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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import numpy as np
from .stylegan_networks import StyleGAN2Discriminator, StyleGAN2Generator, TileStyleGAN2Discriminator
###############################################################################
# Helper Functions
###############################################################################
def get_filter(filt_size=3):
if(filt_size == 1):
a = np.array([1., ])
elif(filt_size == 2):
a = np.array([1., 1.])
elif(filt_size == 3):
a = np.array([1., 2., 1.])
elif(filt_size == 4):
a = np.array([1., 3., 3., 1.])
elif(filt_size == 5):
a = np.array([1., 4., 6., 4., 1.])
elif(filt_size == 6):
a = np.array([1., 5., 10., 10., 5., 1.])
elif(filt_size == 7):
a = np.array([1., 6., 15., 20., 15., 6., 1.])
filt = torch.Tensor(a[:, None] * a[None, :])
filt = filt / torch.sum(filt)
return filt
class Downsample(nn.Module):
def __init__(self, channels, pad_type='reflect', filt_size=3, stride=2, pad_off=0):
super(Downsample, self).__init__()
self.filt_size = filt_size
self.pad_off = pad_off
self.pad_sizes = [int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2)), int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2))]
self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
self.stride = stride
self.off = int((self.stride - 1) / 2.)
self.channels = channels
filt = get_filter(filt_size=self.filt_size)
self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
self.pad = get_pad_layer(pad_type)(self.pad_sizes)
def forward(self, inp):
if(self.filt_size == 1):
if(self.pad_off == 0):
return inp[:, :, ::self.stride, ::self.stride]
else:
return self.pad(inp)[:, :, ::self.stride, ::self.stride]
else:
return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
class Upsample2(nn.Module):
def __init__(self, scale_factor, mode='nearest'):
super().__init__()
self.factor = scale_factor
self.mode = mode
def forward(self, x):
return torch.nn.functional.interpolate(x, scale_factor=self.factor, mode=self.mode)
class Upsample(nn.Module):
def __init__(self, channels, pad_type='repl', filt_size=4, stride=2):
super(Upsample, self).__init__()
self.filt_size = filt_size
self.filt_odd = np.mod(filt_size, 2) == 1
self.pad_size = int((filt_size - 1) / 2)
self.stride = stride
self.off = int((self.stride - 1) / 2.)
self.channels = channels
filt = get_filter(filt_size=self.filt_size) * (stride**2)
self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
self.pad = get_pad_layer(pad_type)([1, 1, 1, 1])
def forward(self, inp):
ret_val = F.conv_transpose2d(self.pad(inp), self.filt, stride=self.stride, padding=1 + self.pad_size, groups=inp.shape[1])[:, :, 1:, 1:]
if(self.filt_odd):
return ret_val
else:
return ret_val[:, :, :-1, :-1]
def get_pad_layer(pad_type):
if(pad_type in ['refl', 'reflect']):
PadLayer = nn.ReflectionPad2d
elif(pad_type in ['repl', 'replicate']):
PadLayer = nn.ReplicationPad2d
elif(pad_type == 'zero'):
PadLayer = nn.ZeroPad2d
else:
print('Pad type [%s] not recognized' % pad_type)
return PadLayer
class Identity(nn.Module):
def forward(self, x):
return x
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
def norm_layer(x):
return Identity()
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
def get_scheduler(optimizer, opt):
"""Return a learning rate scheduler
Parameters:
optimizer -- the optimizer of the network
opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．
opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
See https://pytorch.org/docs/stable/optim.html for more details.
"""
if opt.lr_policy == 'linear':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
elif opt.lr_policy == 'plateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
elif opt.lr_policy == 'cosine':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
else:
return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
return scheduler
def init_weights(net, init_type='normal', init_gain=0.02, debug=False):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if debug:
print(classname)
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
net.apply(init_func) # apply the initialization function <init_func>
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=False, initialize_weights=True):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert(torch.cuda.is_available())
net.to(gpu_ids[0])
# if not amp:
# net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs for non-AMP training
if initialize_weights:
init_weights(net, init_type, init_gain=init_gain, debug=debug)
return net
def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal',
init_gain=0.02, no_antialias=False, no_antialias_up=False, gpu_ids=[], opt=None):
"""Create a 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
netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
norm (str) -- the name of normalization layers used in the network: batch | instance | none
use_dropout (bool) -- if use dropout layers.
