def __init__(self, opt, dataloader):
super(MNIST_UNET, self).__init__()
self.opt = opt
#self.visualizer = Visualizer(opt)
self.dataloader = dataloader
self.total_steps = len(dataloader)
self.device = torch.device(
'cuda:0' if self.opt.device != 'cpu' else 'cpu')
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
weights_init(self.netg)
weights_init(self.netd)
self.l_adv = self.l2_loss
self.l_con = nn.L1Loss()
self.l_enc = self.l2_loss
self.l_bce = nn.BCELoss()
# Initialize input tensors.
self.input_imgs = torch.empty(size=(self.opt.batchsize, self.opt.nc,
self.opt.isize, self.opt.isize),
dtype=torch.float32,
device=self.device)
#self.label = torch.empty(size=(self.opt.batchsize, ), dtype=torch.float32, device=self.device)
#self.gt = torch.empty(size=(self.opt.batchsize, ), dtype=torch.long, device=self.device)
self.real_label = torch.ones(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
self.fake_label = torch.zeros(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
def __init__(self, opt, dataloader):
super(Ganomaly, self).__init__(opt, dataloader)
# -- Misc attributes
self.epoch = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
self.netg.apply(weights_init)
self.netd.apply(weights_init)
##
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
self.opt.iter = torch.load(os.path.join(self.opt.resume, 'netG.pth'))['epoch']
self.netg.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netG.pth'))['state_dict'])
self.netd.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netD.pth'))['state_dict'])
print("\tDone.\n")
self.l_adv = l2_loss
self.l_con = nn.L1Loss()
self.l_enc = l2_loss
self.l_bce = nn.BCELoss()
##
# Initialize input tensors.
self.input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize, self.opt.isize), dtype=torch.float32, device=self.device)
self.label = torch.empty(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.gt = torch.empty(size=(opt.batchsize,), dtype=torch.long, device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize, self.opt.isize), dtype=torch.float32, device=self.device)
self.real_label = torch.ones (size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.fake_label = torch.zeros(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
##
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.optimizer_d = optim.Adam(self.netd.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
def __init__(self, opt, dataloader=None):
super(Ganomaly, self).__init__()
##
# Initalize variables.
self.opt = opt
self.visualizer = Visualizer(opt)
self.dataloader = dataloader
self.trn_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
self.tst_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
self.device = torch.device(
"cuda:0" if self.opt.device != 'cpu' else "cpu")
# -- Discriminator attributes.
self.out_d_real = None
self.feat_real = None
self.err_d_real = None
self.fake = None
self.latent_i = None
self.latent_o = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_fake = None
self.err_d = None
# -- Generator attributes.
self.out_g = None
self.err_g_bce = None
self.err_g_l1l = None
self.err_g_enc = None
self.err_g = None
# -- Misc attributes
self.epoch = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
self.netg.apply(weights_init)
self.netd.apply(weights_init)
##
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
self.opt.iter = torch.load(
os.path.join(self.opt.resume, 'netG.pth'))['epoch']
self.netg.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netG.pth'))['state_dict'])
self.netd.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netD.pth'))['state_dict'])
print("\tDone.\n")
# print(self.netg)
# print(self.netd)
##
# Loss Functions
self.bce_criterion = nn.BCELoss()
self.l1l_criterion = nn.L1Loss()
self.l2l_criterion = l2_loss
##
# Initialize input tensors.
self.input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize,
self.opt.isize),
dtype=torch.float32,
device=self.device)
self.label = torch.empty(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
self.gt = torch.empty(size=(opt.batchsize, ),
dtype=torch.long,
device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, 3,
self.opt.isize, self.opt.isize),
dtype=torch.float32,
device=self.device)
self.real_label = 1
self.fake_label = 0
##
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.optimizer_d = optim.Adam(self.netd.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
def __init__(self, opt):
super(SSnovelty, self).__init__()
