'ArcFace':
ArcFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'CosFace':
CosFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'SphereFace':
SphereFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'Am_softmax':
Am_softmax(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS)
}
HEAD = HEAD_DICT[HEAD_NAME]
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
LOSS_DICT = {'Focal': FocalLoss(), 'Softmax': nn.CrossEntropyLoss()}
LOSS = LOSS_DICT[LOSS_NAME]
print("=" * 60)
print(LOSS)
print("{} Loss Generated".format(LOSS_NAME))
print("=" * 60)
if BACKBONE_NAME.find("IR") >= 0:
backbone_paras_only_bn, backbone_paras_wo_bn = separate_irse_bn_paras(
BACKBONE
) # separate batch_norm parameters from others; do not do weight decay for batch_norm parameters to improve the generalizability
_, head_paras_wo_bn = separate_irse_bn_paras(HEAD)
else:
backbone_paras_only_bn, backbone_paras_wo_bn = separate_resnet_bn_paras(
BACKBONE
) # separate batch_norm parameters from others; do not do weight decay for batch_norm parameters to improve the generalizability
HEAD = CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'SphereFace':
HEAD = SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Am_softmax':
HEAD = Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Softmax':
HEAD = Softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
else:
raise NotImplementedError
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
if LOSS_NAME == 'Focal':
LOSS = FocalLoss()
elif LOSS_NAME == 'Softmax':
LOSS = nn.CrossEntropyLoss()
else:
raise NotImplementedError
print("=" * 60)
print(LOSS)
print("{} Loss Generated".format(LOSS_NAME))
print("=" * 60)
if BACKBONE_NAME.find("IR") >= 0:
backbone_paras_only_bn, backbone_paras_wo_bn = separate_irse_bn_paras(BACKBONE) # separate batch_norm parameters from others; do not do weight decay for batch_norm parameters to improve the generalizability
_, head_paras_wo_bn = separate_irse_bn_paras(HEAD)
else:
backbone_paras_only_bn, backbone_paras_wo_bn = separate_resnet_bn_paras(BACKBONE) # separate batch_norm parameters from others; do not do weight decay for batch_norm parameters to improve the generalizability
_, head_paras_wo_bn = separate_resnet_bn_paras(HEAD)
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
HEAD_DICT = {'ArcFace': ArcFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'CosFace': CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'SphereFace': SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'Am_softmax': Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)}
HEAD = HEAD_DICT[HEAD_NAME]
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
LOSS_DICT = {'Focal': FocalLoss(),
'Softmax': nn.CrossEntropyLoss()
'AdaCos' : AdaCos(),
'AdaM_Softmax': AdaM_Softmax() ,
'ArcFace' : ArcFace() ,
'ArcNegFace': ArcNegFace(),
'CircleLoss': Circleloss(),
'CurricularFace': CurricularFace(),
'MagFace' : MagFace(),
'NPCFace' : MV_Softmax.py(),
'SST_Prototype' SST_Prototype(),
}
LOSS = LOSS_DICT[LOSS_NAME]
print("=" * 60)
print(LOSS)