init_type (str) -- the name of our initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a generator
Our current implementation provides two types of generators:
U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
The original U-Net paper: https://arxiv.org/abs/1505.04597
Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
The generator has been initialized by <init_net>. It uses RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'resnet_9blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=9, opt=opt)
elif netG == 'resnet_6blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=6, opt=opt)
elif netG == 'resnet_4blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=4, opt=opt)
elif netG == 'unet_128':
net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'unet_256':
net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'stylegan2':
net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, opt=opt)
elif netG == 'smallstylegan2':
net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, n_blocks=2, opt=opt)
elif netG == 'resnet_cat':
n_blocks = 8
net = G_Resnet(input_nc, output_nc, opt.nz, num_downs=2, n_res=n_blocks - 4, ngf=ngf, norm='inst', nl_layer='relu')
else:
raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
return init_net(net, init_type, init_gain, gpu_ids, initialize_weights=('stylegan2' not in netG))
def define_F(input_nc, netF, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, no_antialias=False, gpu_ids=[], opt=None):
if netF == 'global_pool':
net = PoolingF()
elif netF == 'reshape':
net = ReshapeF()
elif netF == 'sample':
net = PatchSampleF(use_mlp=False, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
elif netF == 'mlp_sample':
net = PatchSampleF(use_mlp=True, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
elif netF == 'strided_conv':
net = StridedConvF(init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids)
else:
raise NotImplementedError('projection model name [%s] is not recognized' % netF)
return init_net(net, init_type, init_gain, gpu_ids)
def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, no_antialias=False, gpu_ids=[], opt=None):
"""Create a discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the first conv layer
netD (str) -- the architecture's name: basic | n_layers | pixel
n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
norm (str) -- the type of normalization layers used in the network.
init_type (str) -- the name of the initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a discriminator
Our current implementation provides three types of discriminators:
[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
It can classify whether 70×70 overlapping patches are real or fake.
Such a patch-level discriminator architecture has fewer parameters
than a full-image discriminator and can work on arbitrarily-sized images
in a fully convolutional fashion.
[n_layers]: With this mode, you cna specify the number of conv layers in the discriminator
with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
It encourages greater color diversity but has no effect on spatial statistics.
The discriminator has been initialized by <init_net>. It uses Leaky RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == 'basic': # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, no_antialias=no_antialias,)
elif netD == 'n_layers': # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, no_antialias=no_antialias,)
elif netD == 'pixel': # classify if each pixel is real or fake
net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
elif 'stylegan2' in netD:
net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
else:
raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
return init_net(net, init_type, init_gain, gpu_ids,
initialize_weights=('stylegan2' not in netD))
##############################################################################
# Classes
##############################################################################
class GANLoss(nn.Module):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp', 'nonsaturating']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)
def get_target_tensor(self, prediction, target_is_real):
"""Create label tensors with the same size as the input.
Parameters:
prediction (tensor) - - tpyically the prediction from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
A label tensor filled with ground truth label, and with the size of the input
"""
if target_is_real:
target_tensor = self.real_label
else:
target_tensor = self.fake_label
return target_tensor.expand_as(prediction)
def __call__(self, prediction, target_is_real):
"""Calculate loss given Discriminator's output and grount truth labels.
Parameters:
prediction (tensor) - - tpyically the prediction output from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
the calculated loss.
"""
bs = prediction.size(0)
if self.gan_mode in ['lsgan', 'vanilla']:
target_tensor = self.get_target_tensor(prediction, target_is_real)
loss = self.loss(prediction, target_tensor)
elif self.gan_mode == 'wgangp':
if target_is_real:
loss = -prediction.mean()
else:
loss = prediction.mean()
elif self.gan_mode == 'nonsaturating':
if target_is_real:
loss = F.softplus(-prediction).view(bs, -1).mean(dim=1)
else:
loss = F.softplus(prediction).view(bs, -1).mean(dim=1)
return loss
def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
Arguments:
netD (network) -- discriminator network
real_data (tensor array) -- real images
fake_data (tensor array) -- generated images from the generator
device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
type (str) -- if we mix real and fake data or not [real | fake | mixed].
