258 lines
11 KiB
Python
258 lines
11 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from nets.xception import xception
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from nets.mobilenetv2 import mobilenetv2
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class MobileNetV2(nn.Module):
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def __init__(self, downsample_factor=8, pretrained=True):
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super(MobileNetV2, self).__init__()
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from functools import partial
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model = mobilenetv2(pretrained)
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self.features = model.features[:-1]
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self.total_idx = len(self.features)
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self.down_idx = [2, 4, 7, 14]
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if downsample_factor == 8:
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for i in range(self.down_idx[-2], self.down_idx[-1]):
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self.features[i].apply(partial(self._nostride_dilate,
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dilate=2))
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for i in range(self.down_idx[-1], self.total_idx):
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self.features[i].apply(partial(self._nostride_dilate,
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dilate=4))
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elif downsample_factor == 16:
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for i in range(self.down_idx[-1], self.total_idx):
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self.features[i].apply(partial(self._nostride_dilate,
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dilate=2))
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def _nostride_dilate(self, m, dilate):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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if m.stride == (2, 2):
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m.stride = (1, 1)
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if m.kernel_size == (3, 3):
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m.dilation = (dilate // 2, dilate // 2)
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m.padding = (dilate // 2, dilate // 2)
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else:
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if m.kernel_size == (3, 3):
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m.dilation = (dilate, dilate)
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m.padding = (dilate, dilate)
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def forward(self, x):
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low_level_features = self.features[:4](x)
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x = self.features[4:](low_level_features)
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return low_level_features, x
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#-----------------------------------------#
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# ASPP特征提取模块
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# 利用不同膨胀率的膨胀卷积进行特征提取
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#-----------------------------------------#
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"""
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卷积操作使用了膨胀率 dilation=6 * rate，这意味着卷积核内的采样点之间有 6 个像素的间隔。
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为了保持输出特征图的尺寸与输入数据相同，填充参数 padding 被设置为 padding=6 * rate。
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这样，卷积操作将在输入数据上以膨胀率为 6 的间隔进行卷积，并且在输出特征图的边缘周围填充 6 个像素的零，以确保输出特征图的尺寸不会缩小。
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bn_mom 在这段代码中是批量归一化层（Batch Normalization）的动量参数（momentum）。
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在 PyTorch 中的批量归一化层的实现中，动量参数控制了均值和方差的移动平均值的更新速度
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dilation 是卷积操作的膨胀率参数
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"""
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class ASPP(nn.Module):
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def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
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super(ASPP, self).__init__()
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self.branch1 = nn.Sequential(
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nn.Conv2d(dim_in,
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dim_out,
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1,
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1,
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padding=0,
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dilation=rate,
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bias=True),
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nn.BatchNorm2d(dim_out, momentum=bn_mom),
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nn.ReLU(inplace=True),
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)
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self.branch2 = nn.Sequential(
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nn.Conv2d(dim_in,
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dim_out,
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3,
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1,
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padding=6 * rate,
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dilation=6 * rate,
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bias=True),
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nn.BatchNorm2d(dim_out, momentum=bn_mom),
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nn.ReLU(inplace=True),
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)
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self.branch3 = nn.Sequential(
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nn.Conv2d(dim_in,
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dim_out,
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3,
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1,
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padding=12 * rate,
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dilation=12 * rate,
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bias=True),
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nn.BatchNorm2d(dim_out, momentum=bn_mom),
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nn.ReLU(inplace=True),
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)
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self.branch4 = nn.Sequential(
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nn.Conv2d(dim_in,
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dim_out,
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3,
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1,
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padding=18 * rate,
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dilation=18 * rate,
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bias=True),
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nn.BatchNorm2d(dim_out, momentum=bn_mom),
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nn.ReLU(inplace=True),
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)
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# branch5是一个池化过程
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self.branch5_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True)
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self.branch5_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom)
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self.branch5_relu = nn.ReLU(inplace=True)
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self.conv_cat = nn.Sequential(
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nn.Conv2d(dim_out * 5, dim_out, 1, 1, padding=0, bias=True),
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nn.BatchNorm2d(dim_out, momentum=bn_mom),
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nn.ReLU(inplace=True),
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)
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def forward(self, x):
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[b, c, row, col] = x.size()
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#-----------------------------------------#
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# 一共五个分支
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#-----------------------------------------#
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conv1x1 = self.branch1(x)
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conv3x3_1 = self.branch2(x)
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conv3x3_2 = self.branch3(x)
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conv3x3_3 = self.branch4(x)
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#-----------------------------------------#
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# 第五个分支，全局平均池化+卷积
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#-----------------------------------------#
