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import torch
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import os
import math
W = 11
aa = int(math.floor(512./W))
res = 512 - W*aa
def padpart(A,part,centers,opt,device):
IMAGE_SIZE = opt.fineSize
bs,nc,_,_ = A.shape
ratio = IMAGE_SIZE / 256
NOSE_W = opt.NOSE_W * ratio
NOSE_H = opt.NOSE_H * ratio
EYE_W = opt.EYE_W * ratio
EYE_H = opt.EYE_H * ratio
MOUTH_W = opt.MOUTH_W * ratio
MOUTH_H = opt.MOUTH_H * ratio
A_p = torch.ones((bs,nc,IMAGE_SIZE,IMAGE_SIZE)).to(device)
padvalue = -1 # black
for i in range(bs):
center = centers[i]
if part == 'nose':
A_p[i] = torch.nn.ConstantPad2d((center[2,0] - NOSE_W / 2, IMAGE_SIZE - (center[2,0]+NOSE_W/2), center[2,1] - NOSE_H / 2, IMAGE_SIZE - (center[2,1]+NOSE_H/2)),padvalue)(A[i])
elif part == 'eyel':
A_p[i] = torch.nn.ConstantPad2d((center[0,0] - EYE_W / 2, IMAGE_SIZE - (center[0,0]+EYE_W/2), center[0,1] - EYE_H / 2, IMAGE_SIZE - (center[0,1]+EYE_H/2)),padvalue)(A[i])
elif part == 'eyer':
A_p[i] = torch.nn.ConstantPad2d((center[1,0] - EYE_W / 2, IMAGE_SIZE - (center[1,0]+EYE_W/2), center[1,1] - EYE_H / 2, IMAGE_SIZE - (center[1,1]+EYE_H/2)),padvalue)(A[i])
elif part == 'mouth':
A_p[i] = torch.nn.ConstantPad2d((center[3,0] - MOUTH_W / 2, IMAGE_SIZE - (center[3,0]+MOUTH_W/2), center[3,1] - MOUTH_H / 2, IMAGE_SIZE - (center[3,1]+MOUTH_H/2)),padvalue)(A[i])
return A_p
import numpy as np
def nonlinearDt(dt,type='atan',xmax=torch.Tensor([10.0])):#dt in [0,1], first multiply xmax(>1), then remap to [0,1]
if type == 'atan':
nldt = torch.atan(dt*xmax) / torch.atan(xmax)
elif type == 'sigmoid':
nldt = (torch.sigmoid(dt*xmax)-0.5) / (torch.sigmoid(xmax)-0.5)
elif type == 'tanh':
nldt = torch.tanh(dt*xmax) / torch.tanh(xmax)
elif type == 'pow':
nldt = torch.pow(dt*xmax,2) / torch.pow(xmax,2)
elif type == 'exp':
if xmax.item()>1:
xmax = xmax / 3
nldt = (torch.exp(dt*xmax)-1) / (torch.exp(xmax)-1)
#print("remap dt:", type, xmax.item())
return nldt
class APDrawingPPStyleModel(BaseModel):
def name(self):
return 'APDrawingPPStyleModel'
@staticmethod
def modify_commandline_options(parser, is_train=True):
# changing the default values to match the pix2pix paper
# (https://phillipi.github.io/pix2pix/)
parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')# no_lsgan=True, use_lsgan=False
parser.set_defaults(dataset_mode='aligned')
parser.set_defaults(auxiliary_root='auxiliaryeye2o')
parser.set_defaults(use_local=True, hair_local=True, bg_local=True)
parser.set_defaults(discriminator_local=True, gan_loss_strategy=2)
parser.set_defaults(chamfer_loss=True, dt_nonlinear='exp', lambda_chamfer=0.35, lambda_chamfer2=0.35)
parser.set_defaults(nose_ae=True, others_ae=True, compactmask=True, MOUTH_H=56)
parser.set_defaults(soft_border=1, batch_size=1, save_epoch_freq=25)
parser.add_argument('--nnG_hairc', type=int, default=6, help='nnG for hair classifier')
parser.add_argument('--use_resnet', action='store_true', help='use resnet for generator')
parser.add_argument('--regarch', type=int, default=4, help='architecture for netRegressor')
if is_train:
parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss')
parser.add_argument('--lambda_local', type=float, default=25.0, help='weight for Local loss')
parser.set_defaults(netG_dt='unet_512')
parser.set_defaults(netG_line='unet_512')
return parser
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.isTrain = opt.isTrain
# specify the training losses you want to print out. The program will call base_model.get_current_losses
self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
if self.isTrain and self.opt.no_l1_loss:
self.loss_names = ['G_GAN', 'D_real', 'D_fake']
if self.isTrain and self.opt.use_local and not self.opt.no_G_local_loss:
self.loss_names.append('G_local')
self.loss_names.append('G_hair_local')
self.loss_names.append('G_bg_local')
if self.isTrain and self.opt.discriminator_local:
self.loss_names.append('D_real_local')
self.loss_names.append('D_fake_local')
self.loss_names.append('G_GAN_local')
if self.isTrain and self.opt.chamfer_loss:
