def __init__(self, configer, loss_dict=None, flag=""):
super(ClsModel, self).__init__()
self.configer = configer
self.flag = flag if len(flag) == 0 else "{}_".format(flag)
self.backbone = slim.__dict__[configer.get('network.{}backbone'.format(self.flag))](
pretrained=configer.get('network.{}pretrained'.format(self.flag)),
has_classifier=False
)
self.reduction = None
fc_dim_out = configer.get('network.{}fc_dim'.format(self.flag), default=None)
fc_dim = self.backbone.num_features
if fc_dim_out is not None:
self.reduction = nn.Conv2d(self.backbone.num_features, fc_dim_out, 1)
fc_dim = fc_dim_out
self.linear_list = nn.ModuleList()
linear_type = configer.get('network', '{}linear_type'.format(self.flag))
for num_classes in configer.get('data.num_classes'):
self.linear_list.append(ModuleHelper.Linear(linear_type)(fc_dim, num_classes))
self.embed = None
if configer.get('network.{}embed'.format(self.flag), default=True):
feat_dim = configer.get('network', '{}feat_dim'.format(self.flag))
self.embed = nn.Sequential(
nn.Linear(fc_dim, feat_dim),
nn.BatchNorm1d(feat_dim)
)
self.bn = nn.BatchNorm1d(fc_dim)
nn.init.zeros_(self.bn.bias)
self.bn.bias.requires_grad = False
self.valid_loss_dict = LOSS_TYPE[configer.get('loss', 'loss_type')] if loss_dict is None else loss_dict
def __init__(self, configer, flag="", target_class=1):
super(DeployClsModel, self).__init__()
self.configer = configer
self.flag = flag if len(flag) == 0 else "{}_".format(flag)
self.backbone = BackboneSelector(configer).get_backbone(
backbone_type=configer.get('network.{}backbone'.format(self.flag)),
rm_last_stride=configer.get('network', '{}rm_last_stride'.format(self.flag), default=False)
)
self.reduction = None
fc_dim_out = configer.get('network.{}fc_dim'.format(self.flag), default=None)
fc_dim = self.backbone.num_features
if fc_dim_out is not None:
self.reduction = nn.Conv2d(self.backbone.num_features, fc_dim_out, 1)
fc_dim = fc_dim_out
self.bn = None
if configer.get('network.{}fc_bn'.format(self.flag), default=None):
self.bn = nn.BatchNorm2d(fc_dim)
if self.configer.get('deploy.extract_score', default=False) or self.configer.get('deploy.extract_cam', default=False):
self.linear_lists = nn.ModuleList()
for source in range(self.configer.get('data', 'num_data_sources')):
linear_list = nn.ModuleList()
linear_type = self.configer.get('network', '{}src{}_linear_type'.format(self.flag, source))
for num_classes in self.configer.get('data.src{}_num_classes'.format(source)):
linear_list.append(ModuleHelper.Linear(linear_type)(fc_dim, num_classes))
self.linear_lists.append(linear_list)
def __init__(self, configer, loss_dict=None, flag=""):
super(ClsModel, self).__init__()
self.configer = configer
self.flag = flag if len(flag) == 0 else "{}_".format(flag)
self.backbone = BackboneSelector(self.configer).get_backbone(
backbone_type=self.configer.get('network.{}backbone'.format(
self.flag)),
pretrained_model=self.configer.get('network.{}pretrained'.format(
self.flag)),
rm_last_stride=self.configer.get('network.{}rm_last_stride'.format(
self.flag),
default=False))
self.reduction = None
fc_dim_out = self.configer.get('network.{}fc_dim'.format(self.flag),
default=None)
fc_dim = self.backbone.num_features
if fc_dim_out is not None:
self.reduction = nn.Conv2d(self.backbone.num_features, fc_dim_out,
1)
fc_dim = fc_dim_out
self.bn = None
if self.configer.get('network.{}fc_bn'.format(self.flag),
default=True):
self.bn = nn.BatchNorm1d(fc_dim)
nn.init.zeros_(self.bn.bias)
self.bn.bias.requires_grad = False
self.relu = None
if self.configer.get('network.{}fc_relu'.format(self.flag),
default=True):
self.relu = nn.ReLU()
self.linear_lists = nn.ModuleList()
for source in range(self.configer.get('data', 'num_data_sources')):
linear_list = nn.ModuleList()
linear_type = self.configer.get(
'network', '{}src{}_linear_type'.format(self.flag, source))
for num_classes in self.configer.get(
'data.src{}_num_classes'.format(source)):
linear_list.append(
ModuleHelper.Linear(linear_type)(fc_dim, num_classes))
self.linear_lists.append(linear_list)
self.global_linear = None
if self.configer.get('data.global_num_classes',
default=None) is not None:
global_linear_type = self.configer.get(
'network', '{}global_linear_type'.format(self.flag))
self.global_linear = ModuleHelper.Linear(global_linear_type)(
fc_dim,
self.configer.get('data.global_num_classes', default=None))
self.embed_after_norm = self.configer.get('network.embed_after_norm',
default=True)
self.embed = None
if self.configer.get('network.{}embed'.format(self.flag),
default=True):
feat_dim = self.configer.get('network',
'{}feat_dim'.format(self.flag))
embed = []
embed.append(nn.Linear(fc_dim, feat_dim))
if self.configer.get('network.{}embed_norm_type'.format(
self.flag)) == 'L2':
embed.append(LpNormalize(p=2, dim=1))
elif self.configer.get('network.{}embed_norm_type'.format(
self.flag)) == 'BN':
embed.append(nn.BatchNorm1d(feat_dim))
self.embed = nn.Sequential(*embed)
self.valid_loss_dict = LOSS_TYPE[self.configer.get(
'loss', 'loss_type')] if loss_dict is None else loss_dict