def __init__(self):
super(DANNModel, self).__init__()
self.features, self.pooling, self.class_classifier, \
pooling_ftrs, pooling_output_side = backbone_models.get_backbone_model()
self.domain_classifier = nn.Sequential(
nn.Linear(pooling_ftrs * pooling_output_side * pooling_output_side, 128),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Linear(128, 1),
)
def __init__(self):
super(DANNModel, self).__init__()
self.features, self.pooling, self.class_classifier, \
domain_input_len, self.classifier_before_domain_cnt = backbone_models.get_backbone_model()
self.domain_classifier = nn.Sequential(
nn.Linear(domain_input_len, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Linear(1024, 1),
)
def __init__(self):
super(DANNModel, self).__init__()
self.features, self.pooling, self.class_classifier, \
domain_input_len, self.classifier_before_domain_cnt = backbone_models.get_backbone_model()
if dann_config.NEED_ADAPTATION_BLOCK:
self.adaptation_block = nn.Sequential(
nn.ReLU(),
nn.Linear(domain_input_len, 2048),
nn.ReLU(inplace=True),
)
domain_input_len = 2048
classifier_start_output_len = self.class_classifier[self.classifier_before_domain_cnt][-1].out_features
self.class_classifier[self.classifier_before_domain_cnt][-1] = nn.Linear(2048, classifier_start_output_len)
self.domain_classifier = domain_heads.get_domain_head(domain_input_len)
def __init__(self):
super(OneDomainModel, self).__init__()
self.features, self.pooling, self.class_classifier, *_ = backbone_models.get_backbone_model(
)
def __init__(self):
super(DANNModel, self).__init__()
self.features, self.pooling, self.class_classifier, \
domain_input_len, self.classifier_before_domain_cnt = backbone_models.get_backbone_model()
self.domain_classifier = domain_heads.get_domain_head(domain_input_len)
def __init__(self):
super(DANNCA_Model, self).__init__()
self.features, self.pooling, self.class_classifier, \
_, _ = backbone_models.get_backbone_model()