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global_feature = torch.mean(x, 2, True)
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global_feature = torch.mean(global_feature, 3, True) #两次平均池化
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global_feature = self.branch5_conv(global_feature)
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global_feature = self.branch5_bn(global_feature)
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global_feature = self.branch5_relu(global_feature)# 通道的整合
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global_feature = F.interpolate(global_feature, (row, col), None,
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'bilinear', True)
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"""
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这一行代码首先使用 torch.mean 函数计算了 x 沿第 2 维度（通常是垂直方向）的平均值。
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True 参数表示要保持维度的数量，即在结果中保留该维度。这将生成一个张量
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其形状为 [batch_size, num_channels, 1, width]，
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其中 batch_size 是批量大小，num_channels 是通道数，width 是特征图的宽度，高度为 1。
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global_feature = torch.mean(global_feature, 3, True)：
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同样使用 torch.mean 函数，计算了上一步结果中的 global_feature 沿第 3 维度（通常是水平方向）的平均值，
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并再次保持维度的数量。这将生成一个形状为 [batch_size, num_channels, 1, 1] 的张量，
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其中每个通道的值都是整个特征图在该通道上的平均值。
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这个过程的结果是对整个特征图的每个通道执行全局平均池化，
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最终生成一个形状为 [batch_size, num_channels, 1, 1] 的全局平均特征向量。
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将全局平均特征 global_feature 调整为指定的目标尺寸 (row, col)
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"""
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#-----------------------------------------#
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# 将五个分支的内容堆叠起来
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# 然后1x1卷积整合特征。
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#-----------------------------------------#
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feature_cat = torch.cat(
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[conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, global_feature], dim=1)
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result = self.conv_cat(feature_cat) #1*1卷积 这个就是哪个绿色的
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return result
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# ASPP就是使用不同膨胀率的膨胀卷积对特征进行提取
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class DeepLab(nn.Module):
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def __init__(self,
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num_classes,
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backbone="mobilenet",
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pretrained=True,
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downsample_factor=16):
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super(DeepLab, self).__init__()
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if backbone == "xception":
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#----------------------------------#
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# 获得两个特征层
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# 浅层特征 [128,128,256]
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# 主干部分 [30,30,2048]
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#----------------------------------#
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self.backbone = xception(downsample_factor=downsample_factor,
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pretrained=pretrained)
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in_channels = 2048
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low_level_channels = 256
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elif backbone == "mobilenet":
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#----------------------------------#
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# 获得两个特征层
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# 浅层特征 [128,128,24]
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# 主干部分 [30,30,320]
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#----------------------------------#
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self.backbone = MobileNetV2(downsample_factor=downsample_factor,
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pretrained=pretrained)
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in_channels = 320
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low_level_channels = 24
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else:
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raise ValueError(
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'Unsupported backbone - `{}`, Use mobilenet, xception.'.format(
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backbone))
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#-----------------------------------------#
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# ASPP特征提取模块
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# 利用不同膨胀率的膨胀卷积进行特征提取
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#-----------------------------------------#
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self.aspp = ASPP(dim_in=in_channels,
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dim_out=256,
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rate=16 // downsample_factor)
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#----------------------------------#
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# 浅层特征边
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#----------------------------------#
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self.shortcut_conv = nn.Sequential(
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nn.Conv2d(low_level_channels, 48, 1),
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nn.BatchNorm2d(48),
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nn.ReLU(inplace=True))
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self.cat_conv = nn.Sequential(
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nn.Conv2d(48 + 256, 256, 3, stride=1, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Dropout(0.5),
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nn.Conv2d(256, 256, 3, stride=1, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Dropout(0.1),
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)
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self.cls_conv = nn.Conv2d(256, num_classes, 1, stride=1)
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"""
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在每个训练批次中，以一定的概率（通常在 0.2 到 0.5 之间）随机选择一些神经元，并将它们的输出置零。
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这表示在每次前向传播中，只有部分神经元的输出会被传递到下一层，而其他神经元的输出被设置为零。
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在每次反向传播中，只有那些没有被置零的神经元才会更新其权重。这意味着每个神经元都有机会被训练，
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而不是过度依赖于特定的神经元。
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"""
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def forward(self, x):
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H, W = x.size(2), x.size(3)
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#-----------------------------------------#
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# 获得两个特征层
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# low_level_features: 浅层特征-进行卷积处理
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# x : 主干部分-利用ASPP结构进行加强特征提取
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#-----------------------------------------#
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low_level_features, x = self.backbone(x)
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x = self.aspp(x)
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low_level_features = self.shortcut_conv(low_level_features)
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#-----------------------------------------#
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# 将加强特征边上采样 绿色的模块
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# 与浅层特征堆叠后利用卷积进行特征提取
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#-----------------------------------------#
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x = F.interpolate(x,
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size=(low_level_features.size(2),
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low_level_features.size(3)),
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mode='bilinear',#使用双线性插值进行的上采样操作
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align_corners=True) #上采样
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x = self.cat_conv(torch.cat((x, low_level_features), dim=1))
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x = self.cls_conv(x)
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x = F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True)
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return x
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