self.loss_names.append('G_chamfer')
self.loss_names.append('G_chamfer2')
if self.isTrain and self.opt.continuity_loss:
self.loss_names.append('G_continuity')
self.loss_names.append('G')
print('loss_names', self.loss_names)
# specify the images you want to save/display. The program will call base_model.get_current_visuals
self.visual_names = ['real_A', 'fake_B', 'real_B']
if self.opt.use_local:
self.visual_names += ['fake_B0', 'fake_B1']
self.visual_names += ['fake_B_hair', 'real_B_hair', 'real_A_hair']
self.visual_names += ['fake_B_bg', 'real_B_bg', 'real_A_bg']
if self.opt.region_enm in [0,1]:
if self.opt.nose_ae:
self.visual_names += ['fake_B_nose_v','fake_B_nose_v1','fake_B_nose_v2','cmask1no']
if self.opt.others_ae:
self.visual_names += ['fake_B_eyel_v','fake_B_eyel_v1','fake_B_eyel_v2','cmask1el']
self.visual_names += ['fake_B_eyer_v','fake_B_eyer_v1','fake_B_eyer_v2','cmask1er']
self.visual_names += ['fake_B_mouth_v','fake_B_mouth_v1','fake_B_mouth_v2','cmask1mo']
elif self.opt.region_enm in [2]:
self.visual_names += ['fake_B_nose','fake_B_eyel','fake_B_eyer','fake_B_mouth']
if self.isTrain and self.opt.chamfer_loss:
self.visual_names += ['dt1', 'dt2']
self.visual_names += ['dt1gt', 'dt2gt']
if self.isTrain and self.opt.soft_border:
self.visual_names += ['mask']
if not self.isTrain and self.opt.save2:
self.visual_names = ['real_A', 'fake_B']
print('visuals', self.visual_names)
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks
self.auxiliary_model_names = []
if self.isTrain:
self.model_names = ['G', 'D']
if self.opt.discriminator_local:
self.model_names += ['DLEyel','DLEyer','DLNose','DLMouth','DLHair','DLBG']
# auxiliary nets for loss calculation
if self.opt.chamfer_loss:
self.auxiliary_model_names += ['DT1', 'DT2']
self.auxiliary_model_names += ['Line1', 'Line2']
if self.opt.continuity_loss:
self.auxiliary_model_names += ['Regressor']
else: # during test time, only load Gs
self.model_names = ['G']
if self.opt.test_continuity_loss:
self.auxiliary_model_names += ['Regressor']
if self.opt.use_local:
self.model_names += ['GLEyel','GLEyer','GLNose','GLMouth','GLHair','GLBG','GCombine']
self.auxiliary_model_names += ['CLm','CLh']
# auxiliary nets for local output refinement
if self.opt.nose_ae:
self.auxiliary_model_names += ['AE']
if self.opt.others_ae:
self.auxiliary_model_names += ['AEel','AEer','AEmowhite','AEmoblack']
print('model_names', self.model_names)
print('auxiliary_model_names', self.auxiliary_model_names)
# load/define networks
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
opt.nnG)
print('netG', opt.netG)
if self.isTrain:
use_sigmoid = opt.no_lsgan
self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
print('netD', opt.netD, opt.n_layers_D)
if self.opt.discriminator_local:
self.netDLEyel = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
self.netDLEyer = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
self.netDLNose = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
self.netDLMouth = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
self.netDLHair = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
self.netDLBG = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
if self.opt.use_local:
netlocal1 = 'partunet' if self.opt.use_resnet == 0 else 'resnet_nblocks'
netlocal2 = 'partunet2' if self.opt.use_resnet == 0 else 'resnet_6blocks'
netlocal2_style = 'partunet2style' if self.opt.use_resnet == 0 else 'resnet_style2_6blocks'
self.netGLEyel = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal1, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=3)
self.netGLEyer = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal1, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=3)
self.netGLNose = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal1, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=3)
self.netGLMouth = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal1, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=3)
self.netGLHair = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal2_style, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=4,
