def __init__(self, input_num, num1, num2, dilation_rate, drop_out,
norm_type):
super(_DenseAsppBlock, self).__init__()
self.add_module(
'conv1',
nn.Conv2d(in_channels=input_num, out_channels=num1,
kernel_size=1)),
self.add_module(
'norm1',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_features=num1)),
self.add_module('relu1', nn.ReLU(inplace=False)),
self.add_module(
'conv2',
nn.Conv2d(in_channels=num1,
out_channels=num2,
kernel_size=3,
dilation=dilation_rate,
padding=dilation_rate)),
self.add_module(
'norm2',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
num_features=input_num)),
self.add_module('relu2', nn.ReLU(inplace=False)),
self.drop_rate = drop_out
def __init__(self, input_nc, ndf=64, n_layers=3, norm_type=None):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
use_bias = (norm_type == 'instancenorm')
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def __init__(self,
low_in_channels,
high_in_channels,
out_channels,
key_channels,
value_channels,
dropout,
sizes=([1]),
norm_type=None,
psp_size=(1, 3, 6, 8)):
super(AFNB, self).__init__()
self.stages = []
self.norm_type = norm_type
self.psp_size = psp_size
self.stages = nn.ModuleList([
self._make_stage([low_in_channels, high_in_channels], out_channels,
key_channels, value_channels, size)
for size in sizes
])
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(out_channels + high_in_channels,
out_channels,
kernel_size=1,
padding=0),
ModuleHelper.BatchNorm2d(norm_type=self.norm_type)(out_channels),
nn.Dropout2d(dropout))
def build_conv_block(self, dim, padding_type, norm_type, use_dropout,
use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- 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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(dim)
]
return nn.Sequential(*conv_block)
def __init__(self, num_input_features, num_output_features, norm_type):
super(_Transition, self).__init__()
self.add_module(
'conv',
nn.Conv2d(num_input_features,
num_output_features,
kernel_size=1,
stride=1,
bias=False))
self.add_module(
'norm',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
num_features=num_output_features)),
self.add_module('relu', nn.ReLU(inplace=False))
def __init__(self, input_nc, ndf=64, norm_type=None):
"""Construct a 1x1 PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
"""
super(PixelDiscriminator, self).__init__()
use_bias = (norm_type == 'instancenorm')
self.net = [
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(ndf * 2),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
self.net = nn.Sequential(*self.net)
def __init__(self,
outer_nc,
inner_nc,
input_nc=None,
submodule=None,
outermost=False,
innermost=False,
norm_type=None,
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
user_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
use_bias = (norm_type == 'instancenorm')
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 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(inner_nc)
uprelu = nn.ReLU(True)
upnorm = ModuleHelper.BatchNorm2d(norm_type=norm_type)(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 __init__(self,
input_nc,
output_nc,
ngf=64,
norm_type=None,
use_dropout=False,
n_blocks=6,
padding_type='reflect'):
"""Construct a Resnet-based generator
Parameters:
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 -- 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__()
use_bias = (norm_type == 'instancenorm')
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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_type=norm_type,
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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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 _make_stage(self, features, out_features, size, norm_type):
prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False)
bn = ModuleHelper.BatchNorm2d(norm_type=norm_type)(out_features)
return nn.Sequential(prior, conv, bn)