branch1x1 = conv2d_bn(x, 32, 1, 1)这个代码改为用ghostmodule代替1*1卷积

时间: 2023-10-07 13:12:27 浏览: 32
Sure! Here's the modified code using Ghost Module instead of the 1x1 convolution: ```python import tensorflow as tf def ghost_module(x, num_channels, kernel_size): # Split input feature map into two sub-feature maps ghost_channels = num_channels // 2 # First sub-feature map using 1x1 convolution branch1x1 = tf.keras.layers.Conv2D(ghost_channels, 1)(x) # Second sub-feature map using smaller kernel size for convolution branch3x3 = tf.keras.layers.Conv2D(ghost_channels, kernel_size, padding='same')(x) # Concatenate the two sub-feature maps out = tf.keras.layers.Concatenate()([branch1x1, branch3x3]) return out # Replace 1x1 convolution with Ghost Module branch1x1 = ghost_module(x, 32, 1) ``` In the modified code, the `ghost_module` function takes the input feature map `x`, the number of output channels `num_channels`, and the kernel size `kernel_size` as parameters. It splits the input feature map into two sub-feature maps and applies a 1x1 convolution to the first sub-feature map and a smaller kernel convolution to the second sub-feature map. Finally, it concatenates the two sub-feature maps as the output. Please note that this code assumes you have defined the `ghost_module` function correctly and have imported the necessary dependencies. Additionally, make sure to adjust the parameters according to your specific needs.

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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), ) def forward(self, x) [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result用深度可分离卷积代替这段代码的3×3卷积

可以将代码中的3x3卷积替换为深度可分离卷积,即将nn.Conv2d替换为nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=in_channels, bias=True),其中groups=in_channels,表示每个输入通道都对应一个卷积核,实现深度可分离卷积。具体替换代码如下: 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=rate, groups=dim_in, 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=rate, groups=dim_in, 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=rate, groups=dim_in, 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=rate, groups=dim_in, 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=rate, groups=dim_in, 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=rate, groups=dim_in, 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), ) def forward(self, x): [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result

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), ) def forward(self, x) [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result用1×3卷积和3×1卷积代替这段代码的3×3卷积

可以将原来的3x3卷积分解成1x3卷积和3x1卷积的组合,代替代码中的3x3卷积。修改后的代码如下: 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, 1, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 1)*rate, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(1, 0)*rate, dilation=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, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 2)*rate, dilation=2*rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(2, 0)*rate, dilation=2*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, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 3)*rate, dilation=3*rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(3, 0)*rate, dilation=3*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, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 