extra_channel=3)
self.netGLBG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, netlocal2, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, nnG=4)
# by default combiner_type is combiner, which uses resnet
print('combiner_type', self.opt.combiner_type)
self.netGCombine = networks.define_G(2*opt.output_nc, opt.output_nc, opt.ngf, self.opt.combiner_type, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, 2)
# auxiliary classifiers for mouth and hair
ratio = self.opt.fineSize / 256
self.MOUTH_H = int(self.opt.MOUTH_H * ratio)
self.MOUTH_W = int(self.opt.MOUTH_W * ratio)
self.netCLm = networks.define_G(opt.input_nc, 2, opt.ngf, 'classifier', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
nnG = 3, ae_h = self.MOUTH_H, ae_w = self.MOUTH_W)
self.netCLh = networks.define_G(opt.input_nc, 3, opt.ngf, 'classifier', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
nnG = opt.nnG_hairc, ae_h = opt.fineSize, ae_w = opt.fineSize)
if self.isTrain:
self.fake_AB_pool = ImagePool(opt.pool_size)
# define loss functions
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan).to(self.device)
self.criterionL1 = torch.nn.L1Loss()
# initialize optimizers
self.optimizers = []
if not self.opt.use_local:
print('G_params 1 components')
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999))
else:
G_params = list(self.netG.parameters()) + list(self.netGLEyel.parameters()) + list(self.netGLEyer.parameters()) + list(self.netGLNose.parameters()) + list(self.netGLMouth.parameters()) + list(self.netGCombine.parameters()) + list(self.netGLHair.parameters()) + list(self.netGLBG.parameters())
print('G_params 8 components')
self.optimizer_G = torch.optim.Adam(G_params,
lr=opt.lr, betas=(opt.beta1, 0.999))
if not self.opt.discriminator_local:
print('D_params 1 components')
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999))
else:#self.opt.discriminator_local == True
D_params = list(self.netD.parameters()) + list(self.netDLEyel.parameters()) +list(self.netDLEyer.parameters()) + list(self.netDLNose.parameters()) + list(self.netDLMouth.parameters()) + list(self.netDLHair.parameters()) + list(self.netDLBG.parameters())
print('D_params 7 components')
self.optimizer_D = torch.optim.Adam(D_params,
lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
# ==================================auxiliary nets (loaded, parameters fixed)=============================
if self.opt.use_local and self.opt.nose_ae:
ratio = self.opt.fineSize / 256
NOSE_H = self.opt.NOSE_H * ratio
NOSE_W = self.opt.NOSE_W * ratio
self.netAE = networks.define_G(opt.output_nc, opt.output_nc, opt.ngf, self.opt.nose_ae_net, 'batch',
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
latent_dim=self.opt.ae_latentno, ae_h=NOSE_H, ae_w=NOSE_W)
self.set_requires_grad(self.netAE, False)
if self.opt.use_local and self.opt.others_ae:
ratio = self.opt.fineSize / 256
EYE_H = self.opt.EYE_H * ratio
EYE_W = self.opt.EYE_W * ratio
MOUTH_H = self.opt.MOUTH_H * ratio
MOUTH_W = self.opt.MOUTH_W * ratio
self.netAEel = networks.define_G(opt.output_nc, opt.output_nc, opt.ngf, self.opt.nose_ae_net, 'batch',
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
latent_dim=self.opt.ae_latenteye, ae_h=EYE_H, ae_w=EYE_W)
self.netAEer = networks.define_G(opt.output_nc, opt.output_nc, opt.ngf, self.opt.nose_ae_net, 'batch',
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
latent_dim=self.opt.ae_latenteye, ae_h=EYE_H, ae_w=EYE_W)
self.netAEmowhite = networks.define_G(opt.output_nc, opt.output_nc, opt.ngf, self.opt.nose_ae_net, 'batch',
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
latent_dim=self.opt.ae_latentmo, ae_h=MOUTH_H, ae_w=MOUTH_W)
self.netAEmoblack = networks.define_G(opt.output_nc, opt.output_nc, opt.ngf, self.opt.nose_ae_net, 'batch',
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
latent_dim=self.opt.ae_latentmo, ae_h=MOUTH_H, ae_w=MOUTH_W)
self.set_requires_grad(self.netAEel, False)
self.set_requires_grad(self.netAEer, False)
self.set_requires_grad(self.netAEmowhite, False)
