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from cortex_DIM.nn_modules.mi_networks import MIFCNet, MI1x1ConvNet
from torch import optim
from torch.autograd import Variable
import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class GlobalDiscriminator(nn.Module):
def __init__(self, args, input_dim):
super().__init__()
self.l0 = nn.Linear(32, 32)
self.l1 = nn.Linear(32, 32)
self.l2 = nn.Linear(512, 1)
def forward(self, y, M, data):
adj = Variable(data['adj'].float(), requires_grad=False).cuda()
# h0 = Variable(data['feats'].float()).cuda()
batch_num_nodes = data['num_nodes'].int().numpy()
M, _ = self.encoder(M, adj, batch_num_nodes)
# h = F.relu(self.c0(M))
# h = self.c1(h)
# h = h.view(y.shape[0], -1)
h = torch.cat((y, M), dim=1)
h = F.relu(self.l0(h))
h = F.relu(self.l1(h))
return self.l2(h)
class PriorDiscriminator(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.l0 = nn.Linear(input_dim, input_dim)
self.l1 = nn.Linear(input_dim, input_dim)
self.l2 = nn.Linear(input_dim, 1)
def forward(self, x):
h = F.relu(self.l0(x))
h = F.relu(self.l1(h))
return torch.sigmoid(self.l2(h))
class FF(nn.Module):
def __init__(self, input_dim):
super().__init__()
# self.c0 = nn.Conv1d(input_dim, 512, kernel_size=1)
# self.c1 = nn.Conv1d(512, 512, kernel_size=1)
# self.c2 = nn.Conv1d(512, 1, kernel_size=1)
self.block = nn.Sequential(
nn.Linear(input_dim, input_dim),
nn.ReLU(),
nn.Linear(input_dim, input_dim),
nn.ReLU(),
nn.Linear(input_dim, input_dim),
nn.ReLU()
)
self.linear_shortcut = nn.Linear(input_dim, input_dim)
# self.c0 = nn.Conv1d(input_dim, 512, kernel_size=1, stride=1, padding=0)
# self.c1 = nn.Conv1d(512, 512, kernel_size=1, stride=1, padding=0)
# self.c2 = nn.Conv1d(512, 1, kernel_size=1, stride=1, padding=0)
def forward(self, x):
return self.block(x) + self.linear_shortcut(x)
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