4)*rate, dilation=4*rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(4, 0)*rate, dilation=4*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, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 5)*rate, dilation=5*rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(5, 0)*rate, dilation=5*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, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (1, 3), 1, padding=(0, 6)*rate, dilation=6*rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), nn.Conv2d(dim_out, dim_out, (3, 1), 1, padding=(6, 0)*rate, dilation=6*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), ) def forward(self, x): [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result

解释每一句class RepVggBlock(nn.Layer): def init(self, ch_in, ch_out, act='relu', alpha=False): super(RepVggBlock, self).init() self.ch_in = ch_in self.ch_out = ch_out self.conv1 = ConvBNLayer( ch_in, ch_out, 3, stride=1, padding=1, act=None) self.conv2 = ConvBNLayer( ch_in, ch_out, 1, stride=1, padding=0, act=None) self.act = get_act_fn(act) if act is None or isinstance(act, ( str, dict)) else act if alpha: self.alpha = self.create_parameter( shape=[1], attr=ParamAttr(initializer=Constant(value=1.)), dtype="float32") else: self.alpha = None def forward(self, x): if hasattr(self, 'conv'): y = self.conv(x) else: if self.alpha: y = self.conv1(x) + self.alpha * self.conv2(x) else: y = self.conv1(x) + self.conv2(x) y = self.act(y) return y def convert_to_deploy(self): if not hasattr(self, 'conv'): self.conv = nn.Conv2D( in_channels=self.ch_in, out_channels=self.ch_out, kernel_size=3, stride=1, padding=1, groups=1) kernel, bias = self.get_equivalent_kernel_bias() self.conv.weight.set_value(kernel) self.conv.bias.set_value(bias) self.delattr('conv1') self.delattr('conv2') def get_equivalent_kernel_bias(self): kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1) kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2) if self.alpha: return kernel3x3 + self.alpha * self._pad_1x1_to_3x3_tensor( kernel1x1), bias3x3 + self.alpha * bias1x1 else: return kernel3x3 + self._pad_1x1_to_3x3_tensor( kernel1x1), bias3x3 + bias1x1 def _pad_1x1_to_3x3_tensor(self, kernel1x1): if kernel1x1 is None: return 0 else: return nn.functional.pad(kernel1x1, [1, 1, 1, 1]) def _fuse_bn_tensor(self, branch): if branch is None: return 0, 0 kernel = branch.conv.weight running_mean = branch.bn._mean running_var = branch.bn._variance gamma = branch.bn.weight beta = branch.bn.bias eps = branch.bn._epsilon std = (running_var + eps).sqrt() t = (gamma / std).reshape((-1, 1, 1, 1)) return kernel * t, beta - running_mean * gamma / std

这段代码定义了一个名为 RepVggBlock 的类,它继承自 nn.Layer 类。这个类用于实现 RepVGG 网络中的基本块。RepVGG 是一种轻量级的卷积神经网络,它的主要思想是将常规的卷积操作替换为由卷积和 Batch Normalization(BN)操作组成的基本块,从而在不损失精度的情况下减少网络参数的数量。 在这个类中,__init__ 方法定义了类的初始化函数,它接受三个参数:ch_in、ch_out 和 act。其中,ch_in 和 ch_out 分别表示输入通道数和输出通道数,act 表示激活函数的类型。在初始化函数中,首先调用父类的初始化函数 super(RepVggBlock, self).__init__() 来初始化父类的属性。然后,将输入和输出通道数保存到 self.ch_in 和 self.ch_out 中。接着,使用 ConvBNLayer 类创建两个卷积层:self.conv1 和 self.conv2。这两个卷积层分别使用 3x3 和 1x1 的卷积核进行卷积操作,并且没有使用激活函数。最后,根据输入的激活函数类型,使用 get_act_fn 函数获取激活函数,并保存到 self.act 中。如果激活函数为 None 或者是字符串或字典类型,则 self.act 直接保存激活函数类型,否则就保存激活函数的实例。 接着,forward 方法定义了类的前向传播函数。它接受一个输入张量 x,根据是否已经初始化了 self.conv 属性来判断使用哪个卷积操作。如果已经初始化了 self.conv 属性,则使用 self.conv 对输入进行卷积操作;否则,分别对输入使用 self.conv1 和 self.conv2 进行卷积操作,并将它们相加。