constant (float) -- the constant used in formula ( | |gradient||_2 - constant)^2
lambda_gp (float) -- weight for this loss
Returns the gradient penalty loss
"""
if lambda_gp > 0.0:
if type == 'real': # either use real images, fake images, or a linear interpolation of two.
interpolatesv = real_data
elif type == 'fake':
interpolatesv = fake_data
elif type == 'mixed':
alpha = torch.rand(real_data.shape[0], 1, device=device)
alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
else:
raise NotImplementedError('{} not implemented'.format(type))
interpolatesv.requires_grad_(True)
disc_interpolates = netD(interpolatesv)
gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
grad_outputs=torch.ones(disc_interpolates.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
return gradient_penalty, gradients
else:
return 0.0, None
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm + 1e-7)
return out
class PoolingF(nn.Module):
def __init__(self):
super(PoolingF, self).__init__()
model = [nn.AdaptiveMaxPool2d(1)]
self.model = nn.Sequential(*model)
self.l2norm = Normalize(2)
def forward(self, x):
return self.l2norm(self.model(x))
class ReshapeF(nn.Module):
def __init__(self):
super(ReshapeF, self).__init__()
model = [nn.AdaptiveAvgPool2d(4)]
self.model = nn.Sequential(*model)
self.l2norm = Normalize(2)
def forward(self, x):
x = self.model(x)
x_reshape = x.permute(0, 2, 3, 1).flatten(0, 2)
return self.l2norm(x_reshape)
class StridedConvF(nn.Module):
def __init__(self, init_type='normal', init_gain=0.02, gpu_ids=[]):
super().__init__()
# self.conv1 = nn.Conv2d(256, 128, 3, stride=2)
# self.conv2 = nn.Conv2d(128, 64, 3, stride=1)
self.l2_norm = Normalize(2)
self.mlps = {}
self.moving_averages = {}
self.init_type = init_type
self.init_gain = init_gain
self.gpu_ids = gpu_ids
def create_mlp(self, x):
C, H = x.shape[1], x.shape[2]
n_down = int(np.rint(np.log2(H / 32)))
mlp = []
for i in range(n_down):
mlp.append(nn.Conv2d(C, max(C // 2, 64), 3, stride=2))
mlp.append(nn.ReLU())
C = max(C // 2, 64)
mlp.append(nn.Conv2d(C, 64, 3))
mlp = nn.Sequential(*mlp)
init_net(mlp, self.init_type, self.init_gain, self.gpu_ids)
return mlp
def update_moving_average(self, key, x):
if key not in self.moving_averages:
self.moving_averages[key] = x.detach()
self.moving_averages[key] = self.moving_averages[key] * 0.999 + x.detach() * 0.001
def forward(self, x, use_instance_norm=False):
C, H = x.shape[1], x.shape[2]
key = '%d_%d' % (C, H)
if key not in self.mlps:
self.mlps[key] = self.create_mlp(x)
self.add_module("child_%s" % key, self.mlps[key])
mlp = self.mlps[key]
x = mlp(x)
self.update_moving_average(key, x)
x = x - self.moving_averages[key]
if use_instance_norm:
x = F.instance_norm(x)
return self.l2_norm(x)
class PatchSampleF(nn.Module):
def __init__(self, use_mlp=False, init_type='normal', init_gain=0.02, nc=256, gpu_ids=[]):
# potential issues: currently, we use the same patch_ids for multiple images in the batch
super(PatchSampleF, self).__init__()
self.l2norm = Normalize(2)
self.use_mlp = use_mlp
self.nc = nc # hard-coded
self.mlp_init = False
self.init_type = init_type
self.init_gain = init_gain
self.gpu_ids = gpu_ids
def create_mlp(self, feats):
for mlp_id, feat in enumerate(feats):
input_nc = feat.shape[1]
mlp = nn.Sequential(*[nn.Linear(input_nc, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)])
if len(self.gpu_ids) > 0:
mlp.cuda()
setattr(self, 'mlp_%d' % mlp_id, mlp)
init_net(self, self.init_type, self.init_gain, self.gpu_ids)
self.mlp_init = True
def forward(self, feats, num_patches=64, patch_ids=None):
return_ids = []
return_feats = []
if self.use_mlp and not self.mlp_init:
self.create_mlp(feats)
for feat_id, feat in enumerate(feats):
B, H, W = feat.shape[0], feat.shape[2], feat.shape[3]
feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2)
if num_patches > 0:
if patch_ids is not None:
patch_id = patch_ids[feat_id]
else:
# torch.randperm produces cudaErrorIllegalAddress for newer versions of PyTorch. https://github.com/taesungp/contrastive-unpaired-translation/issues/83
#patch_id = torch.randperm(feat_reshape.shape[1], device=feats[0].device)
patch_id = np.random.permutation(feat_reshape.shape[1])
patch_id = patch_id[:int(min(num_patches, patch_id.shape[0]))] # .to(patch_ids.device)
patch_id = torch.tensor(patch_id, dtype=torch.long, device=feat.device)
x_sample = feat_reshape[:, patch_id, :].flatten(0, 1) # reshape(-1, x.shape[1])
else:
x_sample = feat_reshape
patch_id = []
if self.use_mlp:
mlp = getattr(self, 'mlp_%d' % feat_id)
x_sample = mlp(x_sample)
return_ids.append(patch_id)
x_sample = self.l2norm(x_sample)
if num_patches == 0:
