def load_features(args):
if args.arch == 'iresnet18':
features = iresnet.iresnet18(pretrained=True,
num_classes=args.embedding_size)
elif args.arch == 'iresnet34':
features = iresnet.iresnet34(pretrained=True,
num_classes=args.embedding_size)
elif args.arch == 'iresnet50':
features = iresnet.iresnet50(pretrained=True,
num_classes=args.embedding_size)
elif args.arch == 'iresnet100':
features = iresnet.iresnet100(pretrained=True,
num_classes=args.embedding_size)
else:
raise ValueError()
return features
def build_model(args):
if args.arch == 'iresgroup':
assert args.model_depth in [50, 101, 152]
if args.model_depth == 50:
model = iresgroup.iresgroup50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 101:
model = iresgroup.iresgroup101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 152:
model = iresgroup.iresgroup152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
if args.arch == 'iresgroupfix':
assert args.model_depth in [50, 101, 152]
if args.model_depth == 50:
model = iresgroupfix.iresgroupfix50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 101:
model = iresgroupfix.iresgroupfix101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 152:
model = iresgroupfix.iresgroupfix152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
if args.arch == 'resgroupfix':
assert args.model_depth in [50, 101, 152]
if args.model_depth == 50:
model = resgroupfix.resgroupfix50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 101:
model = resgroupfix.resgroupfix101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 152:
model = resgroupfix.resgroupfix152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
if args.arch == 'resgroup':
assert args.model_depth in [50, 101, 152]
if args.model_depth == 50:
model = resgroup.resgroup50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 101:
model = resgroup.resgroup101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 152:
model = resgroup.resgroup152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
if args.arch == 'iresnet':
assert args.model_depth in [18, 34, 50, 101, 152, 200, 302, 404, 1001]
if args.model_depth == 18:
model = iresnet.iresnet18(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 34:
model = iresnet.iresnet34(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 50:
model = iresnet.iresnet50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 101:
model = iresnet.iresnet101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 152:
model = iresnet.iresnet152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 200:
model = iresnet.iresnet200(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 302:
model = iresnet.iresnet302(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 404:
model = iresnet.iresnet404(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 1001:
model = iresnet.iresnet1001(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
##
if args.arch == 'seiresnet':
assert args.model_depth in [18, 34, 50, 101, 152, 200, 302, 404, 1001]
if args.model_depth == 18:
model = seiresnet.seiresnet18(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 34:
model = seiresnet.seiresnet34(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 50:
model = seiresnet.seiresnet50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 101:
model = seiresnet.seiresnet101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 152:
model = seiresnet.seiresnet152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 200:
model = seiresnet.seiresnet200(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 302:
model = seiresnet.seiresnet302(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 404:
model = seiresnet.seiresnet404(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 1001:
model = seiresnet.seiresnet1001(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
if args.arch == 'seiresgroup':
assert args.model_depth in [50, 101, 152]
if args.model_depth == 50:
model = seiresgroup.iresgroup50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 101:
model = seiresgroup.iresgroup101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
elif args.model_depth == 152:
model = seiresgroup.iresgroup152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual,
groups=args.groups)
##
if args.arch == 'resstage':
assert args.model_depth in [18, 34, 50, 101, 152, 200]
if args.model_depth == 18:
model = resstage.resstage18(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 34:
model = resstage.resstage34(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 50:
model = resstage.resstage50(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 101:
model = resstage.resstage101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 152:
model = resstage.resstage152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 200:
model = resstage.resstage200(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
if args.arch == 'resnet':
assert args.model_depth in [18, 34, 50, 101, 152, 200]
if args.model_depth == 18:
model = resnet.resnet18(pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 34:
model = resnet.resnet34(pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 50:
model = resnet.resnet50(pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 101:
model = resnet.resnet101(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 152:
model = resnet.resnet152(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
elif args.model_depth == 200:
model = resnet.resnet200(
pretrained=args.pretrained,
num_classes=args.n_classes,
zero_init_residual=args.zero_init_residual)
return model
def build_backbone(backbone='resnet-50',
layers=50,
output_stride=16,
norm_layer=None):
# if norm_layer is None:
# norm_layer = nn.BatchNorm2d
# elif norm_layer is 'gn':
# norm_layer = GroupNorm
# elif norm_layer is 'frn':
# norm_layer = FilterResponseNorm2d
if backbone is 'resnet':
if layers == 50:
model = resnet.resnet50(norm_layer=norm_layer)
return model
elif layers == 101:
model = resnet.resnet101(norm_layer=norm_layer)
return model
elif layers == 152:
model = resnet.resnet152(norm_layer=norm_layer)
return model
elif layers == 200:
model = resnet.resnet200(norm_layer=norm_layer)
return model
elif backbone is 'resgroup':
if layers == 50:
model = resgroup.resgroup50(norm_layer=norm_layer)
return model
elif layers == 101:
model = resgroup.resgroup101(norm_layer=norm_layer)
return model
elif layers == 152:
model = resgroup.resgroup152(norm_layer=norm_layer)
return model
elif backbone is 'iresnet':
if layers == 50:
model = iresnet.iresnet50(norm_layer=norm_layer)
return model
elif layers == 101:
model = iresnet.iresnet101(norm_layer=norm_layer)
return model
elif layers == 152:
model = iresnet.iresnet152(norm_layer=norm_layer)
return model
elif layers == 200:
model = iresnet.iresnet200(norm_layer=norm_layer)
return model
elif layers == 302:
model = iresnet.iresnet302(norm_layer=norm_layer)
return model
elif layers == 404:
model = iresnet.iresnet404(norm_layer=norm_layer)
return model
elif layers == 1001:
model = iresnet.iresnet1001(norm_layer=norm_layer)
return model
elif backbone is 'iresgroup':
if layers == 50:
model = iresgroup.iresgroup50(norm_layer=norm_layer)
return model
elif layers == 101:
model = iresgroup.iresgroup101(norm_layer=norm_layer)
return model
elif layers == 152:
model = iresgroup.iresgroup152(norm_layer=norm_layer)
return model
elif backbone is 'xception':
model = xception.xception(output_stride=output_stride,
norm_layer=norm_layer)
return model