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import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
from scripts.hf_utils import download_file
class UnetGenerator(nn.Module):
"""Create a Unet-based generator"""
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer
for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer
def forward(self, input):
"""Standard forward"""
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def __init__(self, outer_nc, inner_nc, input_nc=None,
submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return torch.cat([x, self.model(x)], 1)
class Smooth(nn.Module):
def __init__(self):
super().__init__()
kernel = [
[1, 2, 1],
[2, 4, 2],
[1, 2, 1]
]
kernel = torch.tensor([[kernel]], dtype=torch.float)
kernel /= kernel.sum()
self.register_buffer('kernel', kernel)
self.pad = nn.ReplicationPad2d(1)
def forward(self, x):
b, c, h, w = x.shape
x = x.view(-1, 1, h, w)
x = self.pad(x)
x = F.conv2d(x, self.kernel)
return x.view(b, c, h, w)
class Upsample(nn.Module):
def __init__(self, inc, outc, scale_factor=2):
super().__init__()
self.scale_factor = scale_factor
self.up = nn.Upsample(scale_factor=scale_factor, mode='bilinear')
self.smooth = Smooth()
self.conv = nn.Conv2d(inc, outc, kernel_size=3, stride=1, padding=1)
self.mlp = nn.Sequential(
nn.Conv2d(outc, 4 * outc, kernel_size=1, stride=1, padding=0),
nn.GELU(),
nn.Conv2d(4 * outc, outc, kernel_size=1, stride=1, padding=0),
)
def forward(self, x):
x = self.smooth(self.up(x))
x = self.conv(x)
x = self.mlp(x) + x
return x
def create_model(model, use_local):
"""Create a model for anime2sketch
hardcoding the options for simplicity
"""
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False)
import os
if model == 'default':
model_path = "models/Anime2Sketch/netG.pth" if use_local else download_file("netG.pth", "models/Anime2Sketch")
ckpt = torch.load(model_path)
for key in list(ckpt.keys()):
if 'module.' in key:
ckpt[key.replace('module.', '')] = ckpt[key]
del ckpt[key]
net.load_state_dict(ckpt)
elif model == 'improved':
ckpt = torch.load('weights/improved.bin', map_location=torch.device('cpu'))
base = net.model.model[1]
# swap deconvolution layers with reszie + conv layers for 2x upsampling
for _ in range(6):
inc, outc = base.model[5].in_channels, base.model[5].out_channels
base.model[5] = Upsample(inc, outc)
base = base.model[3]
net.load_state_dict(ckpt)
else:
raise ValueError(f"model should be one of ['default', 'improved'], but got {model}")
return net |