Pyorch计算模型参数

介绍一个简单计算模型参数的API，实现模型参数的快速计算
使用方法：

pip install torchsummary

from torchsummary import summary
summary(your_model, input_size=(channels, H, W))

改变输入的大小不会改变网络参数的呃呃大小

例子：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0
model = Net().to(device)

summary(model, (1, 28, 28))

import torch
from torchvision import models
from torchsummary import summary

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vgg = models.vgg16().to(device)

summary(vgg, (3, 224, 224))

import torch
import torch.nn as nn
from torchsummary import summary

class SimpleConv(nn.Module):
    def __init__(self):
        super(SimpleConv, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
        )

    def forward(self, x, y):
        x1 = self.features(x)
        x2 = self.features(y)
        return x1, x2
    
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleConv().to(device)

summary(model, [(1, 16, 16), (1, 28, 28)])