class ChannelPool3d(AvgPool1d): def __init__(self, kernel_size, stride, padding): super(ChannelPool3d, self).__init__(kernel_size, stride, padding) self.pool_1d = AvgPool1d(self.kernel_size, self.stride, self.padding, self.ceil_mode) def forward(self, inp): n, c, d, w, h = inp.size() inp = inp.view(n,c,d*w*h).permute(0,2,1) pooled = self.pool_1d(inp) c = int(c/self.kernel_size[0]) return inp.view(n,c,d,w,h)每句话什么意思

这段代码是定义了一个名为ChannelPool3d的类，它继承自AvgPool1d类。它有三个参数：kernel_size表示池化核的大小，stride表示步长，padding表示填充大小。在初始化时，它调用了父类的构造函数，并且创建了一个AvgPool1d对象作为类的属性。在前向传播时，输入数据的维度为[n, c, d, w, h]，其中n表示batch size，c表示通道数，d表示深度，w和h表示宽和高。然后将输入数据的维度重新排列为[n,d*w*h,c]再进行一次转置，使得通道数c在第三个维度上，接着将其输入到pool_1d中进行池化操作。最后，将通道数c重新计算为c/kernel_size[0]，并将结果重新排列为[n,c,d,w,h]返回。

相关问题

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}

解释代码class BlockB(nn.Module): def __init__(self, in_channels, out_channels, r, s=1): super().__init__() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) 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 = x 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

这是一个基类 BlockB，它继承自 nn.Module。构造函数中定义了三个卷积层、两个激活函数、三个批归一化层和一个 Dropout 层。在 forward 函数中，通过两个卷积层和两个批归一化层对输入 x 进行卷积操作，然后使用 PReLU 激活函数进行非线性变换，并使用 Dropout 层进行正则化。最后将原始的输入 x 与卷积结果相加，再进行一次 PReLU 变换，得到最终的输出 x。