planes[0].at<float>(y, x) = 0;

这行代码是使用 OpenCV 库中的 Mat 类型变量 planes 的第一个通道（即 planes[0]）对坐标为 (x, y) 的像素值进行赋值，赋值为 0。其中，at 方法是访问 Mat 类型变量中指定位置像素值的函数。在这里，at 方法的参数为 y 和 x，表示要访问的像素在矩阵中的行和列，同时返回的是一个 float 类型的值，最后被赋值为 0。

相关问题

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) def forward(self,x): x0 = self.botneck(x) x = self.conv1(x) if self.net_type == 'sa': x = self.sa(x) #x = self.conv2(x) elif self.net_type == 'cbam': x = self.ca(x) * x x = self.spa(x) * x x = self.conv2(x) elif self.net_type == 'ta': x = self.ca(x) * x x = self.spa(x) * x x = self.sa(x) x = self.conv2(x) x = x + x0 x = self.pool(x) return x 改写为tensorflow形式

import tensorflow as tf

class EnhancedResidual(tf.keras.layers.Layer):
def __init__(self, in_c, out_c, fm_sz, net_type='ta', **kwargs):
super(EnhancedResidual, self).__init__(**kwargs)
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()
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)

def call(self, x):
x0 = self.botneck(x)
x = self.conv1(x)
if self.net_type == 'sa':
x = self.sa(x)
# x = self.conv2(x)
elif self.net_type == 'cbam':
x = self.ca(x) * x
x = self.spa(x) * x
x = self.conv2(x)
elif self.net_type == 'ta':
x = self.ca(x) * x
x = self.spa(x) * x
x = self.sa(x)
x = self.conv2(x)
x = x + x0
x = self.pool(x)
return x

class SE_Block(nn.Module): # Squeeze-and-Excitation block def __init__(self, in_planes): super(SE_Block, self).__init__() self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.conv1 = nn.Conv2d(in_planes, in_planes // 16, kernel_size=1) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(in_planes // 16, in_planes, kernel_size=1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.avgpool(x) x = self.conv1(x) x = self.relu(x) x = self.conv2(x) out = self.sigmoid(x) return out 将以上代码改成keras格式

在Keras中实现SE_Block可以如下所示：

from keras.layers import AveragePooling2D, Conv2D, ReLU, Activation, Multiply, GlobalAveragePooling2D

class SE_Block(keras.layers.Layer):
    def __init__(self, in_planes):
        super(SE_Block, self).__init__()
        self.avgpool = AveragePooling2D(pool_size=(1, 1))
        self.conv1 = Conv2D(in_planes // 16, kernel_size=1)
        self.relu = ReLU()
        self.conv2 = Conv2D(in_planes, kernel_size=1)
        self.sigmoid = Activation('sigmoid')

    def call(self, x):
        x = self.avgpool(x)
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        out = self.sigmoid(x)
        return Multiply()([out, x])

需要注意的是，Keras的Layer和Module有一些差别，因此代码中做了一些相应的改动。同时，由于Keras中没有提供直接的Sigmoid激活函数的Layer，因此使用了Activation('sigmoid')替代。另外，SE_Block的输出需要与输入进行相乘，因此在Keras中使用了Multiply()函数实现。