pytorch model()[] 模型对象类型

model = Model() model(input) 直接调用Model类中的forward(input)函数，因其实现了__call__

举个例子

import math, random
import numpy as np

import torch
import torch.nn as nn
import torch.optim as optim
import torch.autograd as autograd 
import torch.nn.functional as F
USE_CUDA = torch.cuda.is_available()
Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(*args, **kwargs)

class Encoder(nn.Module):
    def __init__(self, din=32, hidden_dim=128):
        super(Encoder, self).__init__()
        self.fc = nn.Linear(din, hidden_dim)

    def forward(self, x):
        embedding = F.relu(self.fc(x))
        return embedding

class AttModel(nn.Module):
    def __init__(self, n_node, din, hidden_dim, dout):
        super(AttModel, self).__init__()
        self.fcv = nn.Linear(din, hidden_dim)
        self.fck = nn.Linear(din, hidden_dim)
        self.fcq = nn.Linear(din, hidden_dim)
        self.fcout = nn.Linear(hidden_dim, dout)

    def forward(self, x, mask):
        v = F.relu(self.fcv(x))
        q = F.relu(self.fcq(x))
        k = F.relu(self.fck(x)).permute(0,2,1)
        att = F.softmax(torch.mul(torch.bmm(q,k), mask) - 9e15*(1 - mask),dim=2)

        out = torch.bmm(att,v)
        #out = torch.add(out,v)
        out = F.relu(self.fcout(out))
        return out

class Q_Net(nn.Module):
    def __init__(self, hidden_dim, dout):
        super(Q_Net, self).__init__()
        self.fc = nn.Linear(hidden_dim, dout)

    def forward(self, x):
        q = self.fc(x)
        return q

class DGN(nn.Module):
    def __init__(self,n_agent,num_inputs,hidden_dim,num_actions):
        super(DGN, self).__init__()
        
        self.encoder = Encoder(num_inputs,hidden_dim)
        self.att_1 = AttModel(n_agent,hidden_dim,hidden_dim,hidden_dim)
        self.att_2 = AttModel(n_agent,hidden_dim,hidden_dim,hidden_dim)
        self.q_net = Q_Net(hidden_dim,num_actions)
        
    def forward(self, x, mask):
        h1 = self.encoder(x)
        h2 = self.att_1(h1, mask)
        h3 = self.att_2(h2, mask)
        q = self.q_net(h3)
        return q 

在监视窗口查看

 model是Tensor类型

故model(input)[0]是取第一个batch