def __init__(self, opts):
super(DRIT, self).__init__()
# parameters
lr = 0.0001
lr_dcontent = lr / 2.5
self.nz = 8
self.concat = opts.concat
self.no_ms = opts.no_ms
# discriminators
if opts.dis_scale > 1:
self.disA = networks.MultiScaleDis(opts.input_dim_a, opts.dis_scale, norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disB = networks.MultiScaleDis(opts.input_dim_b, opts.dis_scale, norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disA2 = networks.MultiScaleDis(opts.input_dim_a, opts.dis_scale, norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disB2 = networks.MultiScaleDis(opts.input_dim_b, opts.dis_scale, norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
else:
self.disA = networks.Dis(opts.input_dim_a, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB = networks.Dis(opts.input_dim_b, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disA2 = networks.Dis(opts.input_dim_a, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB2 = networks.Dis(opts.input_dim_b, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disContent = networks.Dis_content()
# encoders
self.enc_c = networks.E_content(opts.input_dim_a, opts.input_dim_b)
if self.concat:
self.enc_a = networks.E_attr_concat(opts.input_dim_a, opts.input_dim_b, self.nz, \
norm_layer=None,
nl_layer=networks.get_non_linearity(layer_type='lrelu'))
else:
self.enc_a = networks.E_attr(opts.input_dim_a, opts.input_dim_b, self.nz)
# generator
if self.concat:
self.gen = networks.G_concat(opts.input_dim_a, opts.input_dim_b, nz=self.nz)
else:
self.gen = networks.G(opts.input_dim_a, opts.input_dim_b, nz=self.nz)
# optimizers
self.disA_opt = torch.optim.Adam(self.disA.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disB_opt = torch.optim.Adam(self.disB.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disA2_opt = torch.optim.Adam(self.disA2.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disB2_opt = torch.optim.Adam(self.disB2.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disContent_opt = torch.optim.Adam(self.disContent.parameters(), lr=lr_dcontent, betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_c_opt = torch.optim.Adam(self.enc_c.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.enc_a_opt = torch.optim.Adam(self.enc_a.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.gen_opt = torch.optim.Adam(self.gen.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
# Setup the loss function for training
self.criterionL1 = torch.nn.L1Loss()
def __init__(self, opts):
super(UID, self).__init__()
with torch.autograd.set_detect_anomaly(True):
# parameters
lr = opts.lr
self.nz = 8
self.concat = opts.concat
self.lambdaB = opts.lambdaB
self.lambdaI = opts.lambdaI
# discriminators
if opts.dis_scale > 1:
self.disA = networks.MultiScaleDis(opts.input_dim_a, opts.dis_scale, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB = networks.MultiScaleDis(opts.input_dim_b, opts.dis_scale, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disA2 = networks.MultiScaleDis(opts.input_dim_a, opts.dis_scale, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB2 = networks.MultiScaleDis(opts.input_dim_b, opts.dis_scale, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
else:
self.disA = networks.Dis(opts.input_dim_a, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB = networks.Dis(opts.input_dim_b, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disA2 = networks.Dis(opts.input_dim_a, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
self.disB2 = networks.Dis(opts.input_dim_b, norm=opts.dis_norm, sn=opts.dis_spectral_norm)
# encoders
self.enc_c = networks.E_content(opts.input_dim_a, opts.input_dim_b)
if self.concat:
self.enc_a = networks.E_attr_concat(opts.input_dim_b, self.nz, \
