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import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.normalization import get_norm_layer
class ConvEncoder(BaseNetwork):
""" Same architecture as the image discriminator """
def __init__(self, opt):
super().__init__()
self.opt = opt
kw = 3
pw = int(np.ceil((kw - 1.0) / 2))
ndf = opt.ngf
norm_layer = get_norm_layer(opt, opt.norm_E)
self.layer1 = norm_layer(nn.Conv2d(3, ndf, kw, stride=2, padding=pw))
self.layer2 = norm_layer(nn.Conv2d(ndf * 1, ndf * 2, kw, stride=2, padding=pw))
self.layer3 = norm_layer(nn.Conv2d(ndf * 2, ndf * 4, kw, stride=2, padding=pw))
self.layer4 = norm_layer(nn.Conv2d(ndf * 4, ndf * 8, kw, stride=2, padding=pw))
self.layer5 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw))
if opt.crop_size >= 256:
self.layer6 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw))
self.so = s0 = 4
self.fc_mu = nn.Linear(ndf * 8 * s0 * s0, 256)
self.fc_var = nn.Linear(ndf * 8 * s0 * s0, 256)
self.actvn = nn.LeakyReLU(0.2, False)
def forward(self, x):
if x.size(2) != 256 or x.size(3) != 256:
x = F.interpolate(x, size=(256, 256), mode='bilinear')
x = self.layer1(x)
x = self.layer2(self.actvn(x))
x = self.layer3(self.actvn(x))
x = self.layer4(self.actvn(x))
x = self.layer5(self.actvn(x))
if self.opt.crop_size >= 256:
x = self.layer6(self.actvn(x))
x = self.actvn(x)
x = x.view(x.size(0), -1)
mu = self.fc_mu(x)
logvar = self.fc_var(x)
return mu, logvar
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