Class OOP新手对声明变量感到困惑
在pytorch上建立一个神经网络，尝试让完全连接的层接受我之前称之为平坦层的维度 到目前为止，我正在做以下工作：Class OOP新手对声明变量感到困惑,class,oop,pytorch,Class,Oop,Pytorch,在pytorch上建立一个神经网络，尝试让完全连接的层接受我之前称之为平坦层的维度 到目前为止，我正在做以下工作： from torch import nn from torch.nn import functional as F class my_model(nn.Module): def __init__(self, channel=16, out_class=1, flat_dim=24): super(my_model, self).__init__()
from torch import nn
from torch.nn import functional as F
class my_model(nn.Module):
def __init__(self, channel=16, out_class=1, flat_dim=24):
super(my_model, self).__init__()
self.channel = channel
n_class = out_class
self.conv1 = nn.Conv3d(1, channel // 8, kernel_size=5, stride=2, padding=1)
self.pool1 = nn.AvgPool3d(2)
self.bn1 = nn.BatchNorm3d(channel //8)
self.conv2 = nn.Conv3d(channel // 8, channel // 4, kernel_size=4, stride=2, padding=1)
self.bn2 = nn.BatchNorm3d(channel // 4)
self.pool2 = nn.AvgPool3d(2)
self.fc1 = nn.Linear(flat_dim,500)
self.fc2 = nn.Linear(500,100)
def forward(self, x):
batch_size, D, W, H, T = x.shape
print('input to model', x.shape)
x = x.permute(0,4,1,2,3)
x = x.reshape(batch_size * T, 1, D, W, H)
print('after reshape', x.shape)
h1 = F.relu(self.bn1(self.pool1(self.conv1(x))))
print('after first layer sequence', h1.shape)
h2 = F.relu(self.bn2(self.pool2(self.conv2(h1))))
print('output to model', h2.shape)
h2, flat_dim = self.flatten_cube(h2)
h3 = self.fc1(h2)
h4 = self.fc2(h3)
print(h4.shape)
return h4
def flatten_cube(self, x):
super_batch, C, D, W, H = x.shape
flat_dim = C * D * W * H
y = x.view(super_batch, flat_dim)
return y, flat_dim