617 lines
28 KiB
Python
617 lines
28 KiB
Python
import torch
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import torch.nn as nn
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from torch.nn import init
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import functools
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from torch.optim import lr_scheduler
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###############################################################################
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# Helper Functions
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###############################################################################
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class Identity(nn.Module):
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def forward(self, x):
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return x
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def get_norm_layer(norm_type='instance'):
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"""Return a normalization layer
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Parameters:
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norm_type (str) -- the name of the normalization layer: batch | instance | none
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For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
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For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
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"""
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if norm_type == 'batch':
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norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
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elif norm_type == 'instance':
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norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
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elif norm_type == 'none':
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def norm_layer(x):
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return Identity()
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else:
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raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
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return norm_layer
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def get_scheduler(optimizer, opt):
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"""Return a learning rate scheduler
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Parameters:
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optimizer -- the optimizer of the network
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opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．
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opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
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For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
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and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
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For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
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See https://pytorch.org/docs/stable/optim.html for more details.
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"""
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if opt.lr_policy == 'linear':
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def lambda_rule(epoch):
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lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
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return lr_l
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
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elif opt.lr_policy == 'step':
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scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
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elif opt.lr_policy == 'plateau':
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scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
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elif opt.lr_policy == 'cosine':
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scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
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else:
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return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
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return scheduler
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def init_weights(net, init_type='normal', init_gain=0.02):
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"""Initialize network weights.
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Parameters:
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net (network) -- network to be initialized
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init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
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init_gain (float) -- scaling factor for normal, xavier and orthogonal.
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We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
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work better for some applications. Feel free to try yourself.
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"""
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def init_func(m): # define the initialization function
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classname = m.__class__.__name__
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if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
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if init_type == 'normal':
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init.normal_(m.weight.data, 0.0, init_gain)
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elif init_type == 'xavier':
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init.xavier_normal_(m.weight.data, gain=init_gain)
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elif init_type == 'kaiming':
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init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
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elif init_type == 'orthogonal':
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init.orthogonal_(m.weight.data, gain=init_gain)
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else:
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raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
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if hasattr(m, 'bias') and m.bias is not None:
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init.constant_(m.bias.data, 0.0)
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elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
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init.normal_(m.weight.data, 1.0, init_gain)
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init.constant_(m.bias.data, 0.0)
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print('initialize network with %s' % init_type)
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net.apply(init_func) # apply the initialization function <init_func>
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def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
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"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
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Parameters:
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net (network) -- the network to be initialized
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init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
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gain (float) -- scaling factor for normal, xavier and orthogonal.
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gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
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Return an initialized network.
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"""
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if len(gpu_ids) > 0:
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assert(torch.cuda.is_available())
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net.to(gpu_ids[0])
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net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
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init_weights(net, init_type, init_gain=init_gain)
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return net
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def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
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"""Create a generator
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Parameters:
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input_nc (int) -- the number of channels in input images
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output_nc (int) -- the number of channels in output images
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ngf (int) -- the number of filters in the last conv layer
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netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
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norm (str) -- the name of normalization layers used in the network: batch | instance | none
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use_dropout (bool) -- if use dropout layers.
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init_type (str) -- the name of our initialization method.
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init_gain (float) -- scaling factor for normal, xavier and orthogonal.
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gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
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Returns a generator
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Our current implementation provides two types of generators:
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U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
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The original U-Net paper: https://arxiv.org/abs/1505.04597
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Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
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Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
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We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
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The generator has been initialized by <init_net>. It uses RELU for non-linearity.
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"""
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net = None
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norm_layer = get_norm_layer(norm_type=norm)
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if netG == 'resnet_9blocks':
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net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
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elif netG == 'resnet_6blocks':
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net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
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elif netG == 'unet_128':
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net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
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elif netG == 'unet_256':
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net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
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else:
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raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
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return init_net(net, init_type, init_gain, gpu_ids)
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def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
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"""Create a discriminator
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Parameters:
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input_nc (int) -- the number of channels in input images
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ndf (int) -- the number of filters in the first conv layer
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netD (str) -- the architecture's name: basic | n_layers | pixel
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n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
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norm (str) -- the type of normalization layers used in the network.
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init_type (str) -- the name of the initialization method.
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init_gain (float) -- scaling factor for normal, xavier and orthogonal.
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gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
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Returns a discriminator
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Our current implementation provides three types of discriminators:
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[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
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It can classify whether 70×70 overlapping patches are real or fake.
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Such a patch-level discriminator architecture has fewer parameters
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than a full-image discriminator and can work on arbitrarily-sized images
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in a fully convolutional fashion.
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[n_layers]: With this mode, you can specify the number of conv layers in the discriminator
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with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
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[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
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It encourages greater color diversity but has no effect on spatial statistics.
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The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
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"""
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net = None
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norm_layer = get_norm_layer(norm_type=norm)
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if netD == 'basic': # default PatchGAN classifier
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net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
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elif netD == 'n_layers': # more options
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net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
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elif netD == 'pixel': # classify if each pixel is real or fake
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net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
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else:
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raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
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return init_net(net, init_type, init_gain, gpu_ids)
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##############################################################################
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# Classes
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##############################################################################
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class GANLoss(nn.Module):
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"""Define different GAN objectives.
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The GANLoss class abstracts away the need to create the target label tensor
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that has the same size as the input.
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"""
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def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
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""" Initialize the GANLoss class.
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Parameters:
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gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
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target_real_label (bool) - - label for a real image
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target_fake_label (bool) - - label of a fake image
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Note: Do not use sigmoid as the last layer of Discriminator.