x_sample = x_sample.permute(0, 2, 1).reshape([B, x_sample.shape[-1], H, W])
return_feats.append(x_sample)
return return_feats, return_ids
class G_Resnet(nn.Module):
def __init__(self, input_nc, output_nc, nz, num_downs, n_res, ngf=64,
norm=None, nl_layer=None):
super(G_Resnet, self).__init__()
n_downsample = num_downs
pad_type = 'reflect'
self.enc_content = ContentEncoder(n_downsample, n_res, input_nc, ngf, norm, nl_layer, pad_type=pad_type)
if nz == 0:
self.dec = Decoder(n_downsample, n_res, self.enc_content.output_dim, output_nc, norm=norm, activ=nl_layer, pad_type=pad_type, nz=nz)
else:
self.dec = Decoder_all(n_downsample, n_res, self.enc_content.output_dim, output_nc, norm=norm, activ=nl_layer, pad_type=pad_type, nz=nz)
def decode(self, content, style=None):
return self.dec(content, style)
def forward(self, image, style=None, nce_layers=[], encode_only=False):
content, feats = self.enc_content(image, nce_layers=nce_layers, encode_only=encode_only)
if encode_only:
return feats
else:
images_recon = self.decode(content, style)
if len(nce_layers) > 0:
return images_recon, feats
else:
return images_recon
##################################################################################
# Encoder and Decoders
##################################################################################
class E_adaIN(nn.Module):
def __init__(self, input_nc, output_nc=1, nef=64, n_layers=4,
norm=None, nl_layer=None, vae=False):
# style encoder
super(E_adaIN, self).__init__()
self.enc_style = StyleEncoder(n_layers, input_nc, nef, output_nc, norm='none', activ='relu', vae=vae)
def forward(self, image):
style = self.enc_style(image)
return style
class StyleEncoder(nn.Module):
def __init__(self, n_downsample, input_dim, dim, style_dim, norm, activ, vae=False):
super(StyleEncoder, self).__init__()
self.vae = vae
self.model = []
self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type='reflect')]
for i in range(2):
self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
dim *= 2
for i in range(n_downsample - 2):
self.model += [Conv2dBlock(dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
self.model += [nn.AdaptiveAvgPool2d(1)] # global average pooling
if self.vae:
self.fc_mean = nn.Linear(dim, style_dim) # , 1, 1, 0)
self.fc_var = nn.Linear(dim, style_dim) # , 1, 1, 0)
else:
self.model += [nn.Conv2d(dim, style_dim, 1, 1, 0)]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def forward(self, x):
if self.vae:
output = self.model(x)
output = output.view(x.size(0), -1)
output_mean = self.fc_mean(output)
output_var = self.fc_var(output)
return output_mean, output_var
else:
return self.model(x).view(x.size(0), -1)
class ContentEncoder(nn.Module):
def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, pad_type='zero'):
super(ContentEncoder, self).__init__()
self.model = []
self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type='reflect')]
# downsampling blocks
for i in range(n_downsample):
self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
dim *= 2
# residual blocks
self.model += [ResBlocks(n_res, dim, norm=norm, activation=activ, pad_type=pad_type)]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def forward(self, x, nce_layers=[], encode_only=False):
if len(nce_layers) > 0:
feat = x
feats = []
for layer_id, layer in enumerate(self.model):
feat = layer(feat)
if layer_id in nce_layers:
feats.append(feat)
if layer_id == nce_layers[-1] and encode_only:
return None, feats
return feat, feats
else:
return self.model(x), None
for layer_id, layer in enumerate(self.model):
print(layer_id, layer)
class Decoder_all(nn.Module):
def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0):
super(Decoder_all, self).__init__()
# AdaIN residual blocks
self.resnet_block = ResBlocks(n_res, dim, norm, activ, pad_type=pad_type, nz=nz)
self.n_blocks = 0
# upsampling blocks
for i in range(n_upsample):
block = [Upsample2(scale_factor=2), Conv2dBlock(dim + nz, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type='reflect')]
setattr(self, 'block_{:d}'.format(self.n_blocks), nn.Sequential(*block))
self.n_blocks += 1
dim //= 2
# use reflection padding in the last conv layer
setattr(self, 'block_{:d}'.format(self.n_blocks), Conv2dBlock(dim + nz, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type='reflect'))
self.n_blocks += 1
def forward(self, x, y=None):
if y is not None:
output = self.resnet_block(cat_feature(x, y))
for n in range(self.n_blocks):
block = getattr(self, 'block_{:d}'.format(n))
if n > 0:
output = block(cat_feature(output, y))
else:
output = block(output)
return output
class Decoder(nn.Module):
