个类的初始化方法。它首先调用了父类的初始化方法。然后,根据给定的参数计算了一些输出通道数和块的数量,以及激活函数。接下来,它计算了输入通道和输出通道的反向列表,然后创建了一个空的列表fpn_stages和fpn_routes。接下来,它通过循环遍历输入通道和输出通道的zip对象,计算了每个阶段的输入通道和输出通道,并创建了一个Sequential对象作为该阶段的模型。在每个阶段中,它使用eval()函数来动态地创建一个新的block_fn对象,并将其添加到Sequential对象中。如果drop_block参数为True,则它还会添加一个DropBlock层到每个阶段的末尾。
相关问题
把这段代码里的location_embedding_dim去掉class my_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads location_embedding_dim = 0 self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features
这段代码里的location_embedding_dim是一个参数,它表示位置嵌入的维度。如果你想去掉它,可以把它的值设为0,然后在构造函数中把self.location_embedding_dim也设为0即可。具体来说,修改后的代码如下:
```
class my_GAT(torch.nn.Module):
def __init__(self, in_channels, out_channels, n_heads, filters_1, filters_2, dropout):
super(my_GAT, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.n_heads = n_heads
self.filters_1 = filters_1
self.filters_2 = filters_2
self.dropout = dropout
self.location_embedding_dim = 0
self.setup_layers()
def setup_layers(self):
self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1)
self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False)
def forward(self, edge_indices, features, location_embedding):
features = self.GAT_1(features, edge_indices)
features = torch.nn.functional.relu(features)
features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training)
features = self.GAT_2(features, edge_indices)
return features
```
super().__init__(*args, **kwargs)
引用和引用中的代码片段中的`super().__init__(*args, **kwargs)`会导致错误。这是因为`object.init()`函数不接受任何参数,而在这些代码中,`super().__init__(*args, **kwargs)`尝试将参数传递给父类的`init()`函数。如果你想在子类的`__init__`方法中传递参数给父类的`__init__`方法,你应该使用正确的语法。例如,在引用和引用中的代码中,你可以删除`super().__init__(*args, **kwargs)`并将`args`分配给`self.a`,就像这样:`self.a = args`。这样可以避免错误。
至于引用中的代码片段,它尝试在调用父类的`__init__`方法之前执行一些操作。然而,在这个示例中,我们并没有看到完整的代码,因此无法确定这段代码的目的和正确性。不过,如果你想在调用父类的`__init__`方法之前执行某些操作,你可以在调用`super().__init__(parent, *args, **kwargs)`之前添加你自己的代码。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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