如果类的 alpha 属性存在,则使用 alpha 值对 self.conv2 的输出进行缩放,然后再将两个卷积层的输出相加。最后,对输出进行激活函数处理,并返回输出。 convert_to_deploy 方法用于将训练好的模型转换为部署模型。它首先检查类中是否已经初始化了 self.conv 属性,如果没有,则创建一个新的 Conv2D 层,并将其权重和偏置设置为等效的卷积和 BN 层的权重和偏置。然后,删除 self.conv1 和 self.conv2 属性。 get_equivalent_kernel_bias 方法用于计算等效的卷积和 BN 层的权重和偏置。它首先将 self.conv1 和 self.conv2 层的权重和偏置分别融合到 kernel3x3 和 bias3x3 变量中,并使用 _pad_1x1_to_3x3_tensor 函数将 kernel1x1 变量的尺寸从 1x1 扩展到 3x3。如果类的 alpha 属性存在,则将 kernel1x1 加权缩放后再加到 kernel3x3 中。最后,将偏置项也进行融合,并返回等效的权重和偏置。 _pad_1x1_to_3x3_tensor 方法用于将 1x1 的卷积核扩展到 3x3。 _fuse_bn_tensor 方法用于将卷积和 BN 层进行融合并返回等效的权重和偏置。它首先获取卷积层的权重、BN 层的运行均值、方差、缩放因子和偏置项。然后,根据 BN 层的参数计算标准差,并将缩放因子 reshape 成与权重相同的形状。最后,根据融合公式计算等效的权重和偏置,并返回。
Inception-v4 是一种用于图像分类的卷积神经网络模型,它是 Google 在 2016 年提出的一种改进版本,相较于 Inception-v3,它在网络架构上做了一些改进,可以获得更好的性能。下面是使用 TensorFlow 搭建 Inception-v4 模型的代码: python import tensorflow as tf def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False): x = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias)(x) x = tf.keras.layers.BatchNormalization()(x) if activation is not None: x = tf.keras.layers.Activation(activation)(x) return x def block_inception_a(x): branch_1x1 = conv2d_bn(x, 96, 1) branch_5x5 = conv2d_bn(x, 64, 1) branch_5x5 = conv2d_bn(branch_5x5, 96, 5) branch_3x3dbl = conv2d_bn(x, 64, 1) branch_3x3dbl = conv2d_bn(branch_3x3dbl, 96, 3) branch_3x3dbl = conv2d_bn(branch_3x3dbl, 96, 3) branch_pool = tf.keras.layers.AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 96, 1) x = tf.keras.layers.concatenate([branch_1x1, branch_5x5, branch_3x3dbl, branch_pool], axis=3) return x def block_reduction_a(x): branch_3x3 = conv2d_bn(x, 384, 3, strides=2, padding='valid') branch_3x3dbl = conv2d_bn(x, 192, 1) branch_3x3dbl = conv2d_bn(branch_3x3dbl, 224, 3) branch_3x3dbl = conv2d_bn(branch_3x3dbl, 256, 3, strides=2, padding='valid') branch_pool = tf.keras.layers.MaxPooling2D(3, strides=2, padding='valid')(x) x = tf.keras.layers.concatenate([branch_3x3, branch_3x3dbl, branch_pool], axis=3) return x def block_inception_b(x): branch_1x1 = conv2d_bn(x, 384, 1) branch_7x7 = conv2d_bn(x, 192, 1) branch_7x7 = conv2d_bn(branch_7x7, 224, [1, 7]) branch_7x7 = conv2d_bn(branch_7x7, 256, [7, 1]) branch_7x7dbl = conv2d_bn(x, 192, 1) branch_7x7dbl = conv2d_bn(branch_7x7dbl, 192, [7, 1]) branch_7x7dbl = conv2d_bn(branch_7x7dbl, 224, [1, 7]) branch_7x7dbl = conv2d_bn(branch_7x7dbl, 224, [7, 1]) branch_7x7dbl = conv2d_bn(branch_7x7dbl, 256, [1, 7]) branch_pool = tf.keras.layers.AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 128, 1) x = tf.keras.layers.concatenate([branch_1x1, branch_7x7, branch_7x7dbl, branch_pool], axis=3) return x def block_reduction_b(x): branch_3x3 = conv2d_bn(x, 192, 1) branch_3x3 = conv2d_bn(branch_3x3, 192, 3, strides=2, padding='valid') branch_7x7x3 = conv2d_bn(x, 256, 1) branch_7x7x3 = conv2d_bn(branch_7x7x3, 256, [1, 7]) branch_7x7x3 = conv2d_bn(branch_7x7x3, 320, [7, 1]) branch_7x7x3 = conv2d_bn(branch_7x7x3, 320, 3, strides=2, padding='valid') branch_pool = tf.keras.layers.MaxPooling2D(3, strides=2, padding='valid')(x) x = tf.keras.layers.concatenate([branch_3x3, branch_7x7x3, branch_pool], axis=3) return x def inception_v4(input_shape=(299, 299, 3), num_classes=1001): inputs = tf.keras.layers.Input(shape=input_shape) x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid') x = conv2d_bn(x, 32, 3, padding='valid') x = conv2d_bn(x, 64, 3) x = tf.keras.layers.MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1) x = conv2d_bn(x, 192, 3, padding='valid') x = tf.keras.layers.MaxPooling2D(3, strides=2)(x) x = block_inception_a(x) x = block_inception_a(x) x = block_inception_a(x) x = block_reduction_a(x) x = block_inception_b(x) x = block_inception_b(x) x = block_inception_b(x) x = block_inception_b(x) x = block_reduction_b(x) x = block_inception_a(x) x = block_inception_a(x) x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dropout(0.2)(x) outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x) model = tf.keras.models.Model(inputs, outputs) return model 这个代码定义了 conv2d_bn 函数来创建一个卷积层和一个批归一化层,然后使用 block_inception_a、block_reduction_a、block_inception_b 和 block_reduction_b 函数来搭建 Inception-v4 的模型结构。最后,使用 inception_v4 函数将这些层组合起来,并返回一个 Keras 模型对象。
以下是将Unet和门结构结合的PyTorch代码: import torch import torch.nn as nn import torch.nn.functional as F class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = F.relu(x) x = self.conv2(x) x = self.bn2(x) x = F.relu(x) return x class UNet(nn.Module): def __init__(self, in_channels=3, out_channels=1): super(UNet, self).__init__() self.down1 = ConvBlock(in_channels, 64) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.down2 = ConvBlock(64, 128) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.down3 = ConvBlock(128, 256) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.down4 = ConvBlock(256, 512) self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) self.bottom = ConvBlock(512, 1024) self.up4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2) self.upconv4 = ConvBlock(1024, 512) self.up3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2) self.upconv3 = ConvBlock(512, 256) self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) self.upconv2 = ConvBlock(256, 128) self.up1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) self.upconv1 = ConvBlock(128, 64) self.outconv = nn.Conv2d(64, out_channels, kernel_size=1) # 定义门结构的两个分支 self.branch1 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=3, padding=1), nn.BatchNorm2d(1024), nn.ReLU(inplace=True), nn.Conv2d(1024, 1, kernel_size=1) ) self.branch2 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=3, padding=1), nn.BatchNorm2d(1024), nn.ReLU(inplace=True), nn.Conv2d(1024, 1, kernel_size=1) ) def forward(self, x): # 下采样 x1 = self.down1(x) x2 = self.pool1(x1) x3 = self.down2(x2) x4 = self.pool2(x3) x5 = self.down3(x4) x6 = self.pool3(x5) x7 = self.down4(x6) x8 = self.pool4(x7) # 中间部分 x9 = self.bottom(x8) # 上采样 x10 = self.up4(x9) x10 = torch.cat([x10, x7], dim=1) x11 = self.upconv4(x10) x12 = self.up3(x11) x12 = torch.cat([x12, x5], dim=1) x13 = self.upconv3(x12) x14 = self.up2(x13) x14 = torch.cat([x14, x3], dim=1) x15 = self.upconv2(x14) x16 = self.up1(x15) x16 = torch.cat([x16, x1], dim=1) x17 = self.upconv1(x16) # 输出结果 x18 = self.outconv(x17) # 计算门控信号 branch1 = self.branch1(x9) branch2 = self.branch2(x9) gate = F.sigmoid(branch2) x18 = gate*x18 + (1-gate)*branch1 return x18 上述代码定义了一个Unet模型和一个门结构模块,并将它们结合起来。具体来说,Unet模型由一系列卷积块和反卷积块组成,使用了下采样和上采样操作实现了图像分割。门结构模块由两个分支组成,它们分别计算了输入特征图的重要程度,用sigmoid函数将它们结合起来得到一个门控信号,然后将这个信号应用于Unet模型输出的结果和门结构模块的输出结果来获得最终的输出。
