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# ------------------------------------------------------------------------------
# OptVQ: Preventing Local Pitfalls in Vector Quantization via Optimal Transport
# Copyright (c) 2024 Borui Zhang. All Rights Reserved.
# Licensed under the MIT License [see LICENSE for details]
# ------------------------------------------------------------------------------
import torch.nn as nn
class PlainCNNEncoder(nn.Module):
def __init__(self, in_dim: int = 3):
super(PlainCNNEncoder, self).__init__()
self.in_dim = in_dim
self.in_fc = nn.Conv2d(in_channels=in_dim, out_channels=16,
kernel_size=3, stride=1, padding=1, bias=True)
self.act0 = nn.ReLU(inplace=True)
self.down1 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=2, stride=2)
self.conv1 = nn.Conv2d(in_channels=16, out_channels=16,
kernel_size=3, stride=1, padding=1, bias=True)
self.act1 = nn.ReLU(inplace=True)
self.down2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32,
kernel_size=3, stride=1, padding=1, bias=True)
self.act2 = nn.ReLU(inplace=True)
self.out_fc = nn.Conv2d(in_channels=32, out_channels=32,
kernel_size=3, stride=1, padding=1, bias=True)
@property
def hidden_dim(self):
return 32
def forward(self, x):
x = self.in_fc(x)
x = self.act0(x)
x = self.down1(x)
x = self.conv1(x)
x = self.act1(x)
x = self.down2(x)
x = self.conv2(x)
x = self.act2(x)
x = self.out_fc(x)
return x
class PlainCNNDecoder(nn.Module):
def __init__(self, out_dim: int = 3):
super(PlainCNNDecoder, self).__init__()
self.out_dim = out_dim
self.in_fc = nn.Conv2d(in_channels=32, out_channels=32,
kernel_size=3, stride=1, padding=1, bias=True)
self.act1 = nn.ReLU(inplace=True)
self.up1 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=2, stride=2)
self.conv1 = nn.Conv2d(in_channels=16, out_channels=16,
kernel_size=3, stride=1, padding=1, bias=True)
self.act2 = nn.ReLU(inplace=True)
self.up2 = nn.ConvTranspose2d(in_channels=16, out_channels=16, kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=16,
kernel_size=3, stride=1, padding=1, bias=True)
self.act3 = nn.ReLU(inplace=True)
self.out_fc = nn.Conv2d(in_channels=16, out_channels=out_dim,
kernel_size=3, stride=1, padding=1, bias=True)
@property
def hidden_dim(self):
return 32
def forward(self, x):
x = self.in_fc(x)
x = self.act1(x)
x = self.up1(x)
x = self.conv1(x)
x = self.act2(x)
x = self.up2(x)
x = self.conv2(x)
x = self.act3(x)
x = self.out_fc(x)
return x |