将以下模型转化为cfg格式class BlockA(nn.Module): def __init__(self, in_channels, out_channels, r, s=2): super().__init__() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=s, padding=1) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=s) self.act1 = nn.PReLU() self.act2 = nn.PReLU() self.bn1 = nn.BatchNorm1d(out_channels) self.bn2 = nn.BatchNorm1d(out_channels) self.bn3 = nn.BatchNorm1d(out_channels) self.dropout = nn.Dropout(r) def forward(self, x): i = self.conv3(x) i = self.bn3(i) x = self.conv1(x) x = self.bn1(x) x = self.act1(x) x = self.dropout(x) x = self.conv2(x) x = self.bn2(x) x = x+i x = self.act2(x) return x

回答：

以下是将该模型转化为cfg格式的代码：

[blockA]
type = "BlockA"
in_channels = ${in_channels}
out_channels = ${out_channels}
r = ${r}
s = ${s}
conv1_kernel_size = 3
conv1_stride = ${s}
conv1_padding = 1
conv2_kernel_size = 3
conv2_stride = 1
conv2_padding = 1
conv3_kernel_size = 1
conv3_stride = ${s}
act1_type = "PReLU"
act2_type = "PReLU"
bn1_type = "BatchNorm1d"
bn2_type = "BatchNorm1d"
bn3_type = "BatchNorm1d"
dropout_prob = ${r}