ShuffleNet V2是一种轻量级的神经网络模型,主要用于图像分类任务。该模型采用了分组卷积和通道重排等技术,能够在保持较高准确率的同时,大幅减小模型参数量和计算量。 下面是基于PyTorch实现基于ShuffleNet V2的图像识别的步骤: 1. 导入必要的库和模块: python import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.utils.data import DataLoader 2. 定义模型结构: python class ShuffleNetV2(nn.Module): def __init__(self, input_size=224, num_classes=1000): super(ShuffleNetV2, self).__init__() # 定义模型各层的参数 self.input_size = input_size self.num_classes = num_classes self.stage_repeats = [4, 8, 4] self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] self.conv1_channel = 24 self.conv3_channel = 116 # 定义模型各层 self.conv1 = nn.Conv2d(3, self.conv1_channel, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(self.conv1_channel) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.stage2 = self._make_stage(2) self.stage3 = self._make_stage(3) self.stage4 = self._make_stage(4) self.conv5 = nn.Conv2d(self.stage_out_channels[-2], self.stage_out_channels[-1], kernel_size=1, stride=1, padding=0, bias=False) self.bn5 = nn.BatchNorm2d(self.stage_out_channels[-1]) self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1) self.fc = nn.Linear(self.stage_out_channels[-1], self.num_classes) def _make_stage(self, stage): modules = [] # 该阶段的输入和输出通道数 stage_channels = self.stage_out_channels[stage] # 需要分组的数量 num_groups = 2 if stage == 2 else 4 # 第一个块的通道数需要在conv3后增加 first_block_channels = self.conv3_channel if stage == 2 else stage_channels // 2 # 第一个块,包含1个3x3分组卷积和1个1x1分组卷积 modules.append(ShuffleBlock(self.stage_out_channels[stage-1], first_block_channels, groups=num_groups, stride=2)) # 后续块,包含1个1x1分组卷积、1个3x3分组卷积和1个1x1分组卷积 for i in range(self.stage_repeats[stage-2]): modules.append(ShuffleBlock(first_block_channels, stage_channels, groups=num_groups, stride=1)) return nn.Sequential(*modules) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = nn.functional.relu(x, inplace=True) x = self.maxpool(x) x = self.stage2(x) x = self.stage3(x) x = self.stage4(x) x = self.conv5(x) x = self.bn5(x) x = nn.functional.relu(x, inplace=True) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x 3. 定义ShuffleBlock模块: python class ShuffleBlock(nn.Module): def __init__(self, in_channels, out_channels, groups, stride): super(ShuffleBlock, self).__init__() mid_channels = out_channels // 2 if stride == 1: self.branch1 = nn.Sequential() else: self.branch1 = nn.Sequential( nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False), nn.BatchNorm2d(in_channels), nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True) ) self.branch2 = nn.Sequential( nn.Conv2d(in_channels if stride > 1 else mid_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True), nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False), nn.BatchNorm2d(mid_channels), nn.Conv2d(mid_channels, out_channels - mid_channels, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(out_channels - mid_channels), nn.ReLU(inplace=True) ) def forward(self, x): x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat([x1, x2], 1) out = channel_shuffle(out, 2) return out 4. 定义ShuffleNet V2中的通道重排操作: python def channel_shuffle(x, groups): batch_size, channels, height, width = x.data.size() channels_per_group = channels // groups # 将x按通道数分组 x = x.view(batch_size, groups, channels_per_group, height, width) # 将x的通道数重排 x = torch.transpose(x, 1, 2).contiguous() # 将x合并回原来的形状 x = x.view(batch_size, -1, height, width) return x 5. 