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"""Modified from https://github.com/chaofengc/PSFRGAN
"""
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class NormLayer(nn.Module):
"""Normalization Layers.
Args:
channels: input channels, for batch norm and instance norm.
input_size: input shape without batch size, for layer norm.
"""
def __init__(self, channels, normalize_shape=None, norm_type='bn'):
super(NormLayer, self).__init__()
norm_type = norm_type.lower()
self.norm_type = norm_type
if norm_type == 'bn':
self.norm = nn.BatchNorm2d(channels, affine=True)
elif norm_type == 'in':
self.norm = nn.InstanceNorm2d(channels, affine=False)
elif norm_type == 'gn':
self.norm = nn.GroupNorm(32, channels, affine=True)
elif norm_type == 'pixel':
self.norm = lambda x: F.normalize(x, p=2, dim=1)
elif norm_type == 'layer':
self.norm = nn.LayerNorm(normalize_shape)
elif norm_type == 'none':
self.norm = lambda x: x * 1.0
else:
assert 1 == 0, f'Norm type {norm_type} not support.'
def forward(self, x, ref=None):
if self.norm_type == 'spade':
return self.norm(x, ref)
else:
return self.norm(x)
class ReluLayer(nn.Module):
"""Relu Layer.
Args:
relu type: type of relu layer, candidates are
- ReLU
- LeakyReLU: default relu slope 0.2
- PRelu
- SELU
- none: direct pass
"""
def __init__(self, channels, relu_type='relu'):
super(ReluLayer, self).__init__()
relu_type = relu_type.lower()
if relu_type == 'relu':
self.func = nn.ReLU(True)
elif relu_type == 'leakyrelu':
self.func = nn.LeakyReLU(0.2, inplace=True)
elif relu_type == 'prelu':
self.func = nn.PReLU(channels)
elif relu_type == 'selu':
self.func = nn.SELU(True)
elif relu_type == 'none':
self.func = lambda x: x * 1.0
else:
assert 1 == 0, f'Relu type {relu_type} not support.'
def forward(self, x):
return self.func(x)
class ConvLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
scale='none',
norm_type='none',
relu_type='none',
use_pad=True,
bias=True):
super(ConvLayer, self).__init__()
self.use_pad = use_pad
self.norm_type = norm_type
if norm_type in ['bn']:
bias = False
stride = 2 if scale == 'down' else 1
self.scale_func = lambda x: x
if scale == 'up':
self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
self.relu = ReluLayer(out_channels, relu_type)
self.norm = NormLayer(out_channels, norm_type=norm_type)
def forward(self, x):
out = self.scale_func(x)
if self.use_pad:
out = self.reflection_pad(out)
out = self.conv2d(out)
out = self.norm(out)
out = self.relu(out)
return out
class ResidualBlock(nn.Module):
"""
Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
"""
def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
super(ResidualBlock, self).__init__()
if scale == 'none' and c_in == c_out:
self.shortcut_func = lambda x: x
else:
self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
scale_conf = scale_config_dict[scale]
self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
def forward(self, x):
identity = self.shortcut_func(x)
res = self.conv1(x)
res = self.conv2(res)
return identity + res
class ParseNet(nn.Module):
def __init__(self,
in_size=128,
out_size=128,
min_feat_size=32,
base_ch=64,
parsing_ch=19,
res_depth=10,
relu_type='LeakyReLU',
norm_type='bn',
ch_range=[32, 256]):
super().__init__()
self.res_depth = res_depth
act_args = {'norm_type': norm_type, 'relu_type': relu_type}
min_ch, max_ch = ch_range
ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731
min_feat_size = min(in_size, min_feat_size)
down_steps = int(np.log2(in_size // min_feat_size))
up_steps = int(np.log2(out_size // min_feat_size))
# =============== define encoder-body-decoder ====================
self.encoder = []
self.encoder.append(ConvLayer(3, base_ch, 3, 1))
head_ch = base_ch
for i in range(down_steps):
cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
head_ch = head_ch * 2
self.body = []
for i in range(res_depth):
self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
self.decoder = []
for i in range(up_steps):
cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
head_ch = head_ch // 2
self.encoder = nn.Sequential(*self.encoder)
self.body = nn.Sequential(*self.body)
self.decoder = nn.Sequential(*self.decoder)
self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
def forward(self, x):
feat = self.encoder(x)
x = feat + self.body(feat)
x = self.decoder(x)
out_img = self.out_img_conv(x)
out_mask = self.out_mask_conv(x)
return out_mask, out_img
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