def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0):
super(Decoder, self).__init__()
self.model = []
# AdaIN residual blocks
self.model += [ResBlocks(n_res, dim, norm, activ, pad_type=pad_type, nz=nz)]
# upsampling blocks
for i in range(n_upsample):
if i == 0:
input_dim = dim + nz
else:
input_dim = dim
self.model += [Upsample2(scale_factor=2), Conv2dBlock(input_dim, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type='reflect')]
dim //= 2
# use reflection padding in the last conv layer
self.model += [Conv2dBlock(dim, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type='reflect')]
self.model = nn.Sequential(*self.model)
def forward(self, x, y=None):
if y is not None:
return self.model(cat_feature(x, y))
else:
return self.model(x)
##################################################################################
# Sequential Models
##################################################################################
class ResBlocks(nn.Module):
def __init__(self, num_blocks, dim, norm='inst', activation='relu', pad_type='zero', nz=0):
super(ResBlocks, self).__init__()
self.model = []
for i in range(num_blocks):
self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type, nz=nz)]
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x)
##################################################################################
# Basic Blocks
##################################################################################
def cat_feature(x, y):
y_expand = y.view(y.size(0), y.size(1), 1, 1).expand(
y.size(0), y.size(1), x.size(2), x.size(3))
x_cat = torch.cat([x, y_expand], 1)
return x_cat
class ResBlock(nn.Module):
def __init__(self, dim, norm='inst', activation='relu', pad_type='zero', nz=0):
super(ResBlock, self).__init__()
model = []
model += [Conv2dBlock(dim + nz, dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
model += [Conv2dBlock(dim, dim + nz, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]
self.model = nn.Sequential(*model)
def forward(self, x):
residual = x
out = self.model(x)
out += residual
return out
class Conv2dBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride,
padding=0, norm='none', activation='relu', pad_type='zero'):
super(Conv2dBlock, self).__init__()
self.use_bias = True
# initialize padding
if pad_type == 'reflect':
self.pad = nn.ReflectionPad2d(padding)
elif pad_type == 'zero':
self.pad = nn.ZeroPad2d(padding)
else:
assert 0, "Unsupported padding type: {}".format(pad_type)
# initialize normalization
norm_dim = output_dim
if norm == 'batch':
self.norm = nn.BatchNorm2d(norm_dim)
elif norm == 'inst':
self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=False)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
# initialize convolution
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)
def forward(self, x):
x = self.conv(self.pad(x))
if self.norm:
x = self.norm(x)
if self.activation:
x = self.activation(x)
return x
class LinearBlock(nn.Module):
def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
super(LinearBlock, self).__init__()
use_bias = True
# initialize fully connected layer
self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
# initialize normalization
norm_dim = output_dim
if norm == 'batch':
self.norm = nn.BatchNorm1d(norm_dim)
elif norm == 'inst':
self.norm = nn.InstanceNorm1d(norm_dim)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
def forward(self, x):
out = self.fc(x)
if self.norm:
out = self.norm(out)
if self.activation:
out = self.activation(out)
return out
##################################################################################
# Normalization layers
##################################################################################
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
self.beta = nn.Parameter(torch.zeros(num_features))
def forward(self, x):
shape = [-1] + [1] * (x.dim() - 1)
mean = x.view(x.size(0), -1).mean(1).view(*shape)
std = x.view(x.size(0), -1).std(1).view(*shape)
x = (x - mean) / (std + self.eps)
if self.affine:
shape = [1, -1] + [1] * (x.dim() - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
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', no_antialias=False, no_antialias_up=False, opt=None):
"""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__()
self.opt = opt
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
if(no_antialias):
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)]
else:
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True),
Downsample(ngf * mult * 2)]
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)
if no_antialias_up:
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)]
else:
