nn.batchnorm2d 参数设定
时间: 2023-04-12 22:02:33 浏览: 75
nn.batchnorm2d是PyTorch中的一个二维批量归一化层,用于在深度学习模型中对输入数据进行归一化处理,以加速训练过程和提高模型的准确性。
该层的参数设定包括:
1. num_features:输入数据的特征数,即通道数。
2. eps:防止分母为零的小数值,通常设置为1e-5。
3. momentum:动量参数,用于计算滑动平均值和滑动方差,通常设置为.1。
4. affine:是否对归一化后的数据进行仿射变换,即乘以一个可学习的缩放因子和加上一个可学习的偏移量,通常设置为True。
5. track_running_stats:是否跟踪滑动平均值和滑动方差,通常设置为True。
6. device:指定运行该层的设备,通常设置为None,表示使用默认设备。
相关问题
介绍torch.nn.BatchNorm2d的weight和bias
torch.nn.BatchNorm2d的weight和bias分别是用于归一化的权重和偏置项。在Batch Normalization中,weight用于缩放归一化的结果,而bias用于平移归一化的结果。权重和偏置项可以通过设置affine参数为True来启用,默认情况下为True。具体来说,weight用于调整归一化后的特征图的尺度,而bias用于调整特征图的偏移。通过调整权重和偏置项,我们可以对归一化结果进行进一步的灵活调整,以适应不同的网络结构和任务需求。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [Pytorch中torch.nn.conv2d和torch.nn.functional.conv2d的区别](https://blog.csdn.net/XU_MAN_/article/details/122557443)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 33.333333333333336%"]
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[ .reference_list ]
class ASPP(nn.Module): def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1): super(ASPP, self).__init__() self.branch1 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch2 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=4 * rate, dilation=4 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch3 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=8 * rate, dilation=8 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch4 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 * rate, dilation=12 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch5 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=16 * rate, dilation=16 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch6 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=20 * rate, dilation=20 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch7 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=24 * rate, dilation=24 * rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch8_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True) self.branch8_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom) self.branch8_relu = nn.ReLU(inplace=True) self.conv_cat = nn.Sequential( nn.Conv2d(dim_out * 8, dim_out, 1, 1, padding=0, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), )用1×3卷积和3×1卷积代替这段代码的3×3卷积
可以将self.branch2~self.branch7中的3×3卷积改为1×3卷积和3×1卷积的组合,代码如下:
```
self.branch2 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 4 * rate), dilation=4 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(4 * rate, 0), dilation=4 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
self.branch3 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 8 * rate), dilation=8 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(8 * rate, 0), dilation=8 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
self.branch4 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 12 * rate), dilation=12 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(12 * rate, 0), dilation=12 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
self.branch5 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 16 * rate), dilation=16 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(16 * rate, 0), dilation=16 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
self.branch6 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 20 * rate), dilation=20 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(20 * rate, 0), dilation=20 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
self.branch7 = nn.Sequential(
nn.Conv2d(dim_in, dim_out, (1, 3), 1, padding=(0, 24 * rate), dilation=24 * rate, bias=True),
nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(24 * rate, 0), dilation=24 * rate, bias=True),
nn.BatchNorm2d(dim_out, momentum=bn_mom),
nn.ReLU(inplace=True),
)
```
其中,(1,3)表示1×3卷积核,(3,1)表示3×1卷积核,padding参数按照3×3卷积的padding进行设置。
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