import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch.nn as nn
from torch.utils.data import Dataset
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch import nn
class DepthwiseConv(nn.Module):
def __init__(self, inp, oup):
super(DepthwiseConv, self).__init__()
self.depth_conv = nn.Sequential(
# dw
nn.Conv2d(inp, inp, kernel_size=3, stride=1, padding=1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
# pw
nn.Conv2d(inp, oup, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(oup)
)
def forward(self, x):
return self.depth_conv(x)
class depthwise_separable_conv(nn.Module):
def __init__(self, ch_in, ch_out):
super(depthwise_separable_conv, self).__init__()
self.ch_in = ch_in
self.ch_out = ch_out
self.depth_conv = nn.Conv2d(ch_in, ch_in, kernel_size=3, padding=1, groups=ch_in)
self.point_conv = nn.Conv2d(ch_in, ch_out, kernel_size=1)
def forward(self, x):
x = self.depth_conv(x)
x = self.point_conv(x)
return x
class EEGNet(nn.Module):
def __init__(self):
super(EEGNet, self).__init__()
self.T = 500
self.conv1 = nn.Conv2d(22,48,(3,3),padding=0)
self.batchnorm1 = nn.BatchNorm2d(48,False)
self.Depth_conv = DepthwiseConv(inp=48,oup=22)
self.pooling1 = nn.AvgPool2d(4,4)
self.Separable_conv = depthwise_separable_conv(ch_in=22, ch_out=48)
self.batchnorm2 = nn.BatchNorm2d(48,False)
self.pooling2 = nn.AvgPool2d(2,2)
self.fc1 = nn.Linear(576, 256)
self.fc2 = nn.Linear(256,64)
self.fc3 = nn.Linear(64,4)
def forward(self, item):
x = F.relu(self.conv1(item))
x = self.batchnorm1(x)
x = F.relu(self.Depth_conv(x))
x = self.pooling1(x)
x = F.relu(self.Separable_conv(x))
x = self.batchnorm2(x)
x = self.pooling2(x)
#flatten
x = x.contiguous().view(x.size()[0],-1)
#view函数：-1为计算后的自动填充值=batch_size，或x = x.contiguous().view(batch_size,x.size()[0])
x = F.relu(self.fc1(x))
x = F.dropout(x,0.25)
x = F.relu(self.fc2(x))
x = F.dropout(x,0.5)
x = F.softmax(self.fc3(x),dim=1)
return x
发生异常: RuntimeError
Given groups=1, weight of size [48, 22, 3, 3], expected input[32, 1, 22, 1000] to have 22 channels, but got 1 channels instead
File "D:\BCIshuju\2a+b\CNN-training-testing-templates-for-processing-EEG-signals-main\EEGNet_Module.py", line 61, in forward
x = F.relu(self.conv1(item))
File "D:\BCIshuju\2a+b\CNN-training-testing-templates-for-processing-EEG-signals-main\Main.py", line 274, in <module>
output = model(data)
RuntimeError: Given groups=1, weight of size [48, 22, 3, 3], expected input[32, 1, 22, 1000] to have 22 channels, but got 1 channels instead
最新发布
博客提供了两个优快云的博客链接，分别为https://blog.youkuaiyun.com/mao_xiao_feng/article/details/78002811和https://blog.youkuaiyun.com/mao_xiao_feng/article/details/78003476，但未提及链接内具体信息技术内容。
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