model += [Upsample(ngf * mult),
nn.Conv2d(ngf * mult, int(ngf * mult / 2),
kernel_size=3, stride=1,
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, layers=[], encode_only=False):
if -1 in layers:
layers.append(len(self.model))
if len(layers) > 0:
feat = input
feats = []
for layer_id, layer in enumerate(self.model):
# print(layer_id, layer)
feat = layer(feat)
if layer_id in layers:
# print("%d: adding the output of %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
feats.append(feat)
else:
# print("%d: skipping %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
pass
if layer_id == layers[-1] and encode_only:
# print('encoder only return features')
return feats # return intermediate features alone; stop in the last layers
return feat, feats # return both output and intermediate features
else:
"""Standard forward"""
fake = self.model(input)
return fake
class ResnetDecoder(nn.Module):
"""Resnet-based decoder that consists of a few Resnet blocks + a few upsampling operations.
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False):
"""Construct a Resnet-based decoder
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(ResnetDecoder, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = []
n_downsampling = 2
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)
if(no_antialias):
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)]
else:
model += [Upsample(ngf * mult),
nn.Conv2d(ngf * mult, int(ngf * mult / 2),
kernel_size=3, stride=1,
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 ResnetEncoder(nn.Module):
"""Resnet-based encoder that consists of a few downsampling + several Resnet blocks
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False):
"""Construct a Resnet-based encoder
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(ResnetEncoder, 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
if(no_antialias):
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)]
else:
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True),
Downsample(ngf * mult * 2)]
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)]
self.model = nn.Sequential(*model)
def forward(self, input):
"""Standard forward"""
return self.model(input)
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.
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), 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 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 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.
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 NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, no_antialias=False):
"""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__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
if(no_antialias):
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
else:
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.LeakyReLU(0.2, True), Downsample(ndf)]
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)
if(no_antialias):
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
else:
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
Downsample(ndf * nf_mult)]
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),
norm_layer(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 forward(self, input):
"""Standard forward."""
return self.model(input)
class PixelDiscriminator(nn.Module):
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
"""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__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
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),
norm_layer(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 forward(self, input):
"""Standard forward."""
return self.net(input)
class PatchDiscriminator(NLayerDiscriminator):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, no_antialias=False):
super().__init__(input_nc, ndf, 2, norm_layer, no_antialias)
def forward(self, input):
B, C, H, W = input.size(0), input.size(1), input.size(2), input.size(3)
size = 16
Y = H // size
X = W // size
input = input.view(B, C, Y, size, X, size)
input = input.permute(0, 2, 4, 1, 3, 5).contiguous().view(B * Y * X, C, size, size)
return super().forward(input)
class GroupedChannelNorm(nn.Module):
def __init__(self, num_groups):
super().__init__()
self.num_groups = num_groups
def forward(self, x):
shape = list(x.shape)
new_shape = [shape[0], self.num_groups, shape[1] // self.num_groups] + shape[2:]
x = x.view(*new_shape)
mean = x.mean(dim=2, keepdim=True)
std = x.std(dim=2, keepdim=True)
x_norm = (x - mean) / (std + 1e-7)
return x_norm.view(*shape)
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