def build_model(model_name, num_classes):
if model_name == 'SQNet':
return SQNet(classes=num_classes)
elif model_name == 'LinkNet':
return LinkNet(classes=num_classes)
elif model_name == 'SegNet':
return SegNet(classes=num_classes)
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'ENet':
return ENet(classes=num_classes)
elif model_name == 'ERFNet':
return ERFNet(classes=num_classes)
elif model_name == 'CGNet':
return CGNet(classes=num_classes)
elif model_name == 'EDANet':
return EDANet(classes=num_classes)
elif model_name == 'ESNet':
return ESNet(classes=num_classes)
elif model_name == 'ESPNet':
return ESPNet(classes=num_classes)
elif model_name == 'LEDNet':
return LEDNet(classes=num_classes)
elif model_name == 'ESPNet_v2':
return EESPNet_Seg(classes=num_classes)
elif model_name == 'ContextNet':
return ContextNet(classes=num_classes)
elif model_name == 'FastSCNN':
return FastSCNN(classes=num_classes)
elif model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'FSSNet':
return FSSNet(classes=num_classes)
elif model_name == 'FPENet':
return FPENet(classes=num_classes)
def build_model(model_name, num_classes):
# small model
if model_name == 'ENet':
return ENet(classes=num_classes)
elif model_name == 'ERFNet':
return ERFNet(classes=num_classes)
elif model_name == 'ESPNet':
return ESPNet(classes=num_classes)
elif model_name == 'ESPNet_v2':
return EESPNet_Seg(classes=num_classes)
elif model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'BiSeNetV2':
return BiSeNetV2(n_classes=num_classes)
# large model
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'PSPNet50':
return PSPNet(layers=50,
bins=(1, 2, 3, 6),
dropout=0.1,
classes=num_classes,
zoom_factor=8,
use_ppm=True,
pretrained=True)
# elif model_name == 'PSANet50':
# return PSANet(layers=50, dropout=0.1, classes=num_classes, zoom_factor=8, use_psa=True, psa_type=2, compact=compact,
# shrink_factor=shrink_factor, mask_h=mask_h, mask_w=mask_w, psa_softmax=True, pretrained=True)
elif model_name == 'Deeplabv3plus':
return Deeplabv3plus(cfg, num_classes=num_classes)
def build_model(model_name, num_classes):
# for deeplabv3
model_map = {
'deeplabv3_resnet50': network.deeplabv3_resnet50,
'deeplabv3plus_resnet50': network.deeplabv3plus_resnet50,
'deeplabv3_resnet101': network.deeplabv3_resnet101,
'deeplabv3plus_resnet101': network.deeplabv3plus_resnet101,
'deeplabv3_mobilenet': network.deeplabv3_mobilenet,
'deeplabv3plus_mobilenet': network.deeplabv3plus_mobilenet
}
if model_name == 'SQNet':
return SQNet(classes=num_classes)
elif model_name == 'LinkNet':
return LinkNet(classes=num_classes)
elif model_name == 'SegNet':
return SegNet(classes=num_classes)
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'ENet':
return ENet(classes=num_classes)
elif model_name == 'ERFNet':
return ERFNet(classes=num_classes)
elif model_name == 'CGNet':
return CGNet(classes=num_classes)
elif model_name == 'EDANet':
return EDANet(classes=num_classes)
elif model_name == 'ESNet':
return ESNet(classes=num_classes)
elif model_name == 'ESPNet':
return ESPNet(classes=num_classes)
elif model_name == 'LEDNet':
return LEDNet(classes=num_classes)
elif model_name == 'ESPNet_v2':
return EESPNet_Seg(classes=num_classes)
elif model_name == 'ContextNet':
return ContextNet(classes=num_classes)
elif model_name == 'FastSCNN':
return FastSCNN(classes=num_classes)
elif model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'FSSNet':
return FSSNet(classes=num_classes)
elif model_name == 'FPENet':
return FPENet(classes=num_classes)
elif model_name == 'FCN':
return FCN32VGG(classes=num_classes)
elif model_name in model_map.keys():
return model_map[model_name](num_classes, output_stride=8)
def build_model(inputs , num_classes, segmentation_model, is_training ):
if segmentation_model=="FCN8":
print ("segmentation_model:FCN8")
return FCN8(inputs , num_classes)
elif segmentation_model=="U_Net":
print ("segmentation_model:U_Net")
return U_Net(inputs , num_classes)
elif segmentation_model=="Seg_Net":
print ("segmentation_model:Seg_Net")
return Seg_Net(inputs , num_classes)
elif segmentation_model=="Deeplab_v1":
print ("segmentation_model:Deeplab_v1")
return Deeplab_v1(inputs , num_classes)
elif segmentation_model=="Deeplab_v2":
print ("segmentation_model:Deeplab_v2")
return Deeplab_v2(inputs , num_classes, is_training)
elif segmentation_model=="Deeplab_v3":
print ("segmentation_model:Deeplab_v3")
