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Zero
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"""
backbone.py - Contains the backbone of the model.
(It is based on LPIENet and CURL's backbone)
Perceptual Image Enhancement for Smartphone Real-Time Applications
https://github.com/mv-lab/AISP
CURL: Neural Curve Layers for Global Image Enhancement
https://github.com/sjmoran/CURL
David Serrano (dserrano@cvc.uab.cat)
May 2024
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
class AttentionBlock(nn.Module):
def __init__(self, dim: int):
super(AttentionBlock, self).__init__()
self._spatial_attention_conv = nn.Conv2d(2, dim, kernel_size=3, padding=1)
# Channel attention MLP
self._channel_attention_conv0 = nn.Conv2d(1, dim, kernel_size=1, padding=0)
self._channel_attention_conv1 = nn.Conv2d(dim, dim, kernel_size=1, padding=0)
self._out_conv = nn.Conv2d(2 * dim, dim, kernel_size=1, padding=0)
def forward(self, x: torch.Tensor):
if len(x.shape) != 4:
raise ValueError(f"Expected [B, C, H, W] input, got {x.shape}.")
# Spatial attention
mean = torch.mean(x, dim=1, keepdim=True) # Mean/Max on C axis
max, _ = torch.max(x, dim=1, keepdim=True)
spatial_attention = torch.cat([mean, max], dim=1) # [B, 2, H, W]
spatial_attention = self._spatial_attention_conv(spatial_attention)
spatial_attention = torch.sigmoid(spatial_attention) * x
# NOTE: This differs from CBAM as it uses Channel pooling, not spatial pooling!
# In a way, this is 2x spatial attention
channel_attention = torch.relu(self._channel_attention_conv0(mean))
channel_attention = self._channel_attention_conv1(channel_attention)
channel_attention = torch.sigmoid(channel_attention) * x
attention = torch.cat([spatial_attention, channel_attention], dim=1) # [B, 2*dim, H, W]
attention = self._out_conv(attention)
return x + attention
class InverseBlock(nn.Module):
def __init__(self, input_channels: int, channels: int):
super(InverseBlock, self).__init__()
self._conv0 = nn.Conv2d(input_channels, channels, kernel_size=1)
self._dw_conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1, groups=channels)
self._conv1 = nn.Conv2d(channels, channels, kernel_size=1)
self._conv2 = nn.Conv2d(input_channels, channels, kernel_size=1)
def forward(self, x: torch.Tensor):
features = self._conv0(x)
features = F.elu(self._dw_conv(features))
features = self._conv1(features)
x = torch.relu(self._conv2(x))
return x + features
class BaseBlock(nn.Module):
def __init__(self, channels: int):
super(BaseBlock, self).__init__()
self._conv0 = nn.Conv2d(channels, channels, kernel_size=1)
self._dw_conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1, groups=channels)
self._conv1 = nn.Conv2d(channels, channels, kernel_size=1)
self._conv2 = nn.Conv2d(channels, channels, kernel_size=1)
self._conv3 = nn.Conv2d(channels, channels, kernel_size=1)
def forward(self, x: torch.Tensor):
features = self._conv0(x)
features = F.elu(self._dw_conv(features))
features = self._conv1(features)
x = x + features
features = F.elu(self._conv2(x))
features = self._conv3(features)
return x + features
class AttentionTail(nn.Module):
def __init__(self, channels: int):
super(AttentionTail, self).__init__()
self._conv0 = nn.Conv2d(channels, channels, kernel_size=7, padding=3)
self._conv1 = nn.Conv2d(channels, channels, kernel_size=5, padding=2)
self._conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
def forward(self, x: torch.Tensor):
attention = torch.relu(self._conv0(x))
attention = torch.relu(self._conv1(attention))
attention = torch.sigmoid(self._conv2(attention))
return x * attention