##
# Initalize variables.
self.opt = opt
self.visualizer = Visualizer(opt)
# self.warmup = hyperparameters['model_specifics']['warmup']
self.trn_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
self.tst_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
self.device = torch.device(
"cuda:0" if self.opt.device != 'cpu' else "cpu")
# -- Discriminator attributes.
self.out_d_real = None
self.feat_real = None
self.err_d_real = None
self.fake = None
self.latent_i = None
# self.latent_o = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_fake = None
self.err_d = None
self.idx = 0
self.opt.display = True
# -- Generator attributes.
self.out_g = None
self.err_g_bce = None
self.err_g_l1l = None
self.err_g_enc = None
self.err_g = None
# -- Misc attributes
self.epoch = 0
self.epoch1 = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
self.netg.apply(weights_init)
self.netd.apply(weights_init)
self.netc = Class(self.opt).to(self.device)
##
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
# self.opt.iter = torch.load(os.path.join(self.opt.resume, 'netG.pth'))['epoch']
# self.netg.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netG.pth'))['state_dict'])
# self.netd.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netD.pth'))['state_dict'])
self.netc.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'class.pth'))['state_dict'])
print("\tDone.\n")
# print(self.netg)
# print(self.netd)
##
# Loss Functions
self.bce_criterion = nn.BCELoss()
self.l1l_criterion = nn.L1Loss()
self.mse_criterion = nn.MSELoss()
self.l2l_criterion = l2_loss
self.loss_func = torch.nn.CrossEntropyLoss()
##
# Initialize input tensors.
self.input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize,
self.opt.isize),
dtype=torch.float32,
device=self.device)
self.input_1 = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize,
self.opt.isize),
dtype=torch.float32,
device=self.device)
self.label = torch.empty(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
self.label_r = torch.empty(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
self.gt = torch.empty(size=(self.opt.batchsize, ),
dtype=torch.long,
device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, 3,
self.opt.isize, self.opt.isize),
dtype=torch.float32,
device=self.device)
self.real_label = 1
self.fake_label = 0
base = 1.0
sigma_list = [1, 2, 4, 8, 16]
self.sigma_list = [sigma / base for sigma in sigma_list]
##
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.netc.train()
self.optimizer_d = optim.Adam(self.netd.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
self.optimizer_c = optim.Adam(self.netc.parameters(),
lr=self.opt.lr_c,
betas=(self.opt.beta1, 0.999))
def __init__(self, opt, dataloader=None):
super(Ganomaly, self).__init__()
##
# Initalize variables.
self.opt = opt
self.visualizer = Visualizer(opt)
self.dataloader = dataloader
# set the output folder, and self.opt.name is always euqal ${ganomaly/$dataset},详见option.py line106
self.trn_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
self.tst_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
self.device = torch.device(
"cuda:0" if self.opt.device != 'cpu' else "cpu")
# -- Discriminator attributes.
self.out_d_real = None
self.feat_real = None
self.err_d_real = None
self.fake = None
self.latent_i = None
self.latent_o = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_fake = None
self.err_d = None
# -- Generator attributes.
self.out_g = None
self.err_g_bce = None
self.err_g_l1l = None
self.err_g_enc = None
self.err_g = None
# -- Misc attributes
self.epoch = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
# 分别对网络模型的Conv层和BatchNorm层进行参数初始化
self.netg.apply(weights_init)
self.netd.apply(weights_init)
##
# is not none, to continue training from the path of checkpointers specified by opt.resume
# 类似于我在cnn_disk项目中使用到的fine-tune
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
self.opt.iter = torch.load(
os.path.join(self.opt.resume, 'netG.pth'))['epoch']
self.netg.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netG.pth'))['state_dict'])
self.netd.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netD.pth'))['state_dict'])
print("\tDone.\n")
# print(self.netg)
# print(self.netd)
##
# Loss Functions, Binary Cross Entropy
self.bce_criterion = nn.BCELoss()
self.l1l_criterion = nn.L1Loss()