self.set_requires_grad(self.netAEmoblack, False)
if self.isTrain and self.opt.continuity_loss:
self.nc = 1
self.netRegressor = networks.define_G(self.nc, 1, opt.ngf, 'regressor', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids_p,
nnG = opt.regarch)
self.set_requires_grad(self.netRegressor, False)
if self.isTrain and self.opt.chamfer_loss:
self.nc = 1
self.netDT1 = networks.define_G(self.nc, self.nc, opt.ngf, opt.netG_dt, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids_p)
self.netDT2 = networks.define_G(self.nc, self.nc, opt.ngf, opt.netG_dt, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids_p)
self.set_requires_grad(self.netDT1, False)
self.set_requires_grad(self.netDT2, False)
self.netLine1 = networks.define_G(self.nc, self.nc, opt.ngf, opt.netG_line, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids_p)
self.netLine2 = networks.define_G(self.nc, self.nc, opt.ngf, opt.netG_line, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids_p)
self.set_requires_grad(self.netLine1, False)
self.set_requires_grad(self.netLine2, False)
# ==================================for test (nets loaded, parameters fixed)=============================
if not self.isTrain and self.opt.test_continuity_loss:
self.nc = 1
self.netRegressor = networks.define_G(self.nc, 1, opt.ngf, 'regressor', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids,
nnG = opt.regarch)
self.set_requires_grad(self.netRegressor, False)
def set_input(self, input):
AtoB = self.opt.which_direction == 'AtoB'
self.real_A = input['A' if AtoB else 'B'].to(self.device)
self.real_B = input['B' if AtoB else 'A'].to(self.device)
self.image_paths = input['A_paths' if AtoB else 'B_paths']
self.batch_size = len(self.image_paths)
if self.opt.use_local:
self.real_A_eyel = input['eyel_A'].to(self.device)
self.real_A_eyer = input['eyer_A'].to(self.device)
self.real_A_nose = input['nose_A'].to(self.device)
self.real_A_mouth = input['mouth_A'].to(self.device)
self.real_B_eyel = input['eyel_B'].to(self.device)
self.real_B_eyer = input['eyer_B'].to(self.device)
self.real_B_nose = input['nose_B'].to(self.device)
self.real_B_mouth = input['mouth_B'].to(self.device)
if self.opt.region_enm in [0,1]:
self.center = input['center']
if self.opt.soft_border:
self.softel = input['soft_eyel_mask'].to(self.device)
self.softer = input['soft_eyer_mask'].to(self.device)
self.softno = input['soft_nose_mask'].to(self.device)
self.softmo = input['soft_mouth_mask'].to(self.device)
if self.opt.compactmask:
self.cmask = input['cmask'].to(self.device)
self.cmask1 = self.cmask*2-1#[0,1]->[-1,1]
self.cmaskel = input['cmaskel'].to(self.device)
self.cmask1el = self.cmaskel*2-1
self.cmasker = input['cmasker'].to(self.device)
self.cmask1er = self.cmasker*2-1
self.cmaskmo = input['cmaskmo'].to(self.device)
self.cmask1mo = self.cmaskmo*2-1
self.real_A_hair = input['hair_A'].to(self.device)
self.real_B_hair = input['hair_B'].to(self.device)
self.mask = input['mask'].to(self.device) # mask for non-eyes,nose,mouth
self.mask2 = input['mask2'].to(self.device) # mask for non-bg
self.real_A_bg = input['bg_A'].to(self.device)
self.real_B_bg = input['bg_B'].to(self.device)
if (self.isTrain and self.opt.chamfer_loss):
self.dt1gt = input['dt1gt'].to(self.device)
self.dt2gt = input['dt2gt'].to(self.device)
if self.isTrain and self.opt.emphasis_conti_face:
self.face_mask = input['face_mask'].cuda(self.gpu_ids_p[0])
def getonehot(self,outputs,classes):
[maxv,index] = torch.max(outputs,1)
y = torch.unsqueeze(index,1)
onehot = torch.FloatTensor(self.batch_size,classes).to(self.device)
onehot.zero_()
onehot.scatter_(1,y,1)
return onehot
def forward(self):
if not self.opt.use_local:
self.fake_B = self.netG(self.real_A)
else:
self.fake_B0 = self.netG(self.real_A)
# EYES, MOUTH
outputs1 = self.netCLm(self.real_A_mouth)
onehot1 = self.getonehot(outputs1,2)
if not self.opt.others_ae:
fake_B_eyel = self.netGLEyel(self.real_A_eyel)
fake_B_eyer = self.netGLEyer(self.real_A_eyer)
fake_B_mouth = self.netGLMouth(self.real_A_mouth)
else: # use AE that only constains compact region, need cmask!
