简单记个笔记
为什么网络模型有固定的输入维度呢?
计算公式
# 写网络架构:两种方法
import torch
from torch import nn
from torch.nn import Conv2d,MaxPool2d,Flatten,Linear,Sequential
# pytorch的nn.module 的时候从使用说明上可以知道其标准输入为 [B, C, H, W]
# 方法1 直接搭建
class MyModule(nn.Module):
def __init__(self):
# super(MyModule, self).__init__()
super().__init__()
self.conv1 = nn.Conv2d(3,32,5,padding=2)
self.maxpool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(32,32,5,padding=2)
self.maxpool2 = nn.MaxPool2d(2)
self.conv3 = nn.Conv2d(32, 64, 5, padding= 2)
self.maxpool3 = nn.MaxPool2d(2)
self.flatten = nn.Flatten()
self.Linear1 = nn.Linear(1024,64)
self.Linear2 = nn.Linear(64, 10)
def forward(self,x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.conv3(x)
x = self.maxpool3(x)
x = self.flatten(x)
x = self.Linear1(x)
x = self.Linear2(x)
return x
# 方法2 Sequantial
class MyModule2(nn.Module):
def __init__(self):
super().__init__()
self.model1 = Sequential(
Conv2d(3,32,5,padding=2),
MaxPool2d(2),
Conv2d(32,32,5,padding=2),
nn.MaxPool2d(2),
Conv2d(32, 64, 5, padding= 2),
MaxPool2d(2),
Flatten(),
Linear(1024,64),
Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
mymodule = MyModule()
mymodule2 = MyModule2()
print(mymodule) #打印网络
# 测试网络
input = torch.ones((64,3,32,32))
print(input)
output = mymodule2(input) ## 出错 output 是none
print(output.shape)
print("over")
输出
MyModule(
(conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(flatten): Flatten(start_dim=1, end_dim=-1)
(Linear1): Linear(in_features=1024, out_features=64, bias=True)
(Linear2): Linear(in_features=64, out_features=10, bias=True)
)