1542 lines
66 KiB
Python
1542 lines
66 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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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import numpy as np
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import random
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from .stylegan_networks import StyleGAN2Discriminator, StyleGAN2Generator, TileStyleGAN2Discriminator
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from .ncsn_networks import NLayerDiscriminator_ncsn, ResnetGenerator_ncsn
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###############################################################################
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# Helper Functions
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###############################################################################
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def get_filter(filt_size=3):
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if(filt_size == 1):
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a = np.array([1., ])
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elif(filt_size == 2):
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a = np.array([1., 1.])
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elif(filt_size == 3):
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a = np.array([1., 2., 1.])
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elif(filt_size == 4):
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a = np.array([1., 3., 3., 1.])
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elif(filt_size == 5):
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a = np.array([1., 4., 6., 4., 1.])
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elif(filt_size == 6):
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a = np.array([1., 5., 10., 10., 5., 1.])
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elif(filt_size == 7):
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a = np.array([1., 6., 15., 20., 15., 6., 1.])
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filt = torch.Tensor(a[:, None] * a[None, :])
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filt = filt / torch.sum(filt)
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return filt
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class Downsample(nn.Module):
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def __init__(self, channels, pad_type='reflect', filt_size=3, stride=2, pad_off=0):
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super(Downsample, self).__init__()
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self.filt_size = filt_size
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self.pad_off = pad_off
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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))]
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self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
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self.stride = stride
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self.off = int((self.stride - 1) / 2.)
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self.channels = channels
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filt = get_filter(filt_size=self.filt_size)
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self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
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self.pad = get_pad_layer(pad_type)(self.pad_sizes)
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def forward(self, inp):
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if(self.filt_size == 1):
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if(self.pad_off == 0):
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return inp[:, :, ::self.stride, ::self.stride]
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else:
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return self.pad(inp)[:, :, ::self.stride, ::self.stride]
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else:
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return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
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class Upsample2(nn.Module):
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def __init__(self, scale_factor, mode='nearest'):
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super().__init__()
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self.factor = scale_factor
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self.mode = mode
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def forward(self, x):
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return torch.nn.functional.interpolate(x, scale_factor=self.factor, mode=self.mode)
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class Upsample(nn.Module):
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def __init__(self, channels, pad_type='repl', filt_size=4, stride=2):
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super(Upsample, self).__init__()
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self.filt_size = filt_size
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self.filt_odd = np.mod(filt_size, 2) == 1
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self.pad_size = int((filt_size - 1) / 2)
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self.stride = stride
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self.off = int((self.stride - 1) / 2.)
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self.channels = channels
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filt = get_filter(filt_size=self.filt_size) * (stride**2)
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self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
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self.pad = get_pad_layer(pad_type)([1, 1, 1, 1])
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def forward(self, inp):
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ret_val = F.conv_transpose2d(self.pad(inp), self.filt, stride=self.stride, padding=1 + self.pad_size, groups=inp.shape[1])[:, :, 1:, 1:]
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if(self.filt_odd):
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return ret_val
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else:
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return ret_val[:, :, :-1, :-1]
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def get_pad_layer(pad_type):
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if(pad_type in ['refl', 'reflect']):
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PadLayer = nn.ReflectionPad2d
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elif(pad_type in ['repl', 'replicate']):
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PadLayer = nn.ReplicationPad2d
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elif(pad_type == 'zero'):
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PadLayer = nn.ZeroPad2d
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else:
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print('Pad type [%s] not recognized' % pad_type)
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return PadLayer
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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, debug=False):
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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 debug:
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print(classname)
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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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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=[], debug=False, initialize_weights=True):
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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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# if not amp:
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# net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs for non-AMP training
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if initialize_weights:
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init_weights(net, init_type, init_gain=init_gain, debug=debug)
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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',
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init_gain=0.02, no_antialias=False, no_antialias_up=False, gpu_ids=[], opt=None):
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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, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=9, opt=opt)
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elif netG == 'resnet_9blocks_mask':
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net = ResnetGeneratorMask(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)
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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, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=6, opt=opt)
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elif netG == 'resnet_4blocks':
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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)
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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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elif netG == 'stylegan2':
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net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, opt=opt)
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elif netG == 'smallstylegan2':
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net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, n_blocks=2, opt=opt)
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elif netG == 'resnet_cat':
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n_blocks = 8
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net = G_Resnet(input_nc, output_nc, opt.nz, num_downs=2, n_res=n_blocks - 4, ngf=ngf, norm='inst', nl_layer='relu')
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elif netG == 'resnet_9blocks_cond':
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net = ResnetGenerator_ncsn(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)
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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, initialize_weights=('stylegan2' not in netG))
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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):
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if netF == 'global_pool':
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net = PoolingF()
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elif netF == 'reshape':
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net = ReshapeF()
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elif netF == 'sample':
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net = PatchSampleF(use_mlp=False, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
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elif netF == 'mlp_sample':
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net = PatchSampleF(use_mlp=True, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
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elif netF == 'strided_conv':
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net = StridedConvF(init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids)
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else:
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raise NotImplementedError('projection model name [%s] is not recognized' % netF)
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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, no_antialias=False, gpu_ids=[], opt=None):
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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 cna 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 Leaky 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, no_antialias=no_antialias,)
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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, no_antialias=no_antialias,)
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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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elif 'stylegan2' in netD:
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net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
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elif netD == 'basic_cond': # more options
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net = NLayerDiscriminator_ncsn(input_nc, ndf, n_layers=3, norm_layer=norm_layer, no_antialias=no_antialias)
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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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initialize_weights=('stylegan2' not in netD))
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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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||
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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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||
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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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||
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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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||
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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', 'nonsaturating']:
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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.
