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wdcnn.py
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wdcnn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
time_series_length = 4096
class wdcnn_Net(nn.Module):
def __init__(self, num_classes=28, input_channels=4):
super(wdcnn_Net, self).__init__()
self.conv1 = nn.Conv1d(input_channels, 16, kernel_size=64, stride=2, padding=1)
self.bn1 = nn.BatchNorm1d(16)
self.conv2 = nn.Conv1d(16, 32, kernel_size=32, stride=1, padding=1)
self.bn2 = nn.BatchNorm1d(32)
self.conv3 = nn.Conv1d(32, 64, kernel_size=16, stride=1, padding=1)
self.bn3 = nn.BatchNorm1d(64)
self.conv4 = nn.Conv1d(64, 128, kernel_size=8, stride=1, padding=1)
self.bn4 = nn.BatchNorm1d(128)
self.conv5 = nn.Conv1d(128, 128, kernel_size=4, stride=1, padding=1)
self.bn5 = nn.BatchNorm1d(128)
self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(128 * (time_series_length // 64), 128) # 调整全连接层输入维度
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = self.pool(F.relu(self.bn1(self.conv1(x))))
x = self.pool(F.relu(self.bn2(self.conv2(x))))
x = self.pool(F.relu(self.bn3(self.conv3(x))))
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = x.view(x.size(0), -1) # 扁平化
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
# 小波变换这里如何使用???
import pywt
# import numpy as np
def wavelet_transform(data, wavelet='db5', level=1):
"""
对输入数据进行小波变换。
:param data: 输入数据,形状为(batch_size, channels, length)
:param wavelet: 小波变换使用的小波母函数名称
:param level: 小波变换的层级
:return: 小波变换后的数据
"""
coeffs = pywt.wavedec(data, wavelet, level=level)
# 只取近似系数
coeffs = [coeffs[0]]
return pywt.waverec(coeffs, wavelet)
# 示例:对单个样本进行小波变换
# sample_data = np.random.randn(4, 512) # 假设样本
# transformed_data = wavelet_transform(sample_data)