self.fake_B_eyel1 = self.netGLEyel(self.real_A_eyel)
self.fake_B_eyer1 = self.netGLEyer(self.real_A_eyer)
self.fake_B_mouth1 = self.netGLMouth(self.real_A_mouth)
self.fake_B_eyel2,_ = self.netAEel(self.fake_B_eyel1)
self.fake_B_eyer2,_ = self.netAEer(self.fake_B_eyer1)
# USE 2 AEs
self.fake_B_mouth2 = torch.FloatTensor(self.batch_size,self.opt.output_nc,self.MOUTH_H,self.MOUTH_W).to(self.device)
for i in range(self.batch_size):
if onehot1[i][0] == 1:
self.fake_B_mouth2[i],_ = self.netAEmowhite(self.fake_B_mouth1[i].unsqueeze(0))
#print('AEmowhite')
elif onehot1[i][1] == 1:
self.fake_B_mouth2[i],_ = self.netAEmoblack(self.fake_B_mouth1[i].unsqueeze(0))
#print('AEmoblack')
fake_B_eyel = self.add_with_mask(self.fake_B_eyel2,self.fake_B_eyel1,self.cmaskel)
fake_B_eyer = self.add_with_mask(self.fake_B_eyer2,self.fake_B_eyer1,self.cmasker)
fake_B_mouth = self.add_with_mask(self.fake_B_mouth2,self.fake_B_mouth1,self.cmaskmo)
# NOSE
if not self.opt.nose_ae:
fake_B_nose = self.netGLNose(self.real_A_nose)
else: # use AE that only constains compact region, need cmask!
self.fake_B_nose1 = self.netGLNose(self.real_A_nose)
self.fake_B_nose2,_ = self.netAE(self.fake_B_nose1)
fake_B_nose = self.add_with_mask(self.fake_B_nose2,self.fake_B_nose1,self.cmask)
# for visuals and later local loss
if self.opt.region_enm in [0,1]:
self.fake_B_nose = fake_B_nose
self.fake_B_eyel = fake_B_eyel
self.fake_B_eyer = fake_B_eyer
self.fake_B_mouth = fake_B_mouth
# for soft border of 4 rectangle facial feature
if self.opt.region_enm == 0 and self.opt.soft_border:
self.fake_B_nose = self.masked(fake_B_nose, self.softno)
self.fake_B_eyel = self.masked(fake_B_eyel, self.softel)
self.fake_B_eyer = self.masked(fake_B_eyer, self.softer)
self.fake_B_mouth = self.masked(fake_B_mouth, self.softmo)
elif self.opt.region_enm in [2]: # need to multiply cmask
self.fake_B_nose = self.masked(fake_B_nose,self.cmask)
self.fake_B_eyel = self.masked(fake_B_eyel,self.cmaskel)
self.fake_B_eyer = self.masked(fake_B_eyer,self.cmasker)
self.fake_B_mouth = self.masked(fake_B_mouth,self.cmaskmo)
# HAIR, BG AND PARTCOMBINE
outputs2 = self.netCLh(self.real_A_hair)
onehot2 = self.getonehot(outputs2,3)
if not self.isTrain:
opt = self.opt
if opt.imagefolder == 'images':
file_name = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch), 'styleonehot.txt')
else:
file_name = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch), opt.imagefolder, 'styleonehot.txt')
message = '%s [%d %d] [%d %d %d]' % (self.image_paths[0], onehot1[0][0], onehot1[0][1],
onehot2[0][0], onehot2[0][1], onehot2[0][2])
with open(file_name, 'a+') as s_file:
s_file.write(message)
s_file.write('\n')
fake_B_hair = self.netGLHair(self.real_A_hair,onehot2)
fake_B_bg = self.netGLBG(self.real_A_bg)
self.fake_B_hair = self.masked(fake_B_hair,self.mask*self.mask2)
self.fake_B_bg = self.masked(fake_B_bg,self.inverse_mask(self.mask2))
if not self.opt.compactmask:
self.fake_B1 = self.partCombiner2_bg(fake_B_eyel,fake_B_eyer,fake_B_nose,fake_B_mouth,fake_B_hair,fake_B_bg,self.mask*self.mask2,self.inverse_mask(self.mask2),self.opt.comb_op)
else:
self.fake_B1 = self.partCombiner2_bg(fake_B_eyel,fake_B_eyer,fake_B_nose,fake_B_mouth,fake_B_hair,fake_B_bg,self.mask*self.mask2,self.inverse_mask(self.mask2),self.opt.comb_op,self.opt.region_enm,self.cmaskel,self.cmasker,self.cmask,self.cmaskmo)
self.fake_B = self.netGCombine(torch.cat([self.fake_B0,self.fake_B1],1))