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LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
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"""
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super(GANLoss, self).__init__()
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self.register_buffer('real_label', torch.tensor(target_real_label))
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self.register_buffer('fake_label', torch.tensor(target_fake_label))
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self.gan_mode = gan_mode
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if gan_mode == 'lsgan':
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self.loss = nn.MSELoss()
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elif gan_mode == 'vanilla':
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self.loss = nn.BCEWithLogitsLoss()
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elif gan_mode in ['wgangp']:
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self.loss = None
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else:
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raise NotImplementedError('gan mode %s not implemented' % gan_mode)
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def get_target_tensor(self, prediction, target_is_real):
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"""Create label tensors with the same size as the input.
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Parameters:
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prediction (tensor) - - tpyically the prediction from a discriminator
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target_is_real (bool) - - if the ground truth label is for real images or fake images
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Returns:
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A label tensor filled with ground truth label, and with the size of the input
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"""
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if target_is_real:
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target_tensor = self.real_label
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else:
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target_tensor = self.fake_label
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return target_tensor.expand_as(prediction)
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def __call__(self, prediction, target_is_real):
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"""Calculate loss given Discriminator's output and grount truth labels.
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Parameters:
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prediction (tensor) - - tpyically the prediction output from a discriminator
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target_is_real (bool) - - if the ground truth label is for real images or fake images
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Returns:
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the calculated loss.
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"""
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if self.gan_mode in ['lsgan', 'vanilla']:
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target_tensor = self.get_target_tensor(prediction, target_is_real)
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loss = self.loss(prediction, target_tensor)
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elif self.gan_mode == 'wgangp':
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if target_is_real:
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loss = -prediction.mean()
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else:
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loss = prediction.mean()
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return loss
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def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
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"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
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Arguments:
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netD (network) -- discriminator network
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real_data (tensor array) -- real images
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fake_data (tensor array) -- generated images from the generator
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device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
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type (str) -- if we mix real and fake data or not [real | fake | mixed].
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constant (float) -- the constant used in formula ( ||gradient||_2 - constant)^2
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lambda_gp (float) -- weight for this loss
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Returns the gradient penalty loss
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"""
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if lambda_gp > 0.0:
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if type == 'real': # either use real images, fake images, or a linear interpolation of two.
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interpolatesv = real_data
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elif type == 'fake':
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interpolatesv = fake_data
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elif type == 'mixed':
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alpha = torch.rand(real_data.shape[0], 1, device=device)
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alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
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interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
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else:
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raise NotImplementedError('{} not implemented'.format(type))
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interpolatesv.requires_grad_(True)
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disc_interpolates = netD(interpolatesv)
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gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
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grad_outputs=torch.ones(disc_interpolates.size()).to(device),
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create_graph=True, retain_graph=True, only_inputs=True)
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gradients = gradients[0].view(real_data.size(0), -1) # flat the data
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gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
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return gradient_penalty, gradients
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else:
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return 0.0, None
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class ResnetGenerator(nn.Module):
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"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
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We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
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"""
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def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
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"""Construct a Resnet-based generator
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Parameters:
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input_nc (int) -- the number of channels in input images
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output_nc (int) -- the number of channels in output images
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ngf (int) -- the number of filters in the last conv layer
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norm_layer -- normalization layer
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use_dropout (bool) -- if use dropout layers
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n_blocks (int) -- the number of ResNet blocks
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padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
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"""
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assert(n_blocks >= 0)
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super(ResnetGenerator, self).__init__()
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if type(norm_layer) == functools.partial:
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use_bias = norm_layer.func == nn.InstanceNorm2d
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else:
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use_bias = norm_layer == nn.InstanceNorm2d
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model = [nn.ReflectionPad2d(3),
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nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
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norm_layer(ngf),
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nn.ReLU(True)]
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n_downsampling = 2
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for i in range(n_downsampling): # add downsampling layers
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mult = 2 ** i
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model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
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norm_layer(ngf * mult * 2),
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nn.ReLU(True)]
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mult = 2 ** n_downsampling
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for i in range(n_blocks): # add ResNet blocks
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model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
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for i in range(n_downsampling): # add upsampling layers
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mult = 2 ** (n_downsampling - i)
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model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
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kernel_size=3, stride=2,
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padding=1, output_padding=1,
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bias=use_bias),
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norm_layer(int(ngf * mult / 2)),
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nn.ReLU(True)]
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model += [nn.ReflectionPad2d(3)]
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model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
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model += [nn.Tanh()]
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self.model = nn.Sequential(*model)
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def forward(self, input):
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"""Standard forward"""
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return self.model(input)
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class ResnetBlock(nn.Module):
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"""Define a Resnet block"""
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def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
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"""Initialize the Resnet block
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A resnet block is a conv block with skip connections
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We construct a conv block with build_conv_block function,
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and implement skip connections in <forward> function.
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Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
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"""
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super(ResnetBlock, self).__init__()
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self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
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def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
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"""Construct a convolutional block.
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Parameters:
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dim (int) -- the number of channels in the conv layer.
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padding_type (str) -- the name of padding layer: reflect | replicate | zero
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norm_layer -- normalization layer
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use_dropout (bool) -- if use dropout layers.
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use_bias (bool) -- if the conv layer uses bias or not
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Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
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"""
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conv_block = []
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p = 0
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if padding_type == 'reflect':
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conv_block += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv_block += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
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if use_dropout:
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conv_block += [nn.Dropout(0.5)]
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p = 0
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if padding_type == 'reflect':
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conv_block += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv_block += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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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):
|
||
"""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
|
||
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),
|
||
norm_layer(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),
|
||
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)
|