加载数据集和预处理: python # 定义图像预处理 transform = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # 加载训练集和测试集 train_dataset = datasets.ImageFolder("path/to/train/folder", transform=transform) test_dataset = datasets.ImageFolder("path/to/test/folder", transform=transform) # 定义数据加载器 train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4) test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, num_workers=4) 6. 定义损失函数和优化器: python net = ShuffleNetV2() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0001) 7. 训练模型: python for epoch in range(30): net.train() for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = net(data) loss = criterion(output, target) loss.backward() optimizer.step() # 在测试集上测试模型 net.eval() correct = 0 total = 0 with torch.no_grad(): for data, target in test_loader: output = net(data) _, predicted = torch.max(output.data, 1) total += target.size(0) correct += (predicted == target).sum().item() print('Epoch: %d, Test Accuracy: %.2f%%' % (epoch+1, 100.*correct/total)) 以上就是基于PyTorch实现基于ShuffleNet V2的图像识别的完整步骤。
可以使用ResNet的最后一个卷积层的输出作为输入,然后添加Inception多尺度模块,将不同尺度的卷积核并行应用到输入特征上,最后将不同尺度的输出特征拼接在一起。具体步骤如下: 1. 使用ResNet骨干网络提取图像特征,得到最后一个卷积层的输出。 2. 定义Inception多尺度模块,包括不同尺度的卷积核和池化核,并行应用到输入特征上,得到不同尺度的输出特征。 3. 将不同尺度的输出特征拼接在一起,得到最终的特征表示。 4. 将最终的特征表示输入到全连接层进行分类或者回归等任务。 以下是代码示例,假设ResNet的最后一个卷积层输出为x,Inception多尺度模块包括3个分支: python import torch.nn as nn class InceptionModule(nn.Module): def __init__(self, in_channels, out_channels): super(InceptionModule, self).__init__() # 1x1 convolution branch self.branch1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1) # 3x3 convolution branch self.branch3x3 = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1), nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) ) # 5x5 convolution branch self.branch5x5 = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1), nn.Conv2d(out_channels, out_channels, kernel_size=5, stride=1, padding=2) ) # max pooling branch self.branch_pool = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1), nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1) ) def forward(self, x): out1x1 = self.branch1x1(x) out3x3 = self.branch3x3(x) out5x5 = self.branch5x5(x) out_pool = self.branch_pool(x) out = torch.cat([out1x1, out3x3, out5x5, out_pool], dim=1) return out class ResNetInception(nn.Module): def __init__(self, num_classes): super(ResNetInception, self).__init__() self.resnet = models.resnet50(pretrained=True) self.inception1 = InceptionModule(2048, 512) self.inception2 = InceptionModule(2048 + 4 * 512, 1024) self.inception3 = InceptionModule(2048 + 4 * 1024, 2048) self.fc = nn.Linear(2048, num_classes) def forward(self, x): # ResNet backbone x = self.resnet.conv1(x) x = self.resnet.bn1(x) x = self.resnet.relu(x) x = self.resnet.maxpool(x) x = self.resnet.layer1(x) x = self.resnet.layer2(x) x = self.resnet.layer3(x) x = self.resnet.layer4(x) # Inception module 1 out1 = self.inception1(x) # Inception module 2 out2 = torch.cat([x, out1], dim=1) out2 = self.inception2(out2) # Inception module 3 out3 = torch.cat([x, out1, out2], dim=1) out3 = self.inception3(out3) # Global average pooling and fully connected layer out = F.adaptive_avg_pool2d(out3, output_size=1) out = out.view(out.size(0), -1) out = self.fc(out) return out 在上面的代码中,ResNet50骨干网络的输出为x,然后分别经过3个Inception多尺度模块,每个模块的输出作为下一个模块的输入。最终的特征表示为第3个模块的输出,经过全局平均池化后输入到全连接层进行分类。

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