return Deeplab_v3(inputs , num_classes, is_training)
elif segmentation_model=="PSPNet":
print ("segmentation_model:PSPNet")
return PSPNet(inputs , num_classes, is_training)
elif segmentation_model=="GCN":
print ("segmentation_model:GCN")
return GCN(inputs, num_classes, is_training)
elif segmentation_model=="ENet":
print ("segmentation_model:ENet")
return ENet(inputs, num_classes, is_training)
elif segmentation_model=="ICNet":
print ("segmentation_model:ICNet")
return ICNet(inputs , num_classes, is_training)
weight_path = 'Log/SegNet/weight.h5' segnet_model.load_weights(weight_path) pspnet_model = PSPNet((480, 480, 3), 12) weight_path = 'Log/PSPNet/weight.h5' pspnet_model.load_weights(weight_path) deeplab_model = Deeplab((512, 512, 3), 12) weight_path = 'Log/Deeplab/weight.h5' deeplab_model.load_weights(weight_path) deconvnet_model = DeconvNet((512, 512, 3), 12) weight_path = 'Log/DeconvNet/weight.h5' deconvnet_model.load_weights(weight_path) enet_model = ENet((512, 512, 3), 12) weight_path = 'Log/ENet/weight.h5' enet_model.load_weights(weight_path) # In[2] train_path = 'G:\备份\备份\dataset\CamVid' val_path = 'G:\备份\备份\dataset\CamVid' train_img_folder = 'train' train_label_folder = 'trainannot' val_image_folder = 'val' val_label_folder = 'valannot' image_size_ = (480, 480) label_size_ = (240, 240) image_size = (512, 512) label_size = (512, 512)
def build_model(model_name, num_classes):
if model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'FCN_8S_res18':
return FCN_res(backbone='resnet18', classes=num_classes, pretrained=True, scale=8)
elif model_name == 'FCN_8S_res34':
return FCN_res(backbone='resnet34', classes=num_classes, pretrained=True, scale=8)
elif model_name == 'FCN_8S_res50':
return FCN_res(backbone='resnet50', classes=num_classes, pretrained=True, scale=8)
elif model_name == 'FCN_8S_res101':
return FCN_res(backbone='resnet101', classes=num_classes, pretrained=True, scale=8)
elif model_name == 'FCN_32S_res18':
return FCN_res(backbone='resnet18', classes=num_classes, pretrained=True, scale=32)
elif model_name == 'FCN_32S_res50':
return FCN_res(backbone='resnet50', classes=num_classes, pretrained=True, scale=32)
elif model_name == 'FCN_32S_res101':
return FCN_res(backbone='resnet101', classes=num_classes, pretrained=True, scale=32)
elif model_name == 'UNet_res18':
return UNet_res(backbone='resnet18', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res34':
return UNet_res(backbone='resnet34', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res50':
return UNet_res(backbone='resnet50', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res101':
return UNet_res(backbone='resnet101', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res18_ori':
return UNet_res_ori(backbone='resnet18', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res34_ori':
return UNet_res_ori(backbone='resnet34', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res50_ori':
return UNet_res_ori(backbone='resnet50', pretrained=True, classes=num_classes)
elif model_name == 'UNet_res101_ori':
return UNet_res_ori(backbone='resnet101', pretrained=True, classes=num_classes)
elif model_name == 'PSPNet_res18':
return PSPNet(layers=18, bins=(1, 2, 3, 6), dropout=0.1, classes=num_classes, zoom_factor=8, use_ppm=True,
pretrained=True)
elif model_name == 'PSPNet_res34':
return PSPNet(layers=34, bins=(1, 2, 3, 6), dropout=0.1, classes=num_classes, zoom_factor=8, use_ppm=True,
pretrained=True)
elif model_name == 'PSPNet_res50':
return PSPNet(layers=50, bins=(1, 2, 3, 6), dropout=0.1, classes=num_classes, zoom_factor=8, use_ppm=True,
pretrained=True)
elif model_name == 'PSPNet_res101':
return PSPNet(layers=101, bins=(1, 2, 3, 6), dropout=0.1, classes=num_classes, zoom_factor=8, use_ppm=True,
pretrained=True)
## backbone == vgg
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'UNet_overlap':
return UNet_overlap(classes=num_classes)
elif model_name == 'BiSeNet_res18':
return BiSeNet(backbone='resnet18', n_classes=num_classes, pretrained=False)
elif model_name == 'BiSeNet_res101':
return BiSeNet(backbone='resnet101', n_classes=num_classes, pretrained=False)
elif model_name == 'lightSeg':
return lightSeg(backbone='resnet101', n_classes=num_classes, pretrained=False)
elif model_name == 'ENet':
return ENet(classes=num_classes)
## GALDNet
elif model_name == 'GALD_res50':
return GALD_res50(num_classes=num_classes)