用tensorflow的layers.Layer模块改写 class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio = 4): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out)

用tensorflow的layers.Layer模块改写class ChannelAttention(nn.Module): def init(self, in_planes, ratio = 4): super(ChannelAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out)

以下是使用tensorflow的layers.Layer模块改写的ChannelAttention类： import tensorflow as tf from tensorflow.keras import layers class ChannelAttention(layers.Layer): def __init__(self, in_planes, ...

class ChannelAttention(nn.Module): def init(self, in_planes, ratio = 4): super(ChannelAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out) 改为tensorflow形式

def __init__(self, in_planes, ratio=4): super(ChannelAttention, self).__init__() self.avg_pool = tf.keras.layers.GlobalAvgPool2D() self.max_pool = tf.keras.layers.GlobalMaxPool2D() self.fc1 = tf....

用tensorflow的layers.Layer模块改写 class MultiHeadSelfAttention(nn.Module): def init(self,in_c,out_c,head_n,fm_sz,pos_bias = False): super(MultiHeadSelfAttention,self).init() self.sa_blocks = [SelfAttention(in_c = in_c,out_c = out_c,fm_sz = fm_sz,pos_bias = pos_bias) for i in range(head_n)] self.sa_blocks = nn.ModuleList(self.sa_blocks) def forward(self,x): results = [sa(x) for sa in self.sa_blocks] return torch.cat(results,dim = 1)

以下是使用tensorflow的layers.Layer模块改写的代码： import tensorflow as tf class MultiHeadSelfAttention(tf.keras.layers.Layer): def __init__(self, in_c, out_c, head_n, fm_sz, pos_bias=False): ...

用tensorflow的layers.Layer模块改写 class SpatialAttention(nn.Module): def init(self): super(SpatialAttention, self).init() self.conv1 = nn.Conv2d(2, 1, 3, padding = 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim = 1, keepdim = True) max_out, _ = torch.max(x, dim = 1, keepdim = True) x = torch.cat([avg_out, max_out], dim = 1) x = self.conv1(x) return self.sigmoid(x)

以下是使用TensorFlow的layers.Layer模块改写的代码： python import tensorflow as tf class SpatialAttention(tf.keras.layers.Layer): def __init__(self): super(SpatialAttention, self).__init__() ...

class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(20, 50, 5) self.fc1 = nn.Linear(4*4*50, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self....

解释class LSTM(nn.Module): def init(self, input_size, hidden_size, num_layers, batch_size, device="cpu"): super().init() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True, bidirectional=False)

这段代码定义了一个名为 LSTM 的类，该类继承了 nn.Module 类，并重写了 __init__() 方法。在 __init__() 方法中，类的实例变量被初始化，包括： - device：该变量指定了设备类型，默认为 "cpu"。 - ...

class DenseBlock(nn.Module): def init(self, in_channels, growth_rate, num_layers): super(DenseBlock, self).init() self.layers = nn.ModuleList([Bottleneck(in_channels + i * growth_rate, growth_rate) for i in range(num_layers)]) def forward(self, x): for layer in self.layers: x = layer(x) return x

在这段代码中，self.layers定义了一个由num_layers个Bottleneck块组成的列表，每个Bottleneck块的输入通道数为in_channels + i * growth_rate，其中i表示Bottleneck块在DenseBlock中的位置，growth_rate表示每个...

请详解class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init

- class GRU(nn.Module):：定义了一个名为GRU的PyTorch类，继承自nn.Module类，表示这个类是一个神经网络模型。 - def __init__(self, feature_size, hidden_size, num_layers, output_size):：定义了这个类的...

详细解释class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init

这是一个定义了一个名为GRU的类，它继承自nn.Module。GRU是一种循环神经网络，它可以用于处理序列数据，其中每个时间步都会有一个输入和一个输出。在这个类的构造函数__init__中，我们需要指定一些参数，包括输入...

class ChannelAttention(layers.Layer): def init(self, in_planes, ratio=32): super(ChannelAttention, self).init() self.avg= layers.GlobalAveragePooling2D() self.max= layers.GlobalMaxPooling2D() self.conv1 = layers.Conv2D(in_planes//ratio, kernel_size=1, strides=1, padding='same', kernel_regularizer=regularizers.l2(5e-4), use_bias=True, activation=tf.nn.relu) self.conv2 = layers.Conv2D(in_planes, kernel_size=1, strides=1, padding='same', kernel_regularizer=regularizers.l2(5e-4), use_bias=True) def call(self, inputs): avg = self.avg(inputs) max = self.max(inputs) avg = layers.Reshape((1, 1, avg.shape[1]))(avg) # shape (None, 1, 1 feature) max = layers.Reshape((1, 1, max.shape[1]))(max) # shape (None, 1, 1 feature) avg_out = self.conv2(self.conv1(avg)) max_out = self.conv2(self.conv1(max)) out = avg_out + max_out out = tf.nn.sigmoid(out) return out

首先在 __init__ 函数中定义了一个全局平均池化和一个全局最大池化，然后通过两个卷积层和一个 sigmoid 函数将平均池化和最大池化的结果融合起来，最后输出一个通道注意力权重矩阵。在 call 函数中，先将输入...

