self.conv1 = BasicConv(3, self.inplanes, kernel_size=3, stride=1) self.feature_channels = [64, 128, 256, 512, 1024]
时间: 2023-10-24 20:05:06 浏览: 31
这段代码是一个类的初始化函数中的部分代码,其中包含了两行代码。
第一行代码中,`self.conv1` 是一个 `BasicConv` 类的实例,它将输入通道数为 3 的图像进行卷积操作,输出通道数为 `self.inplanes`。卷积核的大小为 3,步长为 1。
第二行代码中,`self.feature_channels` 是一个列表,其中包含了 5 个元素,分别对应了网络的 5 个阶段(每个阶段称为一个 stage),每个元素表示该 stage 输出的特征图的通道数。具体来说,第一个元素表示第一个 stage 的输出通道数为 64,第二个元素表示第二个 stage 的输出通道数为 128,以此类推,直到第五个元素表示第五个 stage 的输出通道数为 1024。
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self.conv2 = nn.Conv2d(middle_planes, out_planes, 3, 1, 1, bias=False, groups=groups)
This line of code defines a convolutional layer in a neural network using the PyTorch library.
- `self.conv2` is the name given to the layer.
- `nn.Conv2d` is the class used to create the convolutional layer.
- `middle_planes` is the number of input channels (also known as feature maps) to the layer.
- `out_planes` is the number of output channels (also known as filters) from the layer.
- `3` is the size of the convolution kernel (also known as filter size).
- `1` is the stride (distance between consecutive filter placements).
- `1` is the padding size (number of pixels added around the input feature map).
- `bias=False` means that no bias term will be added to the output of the layer.
- `groups` is used for group convolution, where input and output channels are divided into groups and only connections within a group are made.
Overall, this line of code creates a convolutional layer that takes an input feature map of size `(batch_size, middle_planes, height, width)` and produces an output feature map of size `(batch_size, out_planes, height, width)`.
self.conv2 = nn.Sequential( nn.Conv2d( in_channels = 24, out_channels = 12, kernel_size= 3, stride= 1, padding=1, ), nn.ReLU(), nn.MaxPool2d(kernel_size=2) ) self.fc1 = nn.Sequential( nn.Linear(in_features=12*7*7, out_features=196), nn.ReLU(), )这里的12*7*7是什么意思
这里的12*7*7是指在经过第二个卷积层之后,得到的特征图(feature map)的大小是 12x7x7,其中 12 是指特征图的通道数,7x7 是指特征图的空间大小。在神经网络中,特征图的大小会影响后面全连接层的输入大小,因此需要根据特征图的大小来确定全连接层的输入大小。在这个例子中,将 12x7x7 的特征图展平成一个长度为 12x7x7=588 的向量,作为全连接层的输入。
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