class UNET(tf.keras.Model): def __init__(self, in_channel, out_channel): super(UNET, self).__init__() self.layer1 = conv_block(in_channel, out_channel) self.layer2 = Downsample(out_channel) self.layer3 = conv_block(out_channel, out_channel*2) self.layer4 = Downsample(out_channel*2) self.layer5 = conv_block(out_channel*2, out_channel*4) self.layer6 = Downsample(out_channel*4) self.layer7 = conv_block(out_channel*4, out_channel*8) self.layer8 = Downsample(out_channel*8) self.layer9 = conv_block(out_channel*8, out_channel*16) self.layer10 = Upsample(out_channel*16) self.layer11 = conv_block(out_channel*16, out_channel*8) self.layer12 = Upsample(out_channel*8) self.layer13 = conv_block(out_channel*8, out_channel*4) self.layer14 = Upsample(out_channel*4) self.layer15 = conv_block(out_channel*4, out_channel*2) self.layer16 = Upsample(out_channel*2) self.layer17 = conv_block(out_channel*2, out_channel) self.layer18 = tf.keras.layers.Conv2DTranspose(filters=in_channel, kernel_size=1, strides=1, activation=None) self.act = tf.keras.layers.Activation('sigmoid') #激活函数 def call(self, x): x = self.layer1(x) f1 = x x = self.layer2(x) x = self.layer3(x) f2 = x x = self.layer4(x) x = self.layer5(x) f3 = x x = self.layer6(x) x = self.layer7(x) f4 = x x = self.layer8(x) x = self.layer9(x) x = self.layer10(x, f4) x = self.layer11(x) x = self.layer12(x, f3) x = self.layer13(x) x = self.layer14(x, f2) x = self.layer15(x) x = self.layer16(x, f1) x = self.layer17(x) x = self.layer18(x) return self.act(x) x = tf.random.normal(shape=(2, 256, 256, 1)) y = tf.random.normal(shape=(2, 256, 256, 1)) model = UNET(in_channel=1, out_channel=64) loss_fn = tf.keras.losses.BinaryCrossentropy() optimizer = tf.keras.optimizers.Adam() TypeError: conv_block() missing 1 required positional argument: 'name'
时间: 2023-12-23 15:03:39 浏览: 37
从错误信息来看,是因为在创建`UNET`类的时候,调用了`conv_block()`函数,但是缺少了一个必需的参数`name`。因此建议您检查一下`conv_block()`函数的定义,确保它需要的参数是否包括`name`,并且在调用时传递了正确的参数。
此外,如果您能提供更多代码或上下文信息,我们可以更好地理解您的问题,并提供更具体的帮助。
相关问题
class D_UNet(nn.Module): def __init__(self, n_classes, in_chans, dim=350, aux=False):#dim表嵌入的维度 super(D_UNet, self).__init__()
这段代码定义了一个名为D_UNet的类,该类是nn.Module类的子类。该类有一个__init__方法,其参数包括n_classes、in_chans、dim和aux。n_classes表示分类的类数,in_chans表示输入图像的通道数,dim表示图像的维度,默认为350,aux表示是否使用辅助分支。
class UNet(paddle.nn.Layer): def __init__(self, num_classes): super(UNet, self).__init__() self.conv_1 = paddle.nn.Conv2D(3, 32, kernel_size=3, stride=2, padding='same') self.bn = paddle.nn.BatchNorm2D(32) self.relu = paddle.nn.ReLU() in_channels = 32 self.encoders = [] self.encoder_list = [64, 128, 256] self.decoder_list = [256, 128, 64, 32] # 根据下采样个数和配置循环定义子Layer,避免重复写一样的程序 for out_channels in self.encoder_list: block = self.add_sublayer('encoder_{}'.format(out_channels), Encoder(in_channels, out_channels)) self.encoders.append(block) in_channels = out_channels self.decoders = [] # 根据上采样个数和配置循环定义子Layer,避免重复写一样的程序 for out_channels in self.decoder_list: block = self.add_sublayer('decoder_{}'.format(out_channels), Decoder(in_channels, out_channels)) self.decoders.append(block) in_channels = out_channels self.output_conv = paddle.nn.Conv2D(in_channels, num_classes, kernel_size=3, padding='same') def forward(self, inputs): y = self.conv_1(inputs) y = self.bn(y) y = self.relu(y) for encoder in self.encoders: y = encoder(y) for decoder in self.decoders: y = decoder(y) y = self.output_conv(y) return y怎么将该unet网络的层数改为5层
要将UNet网络的层数改为5层,可以按照以下步骤进行修改:
1. 将Encoder和Decoder的个数都增加到4个,分别对应下采样和上采样的层数,即encoder_list = [64, 128, 256, 512] 和 decoder_list = [512, 256, 128, 64, 32]。
2. 在网络的构造函数中增加一个新的Encoder和Decoder,代码如下:
```python
self.encoder_512 = Encoder(256, 512)
self.encoders.append(self.encoder_512)
self.decoder_32 = Decoder(64, 32)
self.decoders.insert(0, self.decoder_32)
```
这里新增了一个从256通道到512通道的Encoder和一个从64通道到32通道的Decoder,并分别加入到encoders和decoders中。
3. 最后,将输出层的输入通道数改为32,即将self.output_conv = paddle.nn.Conv2D(in_channels, num_classes, kernel_size=3, padding='same')中的in_channels改为32。
修改后的完整代码如下:
```python
class UNet(paddle.nn.Layer):
def __init__(self, num_classes):
super(UNet, self).__init__()
self.conv_1 = paddle.nn.Conv2D(3, 32,
kernel_size=3,
stride=2,
padding='same')
self.bn = paddle.nn.BatchNorm2D(32)
self.relu = paddle.nn.ReLU()
in_channels = 32
self.encoders = []
self.encoder_list = [64, 128, 256, 512]
self.decoder_list = [512, 256, 128, 64, 32]
self.decoders = []
# 根据下采样个数和配置循环定义子Layer,避免重复写一样的程序
for out_channels in self.encoder_list:
block = self.add_sublayer('encoder_{}'.format(out_channels),
Encoder(in_channels, out_channels))
self.encoders.append(block)
in_channels = out_channels
# 新增一个Encoder
self.encoder_512 = Encoder(256, 512)
self.encoders.append(self.encoder_512)
# 根据上采样个数和配置循环定义子Layer,避免重复写一样的程序
for out_channels in self.decoder_list:
block = self.add_sublayer('decoder_{}'.format(out_channels),
Decoder(in_channels, out_channels))
self.decoders.append(block)
in_channels = out_channels
# 新增一个Decoder
self.decoder_32 = Decoder(64, 32)
self.decoders.insert(0, self.decoder_32)
self.output_conv = paddle.nn.Conv2D(32,
num_classes,
kernel_size=3,
padding='same')
def forward(self, inputs):
y = self.conv_1(inputs)
y = self.bn(y)
y = self.relu(y)
for encoder in self.encoders:
y = encoder(y)
for decoder in self.decoders:
y = decoder(y)
y = self.output_conv(y)
return y
```
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