kernel_size=3, padding=1会怎样改变尺寸

使用kernel_size=3和padding=1会保持输入图像的尺寸不变。这是因为padding=1将在输入图像的周围增加一个像素边框，而kernel_size=3意味着在每个像素的周围使用3x3的卷积核进行卷积操作。因此，输出图像的尺寸与输入图像的尺寸相同。

相关问题

def __init__(self, num_classes=77): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=5), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) self.classifier = nn.Linear(256, num_classes)

This is the constructor of the AlexNet class in PyTorch. It defines the architecture of the AlexNet model, which is a deep convolutional neural network designed for image classification tasks.

The model consists of two main parts: the feature extractor and the classifier. The feature extractor is composed of several convolutional layers followed by max pooling layers, which extract high-level features from the input image. The classifier is a fully connected layer that maps the extracted features to the output classes.

The input to the model is a 3-channel image with height and width of at least 227 pixels. The output is a probability distribution over the 77 possible classes (by default).

Overall, this model is effective for image classification tasks and has achieved state-of-the-art performance on several benchmark datasets.

class ImageTransformerModel(nn.Module): def init(self): super().init() self._initial = nn.Sequential( nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4, padding_mode='reflect'), nn.InstanceNorm2d(32, affine=True), nn.ReLU(inplace=True), ) self._down_blocks = nn.Sequential( DownBlock(32, 64, kernel_size=3), DownBlock(64, 128, kernel_size=3), ) self._residual_blocks = nn.Sequential( *[ResidualBlock(128, kernel_size=3) for _ in range(5)] ) self._up_blocks = nn.Sequential( UpBlock(128, 64, kernel_size=3), UpBlock(64, 32, kernel_size=3), ) self._final = nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4, padding_mode='reflect')

这是一个用于图像转换的神经网络模型，包含了卷积层、实例归一化层、残差块、上采样块等不同类型的层。具体来说，该模型包含以下几个部分：

1. 输入层：输入图像的大小为 3（RGB 三通道）。
2. 初始层：使用一个卷积层将输入图像转换为 32 个特征图。然后应用实例归一化层和 ReLU 激活函数。
3. 下采样块：包含两个下采样层，分别将特征图从 32 增加到 64，再从 64 增加到 128。
4. 残差块：包含 5 个残差块，每个残差块包括两个卷积层和一个跳跃连接。
5. 上采样块：包含两个上采样层，将特征图从 128 减少到 64，再从 64 减少到 32。
6. 输出层：最后使用一个卷积层将特征图转换为 3 个通道的输出图像。

总体来说，这个模型可以将一张输入图像转换为一张新的风格化图像。