class BasicBlock(nn.Module): def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.norm = norm_layer(dim) self.conv = conv_layer(net_depth, dim, kernel_size, gate_act) def forward(self, x): identity = x x = self.norm(x) x = self.conv(x) x = identity + x return x转化为Paddle框架写法

class BasicBlock(fluid.dygraph.Layer):
def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=fluid.dygraph.nn.functional.sigmoid):
super(BasicBlock, self).__init__()
self.norm = norm_layer(dim)
self.conv = conv_layer(net_depth, dim, kernel_size, gate_act)
def forward(self, x):
identity = x
x = self.norm(x)
x = self.conv(x)
x = identity + x
return x

相关问题

将以下代码转化为Paddle框架写法class BasicLayer(nn.Module): def __init__(self, net_depth, dim, depth, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.dim = dim self.depth = depth # build blocks self.blocks = nn.ModuleList([ BasicBlock(net_depth, dim, kernel_size, conv_layer, norm_layer, gate_act) for i in range(depth)]) def forward(self, x): for blk in self.blocks: x = blk(x) return x

class BasicLayer(paddle.nn.Layer):
def __init__(self, net_depth, dim, depth, kernel_size=3, conv_layer=paddle.nn.Conv2D, norm_layer=paddle.nn.BatchNorm, gate_act=paddle.nn.Sigmoid):
super(BasicLayer, self).__init__()
self.dim = dim
self.depth = depth

class Net(nn.Module): def __init__(self,): super().__init__() conv_dim=64 encoder_dim = [conv_dim] + [64, 128, 256, 512 ] self.encoder = resnet34d(pretrained=False,in_chans=CFG.one_depth) self.decoder = SmpUnetDecoder( encoder_channels=[0] + encoder_dim, decoder_channels=[256, 128, 64, 32, 16], n_blocks=5, use_batchnorm=True, center=False, attention_type=None, ) self.logit = nn.Conv2d(16,1,kernel_size=1) #-- pool attention weight self.weight = nn.ModuleList([ nn.Sequential( nn.Conv2d(dim, dim, kernel_size=3, padding=1), nn.ReLU(inplace=True), ) for dim in encoder_dim ])

这是一个 PyTorch 中的神经网络模型类定义，包含了一个卷积编码器和一个 SmpUnet 解码器。其中，卷积编码器使用了 resnet34d 预训练模型，解码器的输入通道数为 [0] + encoder_dim，输出通道数为 [256, 128, 64, 32, 16]，使用了 5 个块，每个块中都包含了一个卷积层和一个 ReLU 激活函数。最终输出的 logit 是一个 1x1 的卷积层，输出通道数为 1。此外，还定义了一个 ModuleList，其中包含了多个卷积层，用于计算权重。