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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch import Tensor
# __all__ = [
# "ResidualConvBlock",
# "Discriminator", "Generator",
# ]
class ResidualConvBlock(nn.Module):
"""Implements residual conv function.
Args:
channels (int): Number of channels in the input image.
"""
def __init__(self, channels: int) -> None:
super(ResidualConvBlock, self).__init__()
self.rcb = nn.Sequential(
nn.Conv2d(channels, channels, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(channels),
nn.PReLU(),
nn.Conv2d(channels, channels, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(channels),
)
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.rcb(x)
out = torch.add(out, identity)
return out
class Discriminator(nn.Module):
def __init__(self) -> None:
super(Discriminator, self).__init__()
self.features = nn.Sequential(
# input size. (3) x 96 x 96
nn.Conv2d(3, 64, (3, 3), (1, 1), (1, 1), bias=False),
nn.LeakyReLU(0.2, True),
# state size. (64) x 48 x 48
nn.Conv2d(64, 64, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 128, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, True),
# state size. (128) x 24 x 24
nn.Conv2d(128, 128, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 256, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, True),
# state size. (256) x 12 x 12
nn.Conv2d(256, 256, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, True),
nn.Conv2d(256, 512, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, True),
# state size. (512) x 6 x 6
nn.Conv2d(512, 512, (3, 3), (2, 2), (1, 1), bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, True),
)
self.classifier = nn.Sequential(
nn.Linear(512 * 6 * 6, 1024),
nn.LeakyReLU(0.2, True),
nn.Linear(1024, 1),
)
def forward(self, x: Tensor) -> Tensor:
out = self.features(x)
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
class Generator(nn.Module):
def __init__(self) -> None:
super(Generator, self).__init__()
# First conv layer.
self.conv_block1 = nn.Sequential(
nn.Conv2d(3, 64, (9, 9), (1, 1), (4, 4)),
nn.PReLU(),
)
# Features trunk blocks.
trunk = []
for _ in range(16):
trunk.append(ResidualConvBlock(64))
self.trunk = nn.Sequential(*trunk)
# Second conv layer.
self.conv_block2 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1), bias=False),
nn.BatchNorm2d(64),
)
# Upscale conv block.
self.upsampling = nn.Sequential(
nn.Conv2d(64, 256, (3, 3), (1, 1), (1, 1)),
nn.PixelShuffle(2),
nn.PReLU(),
nn.Conv2d(64, 256, (3, 3), (1, 1), (1, 1)),
nn.PixelShuffle(2),
nn.PReLU(),
)
# Output layer.
self.conv_block3 = nn.Conv2d(64, 3, (9, 9), (1, 1), (4, 4))
# Initialize neural network weights.
self._initialize_weights()
def forward(self, x: Tensor, dop=None) -> Tensor:
if not dop:
return self._forward_impl(x)
else:
return self._forward_w_dop_impl(x, dop)
# Support torch.script function.
def _forward_impl(self, x: Tensor) -> Tensor:
out1 = self.conv_block1(x)
out = self.trunk(out1)
out2 = self.conv_block2(out)
out = torch.add(out1, out2)
out = self.upsampling(out)
out = self.conv_block3(out)
return out
def _forward_w_dop_impl(self, x: Tensor, dop) -> Tensor:
out1 = self.conv_block1(x)
out = self.trunk(out1)
out2 = F.dropout2d(self.conv_block2(out), p=dop)
out = torch.add(out1, out2)
out = self.upsampling(out)
out = self.conv_block3(out)
return out
def _initialize_weights(self) -> None:
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
nn.init.constant_(module.weight, 1)
#### BayesCap
class BayesCap(nn.Module):
def __init__(self, in_channels=3, out_channels=3) -> None:
super(BayesCap, self).__init__()
# First conv layer.
self.conv_block1 = nn.Sequential(
nn.Conv2d(
in_channels, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
)
# Features trunk blocks.
trunk = []
for _ in range(16):
trunk.append(ResidualConvBlock(64))
self.trunk = nn.Sequential(*trunk)
# Second conv layer.
self.conv_block2 = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=3, stride=1, padding=1, bias=False
),
nn.BatchNorm2d(64),
)
# Output layer.
self.conv_block3_mu = nn.Conv2d(
64, out_channels=out_channels,
kernel_size=9, stride=1, padding=4
)
self.conv_block3_alpha = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 1,
kernel_size=9, stride=1, padding=4
),
nn.ReLU(),
)
self.conv_block3_beta = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 1,
kernel_size=9, stride=1, padding=4
),
nn.ReLU(),
)
# Initialize neural network weights.
self._initialize_weights()
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
# Support torch.script function.
def _forward_impl(self, x: Tensor) -> Tensor:
out1 = self.conv_block1(x)
out = self.trunk(out1)
out2 = self.conv_block2(out)
out = out1 + out2
out_mu = self.conv_block3_mu(out)
out_alpha = self.conv_block3_alpha(out)
out_beta = self.conv_block3_beta(out)
return out_mu, out_alpha, out_beta
def _initialize_weights(self) -> None:
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
nn.init.constant_(module.weight, 1)
class BayesCap_noID(nn.Module):
def __init__(self, in_channels=3, out_channels=3) -> None:
super(BayesCap_noID, self).__init__()
# First conv layer.
self.conv_block1 = nn.Sequential(
nn.Conv2d(
in_channels, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
)
# Features trunk blocks.
trunk = []
for _ in range(16):
trunk.append(ResidualConvBlock(64))
self.trunk = nn.Sequential(*trunk)
# Second conv layer.
self.conv_block2 = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=3, stride=1, padding=1, bias=False
),
nn.BatchNorm2d(64),
)
# Output layer.
# self.conv_block3_mu = nn.Conv2d(
# 64, out_channels=out_channels,
# kernel_size=9, stride=1, padding=4
# )
self.conv_block3_alpha = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 1,
kernel_size=9, stride=1, padding=4
),
nn.ReLU(),
)
self.conv_block3_beta = nn.Sequential(
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 64,
kernel_size=9, stride=1, padding=4
),
nn.PReLU(),
nn.Conv2d(
64, 1,
kernel_size=9, stride=1, padding=4
),
nn.ReLU(),
)
# Initialize neural network weights.
self._initialize_weights()
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
# Support torch.script function.
def _forward_impl(self, x: Tensor) -> Tensor:
out1 = self.conv_block1(x)
out = self.trunk(out1)
out2 = self.conv_block2(out)
out = out1 + out2
# out_mu = self.conv_block3_mu(out)
out_alpha = self.conv_block3_alpha(out)
out_beta = self.conv_block3_beta(out)
return out_alpha, out_beta
def _initialize_weights(self) -> None:
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
nn.init.constant_(module.weight, 1) |