Source code for dalib.translation.cyclegan.generator
"""
Modified from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import torch
import torch.nn as nn
import functools
from .util import get_norm_layer, init_weights
class ResnetBlock(nn.Module):
"""Define a Resnet block"""
def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Initialize the Resnet block
A resnet block is a conv block with skip connections
We construct a conv block with build_conv_block function,
and implement skip connections in <forward> function.
Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
"""
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Construct a convolutional block.
Args:
dim (int): the number of channels in the conv layer.
padding_type (str): the name of padding layer: reflect | replicate | zero
norm_layer (torch.nn.Module): normalization layer
use_dropout (bool): if use dropout layers.
use_bias (bool): if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
"""Forward function (with skip connections)"""
out = x + self.conv_block(x) # add skip connections
return out
class ResnetGenerator(nn.Module):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
"""Construct a Resnet-based generator
Args:
input_nc (int): the number of channels in input images
output_nc (int): the number of channels in output images
ngf (int): the number of filters in the last conv layer
norm_layer (torch.nn.Module): normalization layer
use_dropout (bool): if use dropout layers
n_blocks (int): the number of ResNet blocks
padding_type (str): the name of padding layer in conv layers: reflect | replicate | zero
"""
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling): # add downsampling layers
mult = 2 ** i
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True)]
mult = 2 ** n_downsampling
for i in range(n_blocks): # add ResNet blocks
model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
for i in range(n_downsampling): # add upsampling layers
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
kernel_size=3, stride=2,
padding=1, output_padding=1,
bias=use_bias),
norm_layer(int(ngf * mult / 2)),
nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, input):
"""Standard forward"""
return self.model(input)
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
Args:
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(torch.nn.Module): 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 i 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.
Args:
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 (torch.nn.Module): 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)
[docs]def resnet_9(ngf, input_nc=3, output_nc=3, norm='batch', use_dropout=False,
init_type='normal', init_gain=0.02):
"""
Resnet-based generator with 9 Resnet blocks.
Args:
ngf (int): the number of filters in the last conv layer
input_nc (int): the number of channels in input images. Default: 3
output_nc (int): the number of channels in output images. Default: 3
norm (str): the type of normalization layers used in the network. Default: 'batch'
use_dropout (bool): whether use dropout. Default: False
init_type (str): the name of the initialization method. Choices includes: ``normal`` |
``xavier`` | ``kaiming`` | ``orthogonal``. Default: 'normal'
init_gain (float): scaling factor for normal, xavier and orthogonal. Default: 0.02
"""
norm_layer = get_norm_layer(norm_type=norm)
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
init_weights(net, init_type, init_gain)
return net
[docs]def resnet_6(ngf, input_nc=3, output_nc=3, norm='batch', use_dropout=False,
init_type='normal', init_gain=0.02):
"""
Resnet-based generator with 6 Resnet blocks.
Args:
ngf (int): the number of filters in the last conv layer
input_nc (int): the number of channels in input images. Default: 3
output_nc (int): the number of channels in output images. Default: 3
norm (str): the type of normalization layers used in the network. Default: 'batch'
use_dropout (bool): whether use dropout. Default: False
init_type (str): the name of the initialization method. Choices includes: ``normal`` |
``xavier`` | ``kaiming`` | ``orthogonal``. Default: 'normal'
init_gain (float): scaling factor for normal, xavier and orthogonal. Default: 0.02
"""
norm_layer = get_norm_layer(norm_type=norm)
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
init_weights(net, init_type, init_gain)
return net
[docs]def unet_256(ngf, input_nc=3, output_nc=3, norm='batch', use_dropout=False,
init_type='normal', init_gain=0.02):
"""
`U-Net <https://arxiv.org/abs/1505.04597>`_ generator for 256x256 input images.
The size of the input image should be a multiple of 256.
Args:
ngf (int): the number of filters in the last conv layer
input_nc (int): the number of channels in input images. Default: 3
output_nc (int): the number of channels in output images. Default: 3
norm (str): the type of normalization layers used in the network. Default: 'batch'
use_dropout (bool): whether use dropout. Default: False
init_type (str): the name of the initialization method. Choices includes: ``normal`` |
``xavier`` | ``kaiming`` | ``orthogonal``. Default: 'normal'
init_gain (float): scaling factor for normal, xavier and orthogonal. Default: 0.02
"""
norm_layer = get_norm_layer(norm_type=norm)
net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
init_weights(net, init_type, init_gain)
return net
[docs]def unet_128(ngf, input_nc=3, output_nc=3, norm='batch', use_dropout=False,
init_type='normal', init_gain=0.02):
"""
`U-Net <https://arxiv.org/abs/1505.04597>`_ generator for 128x128 input images.
The size of the input image should be a multiple of 128.
Args:
ngf (int): the number of filters in the last conv layer
input_nc (int): the number of channels in input images. Default: 3
output_nc (int): the number of channels in output images. Default: 3
norm (str): the type of normalization layers used in the network. Default: 'batch'
use_dropout (bool): whether use dropout. Default: False
init_type (str): the name of the initialization method. Choices includes: ``normal`` |
``xavier`` | ``kaiming`` | ``orthogonal``. Default: 'normal'
init_gain (float): scaling factor for normal, xavier and orthogonal. Default: 0.02
"""
norm_layer = get_norm_layer(norm_type=norm)
net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
init_weights(net, init_type, init_gain)
return net
def unet_32(ngf, input_nc=3, output_nc=3, norm='batch', use_dropout=False,
init_type='normal', init_gain=0.02):
"""
`U-Net <https://arxiv.org/abs/1505.04597>`_ generator for 32x32 input images
Args:
ngf (int): the number of filters in the last conv layer
input_nc (int): the number of channels in input images. Default: 3
output_nc (int): the number of channels in output images. Default: 3
norm (str): the type of normalization layers used in the network. Default: 'batch'
use_dropout (bool): whether use dropout. Default: False
init_type (str): the name of the initialization method. Choices includes: ``normal`` |
``xavier`` | ``kaiming`` | ``orthogonal``. Default: 'normal'
init_gain (float): scaling factor for normal, xavier and orthogonal. Default: 0.02
"""
norm_layer = get_norm_layer(norm_type=norm)
net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
init_weights(net, init_type, init_gain)
return net
