用tensorflow的layers.Layer模块改写 class SpatialAttention(nn.Module): def init(self): super(SpatialAttention, self).init() self.conv1 = nn.Conv2d(2, 1, 3, padding = 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim = 1, keepdim = True) max_out, _ = torch.max(x, dim = 1, keepdim = True) x = torch.cat([avg_out, max_out], dim = 1) x = self.conv1(x) return self.sigmoid(x)
时间: 2023-11-28 18:05:18 浏览: 41
以下是使用TensorFlow的layers.Layer模块改写的代码:
```python
import tensorflow as tf
class SpatialAttention(tf.keras.layers.Layer):
def __init__(self):
super(SpatialAttention, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(1, 3, padding='same', use_bias=False)
self.sigmoid = tf.keras.layers.Activation('sigmoid')
def call(self, inputs):
avg_out = tf.reduce_mean(inputs, axis=1, keepdims=True)
max_out = tf.reduce_max(inputs, axis=1, keepdims=True)
x = tf.concat([avg_out, max_out], axis=1)
x = self.conv1(x)
return self.sigmoid(x)
```
在TensorFlow的layers.Layer模块中,我们使用`__init__()`方法来初始化层的参数,使用`call()`方法来定义层的前向传播逻辑。`Conv2D`和`Activation`分别对应PyTorch中的`nn.Conv2d`和`nn.Sigmoid`。`reduce_mean`和`reduce_max`分别对应PyTorch中的`torch.mean`和`torch.max`。`concat`用于在给定维度上连接不同的张量。