def create_segmenter(net, pretrained, num_classes):
"""Create Encoder; for now only ResNet [50,101,152]"""
from models.resnet import rf_lw50, rf_lw101, rf_lw152
if str(net) == '50':
return rf_lw50(num_classes, imagenet=pretrained)
elif str(net) == '101':
return rf_lw101(num_classes, imagenet=pretrained)
elif str(net) == '152':
return rf_lw152(num_classes, imagenet=pretrained)
else:
raise ValueError("{} is not supported".format(str(net)))
def get_segmenter(
enc_backbone,
enc_pretrained,
num_classes,
):
"""Create Encoder-Decoder; for now only ResNet [50,101,152] Encoders are supported"""
if enc_backbone == "50":
return rf_lw50(num_classes, imagenet=enc_pretrained)
elif enc_backbone == "101":
return rf_lw101(num_classes, imagenet=enc_pretrained)
elif enc_backbone == "152":
return rf_lw152(num_classes, imagenet=enc_pretrained)
else:
raise ValueError("{} is not supported".format(str(enc_backbone)))
def create_segmenter(net, pretrained, num_classes):
"""Create Encoder; for now only ResNet [50,101,152]"""
import sys
sys.path.append("../")
from models.resnet import rf_lw50, rf_lw101, rf_lw152
init_model = '../models/resnet/50_person.ckpt'
if str(net) == '50':
return rf_lw50(num_classes, model_path=init_model, imagenet=pretrained)
elif str(net) == '101':
return rf_lw101(num_classes,
model_path=init_model,
imagenet=pretrained)
elif str(net) == '152':
return rf_lw152(num_classes, imagenet=pretrained)
else:
raise ValueError("{} is not supported".format(str(net)))
def get_model(model_name, classes, pre_train=False, mode='train'):
if model_name == 'ESpnet_2_8_decoder':
from models import Espnet
if pre_train:
pre_train_path = os.path.join(pre_train)
model = Espnet.ESPNet(classes, 2, 8, pre_train_path, mode=mode)
else:
model = Espnet.ESPNet(classes, 2, 8, mode=mode)
elif model_name == 'ESpnet_2_8':
from models import Espnet
model = Espnet.ESPNet_Encoder(classes, 2, 8)
elif model_name == 'EDAnet':
from models import EDANet
model = EDANet.EDANet(classes)
elif model_name == 'ERFnet':
from models import ERFnet
model = ERFnet.Net(classes)
elif model_name == 'Enet':
from models import Enet
model = Enet.ENet(classes)
elif model_name == 'IRRnet_2_8':
from models import Irregularity_conv
model = Irregularity_conv.ESPNet(classes, 2, 8, mode=mode)
elif model_name == 'MOBILE_V2':
from models import mobilenet
model = mobilenet.mbv2(classes)
elif model_name == 'RF_LW_resnet_50':
from models import resnet
model = resnet.rf_lw50(classes)
elif model_name == 'RF_LW_resnet_101':
from models import resnet
model = resnet.rf_lw101(classes)
elif model_name == 'RF_LW_resnet_152':
from models import resnet
model = resnet.rf_lw152(classes)
elif model_name == 'Bisenet':
from models import BiSeNet
model = BiSeNet.BiSeNet(out_class=classes)
elif model_name == 'Basenet':
from models import Basenet
model = Basenet.Basenet(classes)
else:
raise NotImplementedError
return model
def create_multiNet(net,
num_classes,
num_depths=10,
pretrained=None,
task_type=2):
from models.multitask import multiNet
from models.resnet import rf_lw50, rf_lw101, rf_lw152
if str(net) == 'multi':
return multiNet(num_classes, num_depths, task_type)
elif str(net) == '50':
return rf_lw50(num_classes, imagenet=pretrained)
elif str(net) == '101':
return rf_lw101(num_classes, imagenet=pretrained)
elif str(net) == '152':
return rf_lw152(num_classes, imagenet=pretrained)
else:
raise ValueError("{} is not supported".format(str(net)))