|
||
|
||
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:
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||
A label tensor filled with ground truth label, and with the size of the input
|
||
"""
|
||
|
||
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)
|
||
|
||
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)
|
||
# print(prediction.shape, target_is_real.shape)
|
||
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, input_nc), nn.ReLU(), nn.Linear(input_nc, input_nc)])
|
||
mlp = nn.Sequential(*[nn.Linear(input_nc, input_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_feats = []
|
||
if self.use_mlp and not self.mlp_init:
|
||
self.create_mlp(feats)
|
||
for feat_id, feat in enumerate(feats):
|
||
mlp = getattr(self, 'mlp_%d' % feat_id)
|
||
res = mlp(feat)
|
||
return_feats.append(res)
|
||
|
||
return return_feats
|
||
|
||
|
||
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)
|
||
print(feat.shape)
|
||
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
|
||
print(f"layer_id: {layer_id}, type(layer_id): {type(layer_id)}")
|
||
print(f"len(list(self.model)) - 1: {len(list(self.model)) - 1}, type(len(list(self.model)) - 1): {type(len(list(self.model)) - 1)}")
|
||
# Print layers to see what it is. If layers is still a tensor here, we need to understand why.
|
||
print(f"layers: {layers}, type(layers): {type(layers)}")
|
||
print(f"encode_only'shape: {encode_only.shape}, type(encode_only): {type(encode_only)}")
|
||
if layer_id == len(list(self.model)) - 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 ResnetGeneratorMask(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(ResnetGeneratorMask, 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)]
|
||
|
||
self.layer_encoder = len(model) - 1
|
||
|
||
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, mask_rate=0.0):
|
||
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
|
||
elif mask_rate > 0.0:
|
||
feat = input
|
||
rate = random.uniform(0.0, mask_rate)
|
||
for layer_id, layer in enumerate(self.model):
|
||
feat = layer(feat)
|
||
# print(layer_id, self.layer_encoder)
|
||
if layer_id == self.layer_encoder:
|
||
# print('shape:', feat.shape)
|
||
B , C, H, W = feat.shape[0], feat.shape[1], feat.shape[2], feat.shape[3]
|
||
feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2)
|
||
all_num = feat_reshape.shape[1]
|
||
point_num = all_num * rate
|
||
point_id = np.random.permutation(all_num)
|
||
point_id = point_id[:int(min(point_num, all_num))]
|
||
feat_reshape[:,point_id,:] = 0
|
||
feat = feat_reshape.permute(0, 2, 1).reshape([B, C, H, W])
|
||
# print('rec', feat.shape)
|
||
|
||
return feat
|
||
|
||
|
||
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 MLPDiscriminator(nn.Module):
|
||
def __init__(self, in_feat=768, hid_feat = 768, out_feat = 768, dropout = 0.):
|
||
super().__init__()
|
||
if not hid_feat:
|
||
hid_feat = in_feat
|
||
if not out_feat:
|
||
out_feat = in_feat
|
||
self.linear1 = nn.Linear(in_feat, hid_feat)
|
||
self.activation = nn.GELU()
|
||
self.linear2 = nn.Linear(hid_feat, out_feat)
|
||
self.dropout = nn.Dropout(dropout)
|
||
|
||
def forward(self, x):
|
||
x = self.linear1(x)
|
||
x = self.activation(x)
|
||
x = self.dropout(x)
|
||
x = self.linear2(x)
|
||
return self.dropout(x)
|
||
|
||
|
||
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)
|