# for AE visuals
if self.opt.region_enm in [0,1]:
if self.opt.nose_ae:
self.fake_B_nose_v = padpart(self.fake_B_nose, 'nose', self.center, self.opt, self.device)
self.fake_B_nose_v1 = padpart(self.fake_B_nose1, 'nose', self.center, self.opt, self.device)
self.fake_B_nose_v2 = padpart(self.fake_B_nose2, 'nose', self.center, self.opt, self.device)
self.cmask1no = padpart(self.cmask1, 'nose', self.center, self.opt, self.device)
if self.opt.others_ae:
self.fake_B_eyel_v = padpart(self.fake_B_eyel, 'eyel', self.center, self.opt, self.device)
self.fake_B_eyel_v1 = padpart(self.fake_B_eyel1, 'eyel', self.center, self.opt, self.device)
self.fake_B_eyel_v2 = padpart(self.fake_B_eyel2, 'eyel', self.center, self.opt, self.device)
self.cmask1el = padpart(self.cmask1el, 'eyel', self.center, self.opt, self.device)
self.fake_B_eyer_v = padpart(self.fake_B_eyer, 'eyer', self.center, self.opt, self.device)
self.fake_B_eyer_v1 = padpart(self.fake_B_eyer1, 'eyer', self.center, self.opt, self.device)
self.fake_B_eyer_v2 = padpart(self.fake_B_eyer2, 'eyer', self.center, self.opt, self.device)
self.cmask1er = padpart(self.cmask1er, 'eyer', self.center, self.opt, self.device)
self.fake_B_mouth_v = padpart(self.fake_B_mouth, 'mouth', self.center, self.opt, self.device)
self.fake_B_mouth_v1 = padpart(self.fake_B_mouth1, 'mouth', self.center, self.opt, self.device)
self.fake_B_mouth_v2 = padpart(self.fake_B_mouth2, 'mouth', self.center, self.opt, self.device)
self.cmask1mo = padpart(self.cmask1mo, 'mouth', self.center, self.opt, self.device)
if not self.isTrain and self.opt.test_continuity_loss:
self.ContinuityForTest(real=1)
def backward_D(self):
# Fake
# stop backprop to the generator by detaching fake_B
fake_AB = self.fake_AB_pool.query(torch.cat((self.real_A, self.fake_B), 1))
#print('fake_AB', fake_AB.shape) # (1,4,512,512)
pred_fake = self.netD(fake_AB.detach())# by detach, not affect G's gradient
self.loss_D_fake = self.criterionGAN(pred_fake, False)
if self.opt.discriminator_local:
fake_AB_parts = self.getLocalParts(fake_AB)
local_names = ['DLEyel','DLEyer','DLNose','DLMouth','DLHair','DLBG']
self.loss_D_fake_local = 0
for i in range(len(fake_AB_parts)):
net = getattr(self, 'net' + local_names[i])
pred_fake_tmp = net(fake_AB_parts[i].detach())
addw = self.getaddw(local_names[i])
self.loss_D_fake_local = self.loss_D_fake_local + self.criterionGAN(pred_fake_tmp, False) * addw
self.loss_D_fake = self.loss_D_fake + self.loss_D_fake_local
# Real
real_AB = torch.cat((self.real_A, self.real_B), 1)
pred_real = self.netD(real_AB)
self.loss_D_real = self.criterionGAN(pred_real, True)
if self.opt.discriminator_local:
real_AB_parts = self.getLocalParts(real_AB)
local_names = ['DLEyel','DLEyer','DLNose','DLMouth','DLHair','DLBG']
self.loss_D_real_local = 0
for i in range(len(real_AB_parts)):
net = getattr(self, 'net' + local_names[i])
pred_real_tmp = net(real_AB_parts[i])
addw = self.getaddw(local_names[i])
self.loss_D_real_local = self.loss_D_real_local + self.criterionGAN(pred_real_tmp, True) * addw
self.loss_D_real = self.loss_D_real + self.loss_D_real_local
# Combined loss
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
self.loss_D.backward()
def backward_G(self):
# First, G(A) should fake the discriminator
fake_AB = torch.cat((self.real_A, self.fake_B), 1)
pred_fake = self.netD(fake_AB) # (1,4,512,512)->(1,1,30,30)
self.loss_G_GAN = self.criterionGAN(pred_fake, True)
