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import torch.nn as nn
import torch.nn.functional as F
from utils.common import initialize_weights
from .layers import LayerNorm2d
class DownConv(nn.Module):
def __init__(self, channels, bias=False):
super(DownConv, self).__init__()
self.conv1 = SeparableConv2D(channels, channels, stride=2, bias=bias)
self.conv2 = SeparableConv2D(channels, channels, stride=1, bias=bias)
def forward(self, x):
out1 = self.conv1(x)
out2 = F.interpolate(x, scale_factor=0.5, mode='bilinear')
out2 = self.conv2(out2)
return out1 + out2
class UpConv(nn.Module):
def __init__(self, channels, bias=False):
super(UpConv, self).__init__()
self.conv = SeparableConv2D(channels, channels, stride=1, bias=bias)
def forward(self, x):
out = F.interpolate(x, scale_factor=2.0, mode='bilinear')
out = self.conv(out)
return out
class UpConvLNormLReLU(nn.Module):
"""Upsample Conv block with Layer Norm and Leaky ReLU"""
def __init__(self, in_channels, out_channels, bias=False):
super(UpConvLNormLReLU, self).__init__()
self.conv_block = ConvBlock(
in_channels,
out_channels,
kernel_size=3,
bias=bias,
)
def forward(self, x):
out = F.interpolate(x, scale_factor=2.0, mode='bilinear')
out = self.conv_block(out)
return out
class SeparableConv2D(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, bias=False):
super(SeparableConv2D, self).__init__()
self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size=3,
stride=stride, padding=1, groups=in_channels, bias=bias)
self.pointwise = nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=1, bias=bias)
# self.pad =
self.ins_norm1 = nn.InstanceNorm2d(in_channels)
self.activation1 = nn.LeakyReLU(0.2, True)
self.ins_norm2 = nn.InstanceNorm2d(out_channels)
self.activation2 = nn.LeakyReLU(0.2, True)
initialize_weights(self)
def forward(self, x):
out = self.depthwise(x)
out = self.ins_norm1(out)
out = self.activation1(out)
out = self.pointwise(out)
out = self.ins_norm2(out)
return self.activation2(out)
class ConvBlock(nn.Module):
"""Stack of Conv2D + Norm + LeakyReLU"""
def __init__(
self,
channels,
out_channels,
kernel_size=3,
stride=1,
padding="valid",
bias=False,
norm_type="instance"
):
super(ConvBlock, self).__init__()
if kernel_size == 3 and stride == 1:
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
elif kernel_size == 7 and stride == 1:
self.pad = nn.ReflectionPad2d((3, 3, 3, 3))
elif stride == 2:
self.pad = nn.ReflectionPad2d((0, 1, 1, 0))
else:
self.pad = None
self.conv = nn.Conv2d(
channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=bias
)
if norm_type == "instance":
self.ins_norm = nn.InstanceNorm2d(out_channels)
elif norm_type == "layer":
self.ins_norm = LayerNorm2d(out_channels)
self.activation = nn.LeakyReLU(0.2, True)
initialize_weights(self)
def forward(self, x):
if self.pad is not None:
x = self.pad(x)
out = self.conv(x)
out = self.ins_norm(out)
out = self.activation(out)
return out
class InvertedResBlock(nn.Module):
def __init__(
self,
channels=256,
out_channels=256,
expand_ratio=2,
bias=False,
norm_type="instance",
):
super(InvertedResBlock, self).__init__()
bottleneck_dim = round(expand_ratio * channels)
self.conv_block = ConvBlock(
channels,
bottleneck_dim,
kernel_size=1,
stride=1,
padding=0,
bias=bias
)
self.depthwise_conv = nn.Conv2d(
bottleneck_dim,
bottleneck_dim,
kernel_size=3,
groups=bottleneck_dim,
stride=1,
padding=1,
bias=bias
)
self.conv = nn.Conv2d(
bottleneck_dim,
out_channels,
kernel_size=1,
stride=1,
bias=bias
)
if norm_type == "instance":
self.ins_norm1 = nn.InstanceNorm2d(out_channels)
self.ins_norm2 = nn.InstanceNorm2d(out_channels)
elif norm_type == "layer":
# Keep var name as is for v1 compatibility.
self.ins_norm1 = LayerNorm2d(bottleneck_dim)
self.ins_norm2 = LayerNorm2d(out_channels)
self.activation = nn.LeakyReLU(0.2, True)
initialize_weights(self)
def forward(self, x):
out = self.conv_block(x)
out = self.depthwise_conv(out)
out = self.ins_norm1(out)
out = self.activation(out)
out = self.conv(out)
out = self.ins_norm2(out)
if out.shape[1] != x.shape[1]:
# Only concate if same shape
return out
return out + x
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