self.l2l_criterion = l2_loss
##
# Initialize input tensors.
self.input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize,
self.opt.isize),
dtype=torch.float32,
device=self.device)
# self.opt.logger.info('empty input size: {}'.format(self.input.size()))
self.label = torch.empty(size=(self.opt.batchsize, ),
dtype=torch.float32,
device=self.device)
self.gt = torch.empty(size=(opt.batchsize, ),
dtype=torch.long,
device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, 3,
self.opt.isize, self.opt.isize),
dtype=torch.float32,
device=self.device)
self.real_label = 1
self.fake_label = 0
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.optimizer_d = optim.Adam(self.netd.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
def __init__(self, opt, dataloader=None):
super(Ganomaly, self).__init__()
##
# Initalize variables.
self.opt = opt
self.visualizer = Visualizer(opt)
self.dataloader = dataloader
self.trn_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
self.tst_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
# -- Discriminator attributes.
self.out_d_real = None
self.feat_real = None
self.err_d_real = None
self.fake = None
self.latent_i = None
self.latent_o = None
self.out_d_fake = None
self.feat_fake = None
self.err_d_fake = None
self.err_d = None
# -- Generator attributes.
self.out_g = None
self.err_g_bce = None
self.err_g_l1l = None
self.err_g_enc = None
self.err_g = None
# -- Misc attributes
self.epoch = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt)
self.netd = NetD(self.opt)
self.netg.apply(weights_init)
self.netd.apply(weights_init)
##
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
self.opt.iter = torch.load(
os.path.join(self.opt.resume, 'netG.pth'))['epoch']
self.netg.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netG.pth'))['state_dict'])
self.netd.load_state_dict(
torch.load(os.path.join(self.opt.resume,
'netD.pth'))['state_dict'])
print("\tDone.\n")
print(self.netg)
print(self.netd)
##
# Loss Functions
self.bce_criterion = nn.BCELoss()
self.l1l_criterion = nn.L1Loss()
self.l2l_criterion = l2_loss
##
# Initialize input tensors.
self.input = torch.FloatTensor(self.opt.batchsize, 3, self.opt.isize,
self.opt.isize)
self.label = torch.FloatTensor(self.opt.batchsize)
self.gt = torch.LongTensor(self.opt.batchsize)
self.pixel_gt = torch.FloatTensor(self.opt.batchsize, 3,
self.opt.isize, self.opt.isize)
self.noise = torch.FloatTensor(self.opt.batchsize, self.opt.nz, 1, 1)
self.fixed_noise = torch.FloatTensor(self.opt.batchsize, self.opt.nz,
1, 1).normal_(0, 1)
self.fixed_input = torch.FloatTensor(self.opt.batchsize, 3,
self.opt.isize, self.opt.isize)
self.real_label = 1
self.fake_label = 0
self.an_scores = torch.FloatTensor([]) # Anomaly scores.
self.gt_labels = torch.LongTensor([]) # Frame Level GT Labels.
self.pixel_gts = torch.FloatTensor([]) # Pixel Level GT Labels.
##
# Convert to CUDA if available.
if self.opt.gpu_ids:
self.netd.cuda()
self.netg.cuda()
self.bce_criterion.cuda()
self.l1l_criterion.cuda()
self.input, self.label = self.input.cuda(), self.label.cuda()
self.gt, self.pixel_gt = self.gt.cuda(), self.pixel_gt.cuda()
self.noise, self.fixed_noise = self.noise.cuda(
), self.fixed_noise.cuda()
self.fixed_input = self.fixed_input.cuda()
##
# Convert to Autograd Variable
self.input = Variable(self.input, requires_grad=False)
self.label = Variable(self.label, requires_grad=False)
self.gt = Variable(self.gt, requires_grad=False)
self.pixel_gt = Variable(self.pixel_gt, requires_grad=False)
self.noise = Variable(self.noise, requires_grad=False)
self.fixed_noise = Variable(self.fixed_noise, requires_grad=False)
self.fixed_input = Variable(self.fixed_input, requires_grad=False)
##
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.optimizer_d = optim.Adam(self.netd.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(),
lr=self.opt.lr,
betas=(self.opt.beta1, 0.999))