if self.opt.discriminator_local:
fake_AB_parts = self.getLocalParts(fake_AB)
local_names = ['DLEyel','DLEyer','DLNose','DLMouth','DLHair','DLBG']
self.loss_G_GAN_local = 0 # G_GAN_local is then added into G_GAN
for i in range(len(fake_AB_parts)):
net = getattr(self, 'net' + local_names[i])
pred_fake_tmp = net(fake_AB_parts[i])
addw = self.getaddw(local_names[i])
self.loss_G_GAN_local = self.loss_G_GAN_local + self.criterionGAN(pred_fake_tmp, True) * addw
if self.opt.gan_loss_strategy == 1:
self.loss_G_GAN = (self.loss_G_GAN + self.loss_G_GAN_local) / (len(fake_AB_parts) + 1)
elif self.opt.gan_loss_strategy == 2:
self.loss_G_GAN_local = self.loss_G_GAN_local * 0.25
self.loss_G_GAN = self.loss_G_GAN + self.loss_G_GAN_local
# Second, G(A) = B
if not self.opt.no_l1_loss:
self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
if self.opt.use_local and not self.opt.no_G_local_loss:
local_names = ['eyel','eyer','nose','mouth']
self.loss_G_local = 0
for i in range(len(local_names)):
fakeblocal = getattr(self, 'fake_B_' + local_names[i])
realblocal = getattr(self, 'real_B_' + local_names[i])
addw = self.getaddw(local_names[i])
self.loss_G_local = self.loss_G_local + self.criterionL1(fakeblocal,realblocal) * self.opt.lambda_local * addw
self.loss_G_hair_local = self.criterionL1(self.fake_B_hair, self.real_B_hair) * self.opt.lambda_local * self.opt.addw_hair
self.loss_G_bg_local = self.criterionL1(self.fake_B_bg, self.real_B_bg) * self.opt.lambda_local * self.opt.addw_bg
# Third, chamfer matching (assume chamfer_2way and chamfer_only_line is true)
if self.opt.chamfer_loss:
if self.fake_B.shape[1] == 3:
tmp = self.fake_B[:,0,...]*0.299+self.fake_B[:,1,...]*0.587+self.fake_B[:,2,...]*0.114
fake_B_gray = tmp.unsqueeze(1)
else:
fake_B_gray = self.fake_B
if self.real_B.shape[1] == 3:
tmp = self.real_B[:,0,...]*0.299+self.real_B[:,1,...]*0.587+self.real_B[:,2,...]*0.114
real_B_gray = tmp.unsqueeze(1)
else:
real_B_gray = self.real_B
gpu_p = self.opt.gpu_ids_p[0]
gpu = self.opt.gpu_ids[0]
if gpu_p != gpu:
fake_B_gray = fake_B_gray.cuda(gpu_p)
real_B_gray = real_B_gray.cuda(gpu_p)
# d_CM(a_i,G(p_i))
self.dt1 = self.netDT1(fake_B_gray)
self.dt2 = self.netDT2(fake_B_gray)
dt1 = self.dt1/2.0+0.5#[-1,1]->[0,1]
dt2 = self.dt2/2.0+0.5
if self.opt.dt_nonlinear != '':
dt_xmax = torch.Tensor([self.opt.dt_xmax]).cuda(gpu_p)
dt1 = nonlinearDt(dt1, self.opt.dt_nonlinear, dt_xmax)
dt2 = nonlinearDt(dt2, self.opt.dt_nonlinear, dt_xmax)
#print('dt1dt2',torch.min(dt1).item(),torch.max(dt1).item(),torch.min(dt2).item(),torch.max(dt2).item())
bs = real_B_gray.shape[0]
real_B_gray_line1 = self.netLine1(real_B_gray)
real_B_gray_line2 = self.netLine2(real_B_gray)
self.loss_G_chamfer = (dt1[(real_B_gray<0)&(real_B_gray_line1<0)].sum() + dt2[(real_B_gray>=0)&(real_B_gray_line2>=0)].sum()) / bs * self.opt.lambda_chamfer
if gpu_p != gpu:
self.loss_G_chamfer = self.loss_G_chamfer.cuda(gpu)
# d_CM(G(p_i),a_i)
if gpu_p != gpu:
dt1gt = self.dt1gt.cuda(gpu_p)
dt2gt = self.dt2gt.cuda(gpu_p)
else:
dt1gt = self.dt1gt
dt2gt = self.dt2gt
if self.opt.dt_nonlinear != '':
dt1gt = nonlinearDt(dt1gt, self.opt.dt_nonlinear, dt_xmax)
dt2gt = nonlinearDt(dt2gt, self.opt.dt_nonlinear, dt_xmax)
#print('dt1gtdt2gt',torch.min(dt1gt).item(),torch.max(dt1gt).item(),torch.min(dt2gt).item(),torch.max(dt2gt).item())
self.dt1gt = (self.dt1gt-0.5)*2
self.dt2gt = (self.dt2gt-0.5)*2
fake_B_gray_line1 = self.netLine1(fake_B_gray)
