def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier(opt.model) #.cuda(device=opt.device)
#####################
# Init weights
#####################
# self.classifier.apply(weights_init)
print_network(self.classifier)
self.optimizer = get_optimizer(opt, self.classifier)
self.scheduler = get_scheduler(opt, self.optimizer)
# load networks
# if opt.load:
# pretrained_path = opt.load
# self.load_network(self.classifier, 'G', opt.which_epoch, pretrained_path)
# if self.training:
# self.load_network(self.discriminitor, 'D', opt.which_epoch, pretrained_path)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
# with open('datasets/class_weight.pkl', 'rb') as f:
# class_weight = pickle.load(f, encoding='bytes')
# class_weight = np.array(class_weight, dtype=np.float32)
# class_weight = torch.from_numpy(class_weight).to(opt.device)
# if opt.class_weight:
# self.criterionCE = nn.CrossEntropyLoss(weight=class_weight)
# else:
self.criterionCE = nn.CrossEntropyLoss()
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier() #.cuda(device=opt.device)
#####################
# Init weights
#####################
# self.classifier.apply(weights_init)
print_network(self.classifier)
self.optimizer = optim.Adam(self.classifier.parameters(),
lr=opt.lr,
betas=(0.95, 0.999))
# load networks
if opt.load:
pretrained_path = opt.load
self.load_network(self.classifier, 'G', opt.which_epoch,
pretrained_path)
# if self.training:
# self.load_network(self.discriminitor, 'D', opt.which_epoch, pretrained_path)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier()
#####################
# Init weights
#####################
# self.classifier.apply(weights_init)
print_network(self.classifier)
self.optimizer = optim.Adam(self.classifier.parameters(),
lr=opt.lr,
betas=(0.95, 0.999))
self.scheduler = get_scheduler(opt, self.optimizer)
# load networks
# if opt.load:
# pretrained_path = opt.load
# self.load_network(self.classifier, 'G', opt.which_epoch, pretrained_path)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.direct_feature = DirectFeature(opt.model)
self.feature_nums = self.direct_feature.get_feature_num()
self.meta_embedding = MetaEmbedding(self.feature_nums, 50030)
print_network(self.direct_feature)
print_network(self.meta_embedding)
# TODO: 这里学习率是不是可以调成 direct_feature 0.01 meta_embedding 0.1
# self.optimizer = optim.SGD(chain(self.direct_feature.parameters(), self.meta_embedding.parameters()),
# lr=0.01, momentum=0.9, weight_decay=0.0005)
self.optimizer = optim.Adam(chain(self.direct_feature.parameters(), self.meta_embedding.parameters()),
lr=0.01)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
# different weight for different classes
self.criterionCE = nn.CrossEntropyLoss()