def conv_dw(filter_in, filter_out, stride = 1):是什么意思
时间: 2024-05-22 16:11:35 浏览: 16
这是一个深度可分离卷积(depthwise separable convolution)的函数定义,常用于卷积神经网络的设计中。
深度可分离卷积是一种卷积操作,它将标准卷积分成两个步骤:深度卷积和逐点卷积。深度卷积只卷积输入的每个通道内的每个滤波器,而逐点卷积是在深度卷积之后应用的,用于将每个通道的输出与其他通道的输出混合。
这个函数定义中,`filter_in`表示输入图像的通道数,`filter_out`表示输出图像的通道数,`stride`表示卷积步长。
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__all__ = ["ResNet45"] def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2D( in_planes, out_planes, kernel_size=1, stride=1, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=Fals
e() def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): return nn.Conv2D( in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, dilation=dilation, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False) class BasicBlock(nn.Layer): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2D(planes) self.relu = nn.ReLU() self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2D(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class ResNet45(nn.Layer): def __init__(self, block=BasicBlock, layers=[2, 2, 2, 2], num_classes=1000): super(ResNet45, self).__init__() self.inplanes = 64 self.conv1 = nn.Conv2D( 3, 64, kernel_size=7, stride=2, padding=3, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False) self.bn1 = nn.BatchNorm2D(64) self.relu = nn.ReLU() self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = nn.AdaptiveAvgPool2D((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2D( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False), nn.BatchNorm2D(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = paddle.flatten(x, 1) x = self.fc(x) return x
这是一个 ResNet45 的实现,是一个卷积神经网络用于图像分类。其中包含了 BasicBlock 和 ResNet45 两个类,ResNet45 是整个网络的主体部分,由多个 BasicBlock 组成。BasicBlock 由两个卷积层和一个残差连接组成。网络的输入是一张 3 通道的图片,输出是对应的分类结果。
def adapt_input_conv(in_chans, conv_weight):
def adapt_input_conv是一个用于适应输入卷积的函数。该函数有两个参数:in_chans和conv_weight。
in_chans是指输入数据的通道数,也就是输入数据的维度。conv_weight则是卷积核的权重参数,用于在输入数据上进行卷积操作。
通过adapt_input_conv函数,可以根据输入数据的通道数和卷积核的权重参数,自动调整输入数据的维度,以便于进行卷积操作。这一过程中,可能会对卷积核的权重参数进行调整,以确保其与输入数据的通道数相匹配。
这个函数通常在深度学习模型的卷积层中使用,用于处理输入数据并生成卷积后的结果。它是一个重要的函数,可以有效地提高模型的性能和准确度,同时也方便了数据的处理和管理。