fake_B_gray_line2 = self.netLine2(fake_B_gray)
self.loss_G_chamfer2 = (dt1gt[(fake_B_gray<0)&(fake_B_gray_line1<0)].sum() + dt2gt[(fake_B_gray>=0)&(fake_B_gray_line2>=0)].sum()) / bs * self.opt.lambda_chamfer2
if gpu_p != gpu:
self.loss_G_chamfer2 = self.loss_G_chamfer2.cuda(gpu)
# Fourth, line continuity loss, constrained on synthesized drawing
if self.opt.continuity_loss:
# Patch-based
self.get_patches()
self.outputs = self.netRegressor(self.fake_B_patches)
if not self.opt.emphasis_conti_face:
self.loss_G_continuity = (1.0-torch.mean(self.outputs)).cuda(gpu) * self.opt.lambda_continuity
else:
self.loss_G_continuity = torch.mean((1.0-self.outputs)*self.conti_weights).cuda(gpu) * self.opt.lambda_continuity
self.loss_G = self.loss_G_GAN
if 'G_L1' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_L1
if 'G_local' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_local
if 'G_hair_local' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_hair_local
if 'G_bg_local' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_bg_local
if 'G_chamfer' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_chamfer
if 'G_chamfer2' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_chamfer2
if 'G_continuity' in self.loss_names:
self.loss_G = self.loss_G + self.loss_G_continuity
self.loss_G.backward()
def optimize_parameters(self):
self.forward()
# update D
self.set_requires_grad(self.netD, True)
if self.opt.discriminator_local:
self.set_requires_grad(self.netDLEyel, True)
self.set_requires_grad(self.netDLEyer, True)
self.set_requires_grad(self.netDLNose, True)
self.set_requires_grad(self.netDLMouth, True)
self.set_requires_grad(self.netDLHair, True)
self.set_requires_grad(self.netDLBG, True)
self.optimizer_D.zero_grad()
self.backward_D()
self.optimizer_D.step()
# update G
self.set_requires_grad(self.netD, False)
if self.opt.discriminator_local:
self.set_requires_grad(self.netDLEyel, False)
self.set_requires_grad(self.netDLEyer, False)
self.set_requires_grad(self.netDLNose, False)
self.set_requires_grad(self.netDLMouth, False)
self.set_requires_grad(self.netDLHair, False)
self.set_requires_grad(self.netDLBG, False)
self.optimizer_G.zero_grad()
self.backward_G()
self.optimizer_G.step()
def get_patches(self):
gpu_p = self.opt.gpu_ids_p[0]
gpu = self.opt.gpu_ids[0]
if gpu_p != gpu:
self.fake_B = self.fake_B.cuda(gpu_p)
# [1,1,512,512]->[bs,1,11,11]
patches = []
if self.isTrain and self.opt.emphasis_conti_face:
weights = []
W2 = int(W/2)
t = np.random.randint(res,size=2)
for i in range(aa):
for j in range(aa):
p = self.fake_B[:,:,t[0]+i*W:t[0]+(i+1)*W,t[1]+j*W:t[1]+(j+1)*W]
whitenum = torch.sum(p>=0.0)
#if whitenum < 5 or whitenum > W*W-5:
if whitenum < 1 or whitenum > W*W-1:
continue
patches.append(p)
if self.isTrain and self.opt.emphasis_conti_face:
weights.append(self.face_mask[:,:,t[0]+i*W+W2,t[1]+j*W+W2])
self.fake_B_patches = torch.cat(patches, dim=0)
if self.isTrain and self.opt.emphasis_conti_face:
self.conti_weights = torch.cat(weights, dim=0)+1 #0->1,1->2
def get_patches_real(self):
# [1,1,512,512]->[bs,1,11,11]
patches = []
t = np.random.randint(res,size=2)
for i in range(aa):
for j in range(aa):
p = self.real_B[:,:,t[0]+i*W:t[0]+(i+1)*W,t[1]+j*W:t[1]+(j+1)*W]
whitenum = torch.sum(p>=0.0)
#if whitenum < 5 or whitenum > W*W-5:
if whitenum < 1 or whitenum > W*W-1:
continue
patches.append(p)
self.real_B_patches = torch.cat(patches, dim=0) |