class EnhancedResidual(nn.Module): def __init__(self,in_c,out_c,fm_sz,net_type = 'ta'): super(EnhancedResidual,self).__init__() self.net_type = net_type self.conv1 = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = in_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(in_c), nn.ReLU(), ) self.conv2 = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), ) self.botneck = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pool = nn.MaxPool2d(kernel_size = 2,stride = 2) if net_type == 'ta': self.spa = SpatialAttention() self.ca = ChannelAttention(in_planes = in_c,ratio = in_c) self.sa = MultiHeadSelfAttention(in_c = in_c,out_c = in_c // 4,head_n = 4,fm_sz = fm_sz) elif net_type == 'sa': self.sa = MultiHeadSelfAttention(in_c = in_c,out_c = out_c // 4,head_n = 4,fm_sz = fm_sz) elif net_type == 'cbam': self.spa = SpatialAttention() self.ca = ChannelAttention(in_planes = in_c,ratio = in_c) 改写为tensorflow形式
时间: 2024-03-12 08:50:31 浏览: 24
把上述PyTorch代码转换成TensorFlow代码如下:
```python
import tensorflow as tf
class EnhancedResidual(tf.keras.layers.Layer):
def __init__(self, in_c, out_c, fm_sz, net_type='ta'):
super(EnhancedResidual, self).__init__()
self.net_type = net_type
self.conv1 = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=in_c, kernel_size=3, padding='same'),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU()
])
self.conv2 = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=out_c, kernel_size=3, padding='same'),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU()
])
self.botneck = tf.keras.layers.Conv2D(filters=out_c, kernel_size=1)
self.pool = tf.keras.layers.MaxPool2D(pool_size=2, strides=2)
if net_type == 'ta':
self.spa = SpatialAttention() # 需要自行定义SpatialAttention类
self.ca = ChannelAttention(in_planes=in_c, ratio=in_c) # 需要自行定义ChannelAttention类
self.sa = MultiHeadSelfAttention(in_c=in_c, out_c=in_c // 4, head_n=4, fm_sz=fm_sz) # 需要自行定义MultiHeadSelfAttention类
elif net_type == 'sa':
self.sa = MultiHeadSelfAttention(in_c=in_c, out_c=out_c // 4, head_n=4, fm_sz=fm_sz) # 需要自行定义MultiHeadSelfAttention类
elif net_type == 'cbam':
self.spa = SpatialAttention() # 需要自行定义SpatialAttention类
self.ca = ChannelAttention(in_planes=in_c, ratio=in_c) # 需要自行定义ChannelAttention类
def call(self, inputs):
x = self.conv1(inputs)
x = self.conv2(x)
shortcut = self.botneck(inputs)
x += shortcut
x = self.pool(x)
if self.net_type == 'ta':
x = self.spa(x) * x
x = self.ca(x) * x
x = self.sa(x) + x
elif self.net_type == 'sa':
x = self.sa(x) + x
elif self.net_type == 'cbam':
x = self.spa(x) * x
x = self.ca(x) * x
return x
```
需要注意的是,这里用到了一些自定义的类,如SpatialAttention、ChannelAttention和MultiHeadSelfAttention,需要根据具体情况自行实现。