详细解释这段代码import torch from torch import nn from einops.layers.torch import Rearrange class Transformer(nn.Module): def init(self, input_dim, num_class, hidden_dim) -> None: super().init() self.d_model = hidden_dim self.hidden_dim = 21 * self.d_model self.transformer = nn.Sequential( nn.Linear(input_dim, self.hidden_dim), Rearrange("b (n c) -> b n c", c=self.d_model), nn.TransformerEncoder( nn.TransformerEncoderLayer( d_model=self.d_model, nhead=4, dim_feedforward=self.d_model * 2, dropout=0.1, batch_first=True ), 4, torch.nn.LayerNorm(self.d_model), ), Rearrange("b n c -> b (n c)"), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, num_class), ) def forward(self,x): return self.transformer(x)

具体来说，模型的输入是一个大小为input_dim的向量，输出是一个大小为num_class的向量，表示预测的类别概率。模型的主要组成部分是一个TransformerEncoder，它是由多个TransformerEncoderLayer组成的序列。每个...

class TransAm(nn.Module): def init(self,feature_size=250,num_layers=1,dropout=0.1): super(TransAm, self).init() self.model_type = 'Transformer' self.input_embedding = nn.Linear(1,feature_size) self.src_mask = None self.pos_encoder = PositionalEncoding(feature_size) self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=10, dropout=dropout) self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers) self.decoder = nn.Linear(feature_size,1) self.init_weights() 这段代码的作用举例说明

- encoder_layer: 编码器层，用于在位置编码后对输入序列进行编码，其中d_model表示输入和输出的特征向量维度，nhead表示多头注意力的头数，dropout表示随机失活率； - transformer_encoder: 编码器，由多个encoder_...

class ResNetRGBD(nn.Module): def init(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNetRGBD, self).init() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1

这是一个 PyTorch 中的 ResNetRGBD 模型类的定义，它继承自 nn.Module 类。在初始化函数 __init__ 中，它接受以下参数： - block：ResNet 中残差块的类（比如 BasicBlock 或 Bottleneck）。 - layers：一个...

class HGMN(nn.Module): def init(self, args, n_user, n_item, n_category): super(HGMN, self).init() self.n_user = n_user self.n_item = n_item self.n_category = n_category self.n_hid = args.n_hid self.n_layers = args.n_layers self.mem_size = args.mem_size self.emb = nn.Parameter(torch.empty(n_user + n_item + n_category, self.n_hid)) self.norm = nn.LayerNorm((args.n_layers + 1) * self.n_hid) self.layers = nn.ModuleList() for i in range(0, self.n_layers): self.layers.append(GNNLayer(self.n_hid, self.n_hid, self.mem_size, 5, layer_norm=True, dropout=args.dropout, activation=nn.LeakyReLU(0.2, inplace=True))) self.pool = GraphPooling('mean') self.reset_parameters()

然后，使用 nn.ModuleList 创建一个包含 self.n_layers 个 GNNLayer 对象的列表 self.layers。GNNLayer 是一个图神经网络层，接受隐藏层大小、记忆大小等参数，并进行相应的操作。最后，创建一个 GraphPooling ...

import tensorflow as tf class BaseAttention(tf.keras.layers.Layer): def init(self, kwargs): super().init() self.mha = tf.keras.layers.MultiHeadAttention(kwargs) self.layernorm = tf.keras.layers.LayerNormalization() self.add = tf.keras.layers.Add() class CrossAttention(BaseAttention): def call(self, x, context): attn_output, attn_scores = self.mha( query=x, key=context, value=context, return_attention_scores=True) # Cache the attention scores for plotting later. self.last_attn_scores = attn_scores x = self.add([x, attn_output]) x = self.layernorm(x) return x, attn_scores class GlobalSelfAttention(BaseAttention): def call(self, x): attn_output, attn_scores = self.mha( query=x, value=x, key=x, return_attention_scores=True) # Cache the attention scores for plotting later. self.last_attn_scores = attn_scores x = self.add([x, attn_output]) x = self.layernorm(x) return x, attn_scores

这段代码使用了 TensorFlow 框架的 tf.keras.layers 模块来定义注意力层的结构。你可以根据自己的需求进一步使用这些类来构建注意力机制的模型。请注意，这只是代码片段的一部分，可能还需要根据具体的模型和任务...

class BufferedSequential(nn.Module): def init(self, layers, buffer_or_not): super(BufferedSequential, self).init() self.layers = layers self.buffer_or_not = buffer_or_not self.n_buffers = np.sum(buffer_or_not) def parameters(self): return [param for layer in self.layers for param in layer.parameters()] def forward(self, x): if not isinstance(x, Variable): if not isinstance(x, torch.Tensor): x = torch.Tensor(x) x = Variable(x) self.buffer = [] for layer, is_buffered in zip(self.layers, self.buffer_or_not): x = layer(x) if is_buffered: self.buffer.append(x) return x

在 __init__ 方法中，它首先调用父类的 __init__ 方法进行初始化，然后将传入的 layers 和 buffer_or_not 参数保存在对象的属性中。n_buffers 属性是通过统计 buffer_or_not 列表中为 True 的元素数量来...

YOLO算法-城市电杆数据集-496张图像带标签-电杆.zip

YOLO系列算法目标检测数据集，包含标签，可以直接训练模型和验证测试，数据集已经划分好，包含数据集配置文件data.yaml，适用yolov5,yolov8,yolov9,yolov7,yolov10,yolo11算法；包含两种标签格:yolo格式（txt文件）和voc格式（xml文件），分别保存在两个文件夹中，文件名末尾是部分类别名称; yolo格式：<class> <x_center> <y_center> <width> <height>，其中： <class> 是目标的类别索引（从0开始）。 <x_center> 和 <y_center> 是目标框中心点的x和y坐标，这些坐标是相对于图像宽度和高度的比例值，范围在0到1之间。 <width> 和 <height> 是目标框的宽度和高度，也是相对于图像宽度和高度的比例值；【注】可以下拉页面，在资源详情处查看标签具体内容；

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请详解class GRU(nn.Module): def__init__(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).__init__

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class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

请详解class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init

详细解释class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init