norm_layer=None, nl_layer=networks.get_non_linearity(layer_type='lrelu'))
else:
self.enc_a = networks.E_attr(opts.input_dim_a, opts.input_dim_b, self.nz)
# generator
if self.concat:
self.gen = networks.G_concat(opts.input_dim_a, opts.input_dim_b, nz=self.nz)
else:
self.gen = networks.G(opts.input_dim_a, opts.input_dim_b, nz=self.nz)
# optimizers
self.disA_opt = torch.optim.Adam(self.disA.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disB_opt = torch.optim.Adam(self.disB.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disA2_opt = torch.optim.Adam(self.disA2.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.disB2_opt = torch.optim.Adam(self.disB2.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.enc_c_opt = torch.optim.Adam(self.enc_c.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.enc_a_opt = torch.optim.Adam(self.enc_a.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
self.gen_opt = torch.optim.Adam(self.gen.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=0.0001)
# Setup the loss function for training
self.criterionL1 = torch.nn.L1Loss()
if opts.percep == 'default':
self.perceptualLoss = networks.PerceptualLoss(nn.MSELoss(), opts.gpu, opts.percp_layer)
elif opts.percep == 'face':
self.perceptualLoss = networks.PerceptualLoss16(nn.MSELoss(), opts.gpu, opts.percp_layer)
else:
self.perceptualLoss = networks.MultiPerceptualLoss(nn.MSELoss(), opts.gpu)
def __init__(self, opts):
super(MD_multi, self).__init__()
self.opts = opts
lr = 0.0001
lr_dcontent = lr / 2.5
self.nz = 8
self.isDcontent = opts.isDcontent
if opts.concat == 1:
self.concat = True
else:
self.concat = False
self.dis1 = networks.MD_Dis(opts.input_dim,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm,
c_dim=opts.num_domains,
image_size=opts.crop_size)
self.dis2 = networks.MD_Dis(opts.input_dim,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm,
c_dim=opts.num_domains,
image_size=opts.crop_size)
self.enc_c = networks.MD_E_content(opts.input_dim)
if self.concat:
self.enc_a = networks.MD_E_attr_concat(opts.input_dim, output_nc=self.nz, c_dim=opts.num_domains, \
norm_layer=None, nl_layer=networks.get_non_linearity(layer_type='lrelu'))
self.gen = networks.MD_G_multi_concat(opts.input_dim,
c_dim=opts.num_domains,
nz=self.nz)
else:
self.enc_a = networks.MD_E_attr(opts.input_dim,
output_nc=self.nz,
c_dim=opts.num_domains)
self.gen = networks.MD_G_multi(opts.input_dim,
nz=self.nz,
c_dim=opts.num_domains)
self.dis1_opt = torch.optim.Adam(self.dis1.parameters(),
lr=lr,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.dis2_opt = torch.optim.Adam(self.dis2.parameters(),
lr=lr,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_c_opt = torch.optim.Adam(self.enc_c.parameters(),
lr=lr,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_a_opt = torch.optim.Adam(self.enc_a.parameters(),
lr=lr,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.gen_opt = torch.optim.Adam(self.gen.parameters(),
lr=lr,
betas=(0.5, 0.999),
weight_decay=0.0001)
#if self.isDcontent:
self.disContent = networks.MD_Dis_content(c_dim=opts.num_domains)
self.disContent_opt = torch.optim.Adam(self.disContent.parameters(),
lr=lr_dcontent,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.cls_loss = nn.BCEWithLogitsLoss()
def __init__(self, opts):
super(DRIT, self).__init__()
# parameters
lr = 0.0001
self.nz = 8
self.concat = opts.concat
self.lr_subspace = opts.lr_subspace
#self.lr_MI=opts.lr_MI
self.lr_dis = opts.lr_dis
self.lr_enc = opts.lr_enc
self.lr_gen = opts.lr_gen
self.lr_gen_attr = opts.lr_gen_attr
self.lr_pre_subspace = opts.lr_pre_subspace
#self.lr_pre_MI = opts.lr_pre_MI
self.lr_pre_enc = opts.lr_pre_enc
self.lr_pre_gen = opts.lr_pre_gen
self.margin = opts.margin
self.semantic_w = opts.semantic_w
self.recon_w = opts.recon_w