class Flatten(nn.Module):
def forward(self, x):
"""Flatten a Tensor to a Vector
:param x: Tensor
:returns: 1D Tensor
:rtype: Tensor
"""
return x.view(x.size()[0], -1)
class ResidualConnection(nn.Module):
def __init__(self, in_channels):
super(ResidualConnection, self).__init__()
self.in_channels = in_channels
self.midnet2 = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, 1, 2, 2),
nn.LeakyReLU(),
nn.Conv2d(64, 64, 3, 1, 2, 2),
nn.LeakyReLU()
)
self.midnet4 = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, 1, 4, 4),
nn.LeakyReLU(),
nn.Conv2d(64, 64, 3, 1, 4, 4),
nn.LeakyReLU()
)
self.globnet = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, 2, 1, 1),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.Conv2d(64, 64, 3, 2, 1, 1),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.Conv2d(64, 64, 3, 2, 1, 1),
nn.LeakyReLU(),
nn.AdaptiveAvgPool2d(1),
Flatten(),
nn.Dropout(0.5),
nn.Linear(64, 64)
)
self.conv_fuse = nn.Conv2d(in_channels=192+in_channels, out_channels=in_channels, kernel_size=1)
def forward(self, x):
x_midnet2 = self.midnet2(x)
x_midnet4 = self.midnet4(x)
x_global = self.globnet(x).unsqueeze(2).unsqueeze(3)
x_global = x_global.repeat(1, 1, x_midnet2.shape[2], x_midnet2.shape[3])
x_fuse = torch.cat((x, x_midnet2, x_midnet4, x_global), dim=1)
x_out = self.conv_fuse(x_fuse)
return x_out
class Backbone(nn.Module):
def __init__(self, input_channels: int, output_channels: int, encoder_dims: List[int], decoder_dims: List[int]):
super(Backbone, self).__init__()
if len(encoder_dims) != len(decoder_dims) + 1 or len(decoder_dims) < 1:
raise ValueError(f"Unexpected encoder and decoder dims: {encoder_dims}, {decoder_dims}.")
if input_channels != output_channels:
raise NotImplementedError()
encoders = []
for i, encoder_dim in enumerate(encoder_dims):
input_dim = input_channels if i == 0 else encoder_dims[i - 1]
encoders.append(
nn.Sequential(
nn.Conv2d(input_dim, encoder_dim, kernel_size=3, padding=1),
BaseBlock(encoder_dim),
BaseBlock(encoder_dim),
AttentionBlock(encoder_dim),
)
)
self._encoders = nn.ModuleList(encoders)
decoders = []
for i, decoder_dim in enumerate(decoder_dims):
input_dim = encoder_dims[-1] if i == 0 else decoder_dims[i - 1] + encoder_dims[-i - 1]
decoders.append(
nn.Sequential(
nn.Conv2d(input_dim, decoder_dim, kernel_size=3, padding=1),
BaseBlock(decoder_dim),
BaseBlock(decoder_dim),
AttentionBlock(decoder_dim),
)
)
self._decoders = nn.ModuleList(decoders)
self._inverse_bock = InverseBlock(encoder_dims[0] + decoder_dims[-1], output_channels)
self._attention_tail = AttentionTail(output_channels)
residual_connections = []
for i, decoder_dim in enumerate(encoder_dims):
residual_connections.append(
ResidualConnection(in_channels=decoder_dim)
)
self._residual_connections = nn.ModuleList(residual_connections)
def forward(self, x: torch.Tensor):
if len(x.shape) != 4:
raise ValueError(f"Expected [B, C, H, W] input, got {x.shape}.")
global_residual = x
encoder_outputs, residual_connections = [], []
for i, encoder in enumerate(self._encoders):
x = encoder(x)
if i != len(self._encoders) - 1:
encoder_outputs.append(x)
residual_connections.append(self._residual_connections[i](x))
x = F.max_pool2d(x, kernel_size=2)
encoder_outputs.reverse()
residual_connections.reverse()
for i, decoder in enumerate(self._decoders):
x = decoder(x)
x = nn.Upsample(size=encoder_outputs[i].shape[2:], mode='bilinear', align_corners=False)(x)
x = torch.cat([x, residual_connections[i]], dim=1)
x = self._inverse_bock(x)
x = self._attention_tail(x)
return torch.clip(x + global_residual, 0, 1) |