class BasicBlock1(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock, self).__init__() g\对不对
时间: 2024-03-08 08:46:06 浏览: 104
这段代码有一个问题:在 `super()` 函数中应该使用当前类的名称 `BasicBlock1` 而不是 `BasicBlock`,因为这个类的名称是 `BasicBlock1`。正确的代码应该如下所示:
```
class BasicBlock1(layers.Layer):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock1, self).__init__()
# rest of the code
```
这样,`super()` 函数将会调用 `BasicBlock1` 的父类的构造函数,而不是 `BasicBlock` 的。
相关问题
class BasicBlock(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1):定义的basicblock模块之后如何放到def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)后面
可以使用`BasicBlock`类来定义一个基本块模块,然后在`MEAN_Spot`函数中调用该模块。具体实现方法如下:
```python
class BasicBlock(layers.Layer):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = layers.Conv2D(out_channels, kernel_size=3, strides=stride, padding='same', use_bias=False)
self.bn1 = layers.BatchNormalization()
self.relu = layers.ReLU()
self.conv2 = layers.Conv2D(out_channels * self.expansion, kernel_size=3, strides=1, padding='same', use_bias=False)
self.bn2 = layers.BatchNormalization()
if stride != 1 or in_channels != out_channels * self.expansion:
self.shortcut = keras.Sequential([
layers.Conv2D(out_channels * self.expansion, kernel_size=1, strides=stride, use_bias=False),
layers.BatchNormalization()
])
else:
self.shortcut = lambda x: x
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x += self.shortcut(inputs)
x = self.relu(x)
return x
def MEAN_Spot(opt):
# channel 1
inputs1 = layers.Input(shape=(42, 42, 1))
inputs2 = layers.Input(shape=(42, 42, 1))
inputs3 = layers.Input(shape=(42, 42, 1))
# merge 1
inputs = layers.Concatenate()([inputs1, inputs2, inputs3])
x = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
# basic block
x = BasicBlock(3, 16)(x)
x = BasicBlock(16, 32, stride=2)(x)
x = BasicBlock(32, 64, stride=2)(x)
# global average pooling
x = layers.GlobalAveragePooling2D()(x)
# output
outputs = layers.Dense(1, activation='sigmoid')(x)
# define model
model = keras.Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
return model
```
这里我们在`MEAN_Spot`函数中使用`BasicBlock`类来构建基本块模块,并且将该模块的输出作为下一个模块的输入。最后使用全局平均池化层和全连接层得到最终的输出。
class BasicBlock2D(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层
为了用稀疏表示替换全连接层,需要先将特征图展平,然后使用稀疏矩阵与其进行乘法运算。
以下是修改后的代码:
```
import torch.sparse as sparse
class ResNet18_2D(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet18_2D, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1)
self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2)
self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2)
self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
# 替换全连接层
self.fc = nn.Identity()
def _make_layer(self, block, out_channels, num_blocks, stride):
layers = []
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.maxpool(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
# 使用稀疏矩阵与展平后的特征图进行乘法运算
out = sparse.mm(out, nn.Parameter(torch.randn(out.size(1), 512)))
return out
```
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