#self.MI_w = opts.MI_w
self.gan_w = opts.gan_w
self.content_w = opts.content_w
self.no_ms = opts.no_ms
self.loss_1 = nn.BCEWithLogitsLoss()
#映射到公共子空间的网络
self.subspace = model_1.IDCM_NN(img_input_dim=4096, text_input_dim=300)
#self.MI=KLLoss()
self.criterion = ContrastiveLoss(margin=opts.margin,
measure=opts.measure,
max_violation=opts.max_violation)
one = torch.tensor(1, dtype=torch.float).cuda(0)
self.mone = one * -1
# discriminators
if opts.dis_scale > 1: #=3
self.disA = networks.MultiScaleDis(opts.input_dim_a,
n_scale=opts.dis_scale,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disB = networks.MultiScaleDis(opts.input_dim_b,
n_scale=opts.dis_scale,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disA_attr = networks.MultiScaleDis(opts.input_dim_a,
n_scale=opts.dis_scale,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disB_attr = networks.MultiScaleDis(opts.input_dim_b,
n_scale=opts.dis_scale,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
else:
self.disA = networks.Dis(opts.input_dim_a,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
self.disB = networks.Dis(opts.input_dim_b,
norm=opts.dis_norm,
sn=opts.dis_spectral_norm)
# encoders
self.enc_c = networks.E_content(opts.input_dim_a, opts.input_dim_b)
if self.concat:
self.enc_a = networks.E_attr_concat(opts.input_dim_a, opts.input_dim_b, self.nz, \
norm_layer=None, nl_layer=networks.get_non_linearity(layer_type='lrelu'))
else:
self.enc_a = networks.E_attr(opts.input_dim_a, opts.input_dim_b,
self.nz)
# generator
if self.concat:
self.gen = networks.G_concat(opts.input_dim_a,
opts.input_dim_b,
nz=self.nz)
else:
self.gen = networks.G(opts.input_dim_a,
opts.input_dim_b,
nz=self.nz)
self.gen_attr = networks.G_a(opts, opts.input_dim_a, opts.input_dim_b)
# optimizers
self.subspace_opt = torch.optim.Adam(self.subspace.parameters(),
lr=self.lr_subspace,
betas=(0.5, 0.999),
weight_decay=0.0001)
#self.MI_opt = torch.optim.Adam(self.MI.parameters(), lr=self.lr_MI, betas=(0.5, 0.999), weight_decay=0.0001)
self.disA_opt = torch.optim.Adam(self.disA.parameters(),
lr=self.lr_dis,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.disB_opt = torch.optim.Adam(self.disB.parameters(),
lr=self.lr_dis,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.disA_attr_opt = torch.optim.Adam(self.disA_attr.parameters(),
lr=self.lr_dis,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.disB_attr_opt = torch.optim.Adam(self.disB_attr.parameters(),
lr=self.lr_dis,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_c_opt = torch.optim.Adam(self.enc_c.parameters(),
lr=self.lr_enc,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_a_opt = torch.optim.Adam(self.enc_a.parameters(),
lr=self.lr_enc,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.gen_opt = torch.optim.Adam(self.gen.parameters(),
lr=self.lr_gen,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.gen_attr_opt = torch.optim.Adam(self.gen_attr.parameters(),
lr=self.lr_gen_attr,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.subspace_pre_opt = torch.optim.Adam(self.subspace.parameters(),
lr=self.lr_pre_subspace,
betas=(0.5, 0.999),
weight_decay=0.0001)
#self.MI_pre_opt = torch.optim.Adam(self.MI.parameters(), lr=self.lr_pre_MI, betas=(0.5, 0.999), weight_decay=0.0001)
self.enc_c_pre_opt = torch.optim.Adam(self.enc_c.parameters(),
lr=self.lr_pre_enc,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.enc_a_pre_opt = torch.optim.Adam(self.enc_a.parameters(),
lr=self.lr_pre_enc,
betas=(0.5, 0.999),
weight_decay=0.0001)
self.gen_pre_opt = torch.optim.Adam(self.gen.parameters(),
lr=self.lr_pre_gen,
betas=(0.5, 0.999),
weight_decay=0.0001)
# Setup the loss function for training
self.criterionL1 = torch.nn.L1Loss()
self.MSE_loss_fn = torch.nn.MSELoss(reduce=True, size_average=True)
