def define_model(is_resnet, is_densenet, is_senet): use18 = True # True if is_resnet: if not use18: original_model = resnet.resnet18(pretrained = True) Encoder = modules.E_resnet(original_model) model = net.model(Encoder, num_features=512, block_channel = [64, 128, 256, 512]) else: stereoModel = Resnet18Encoder(3) model_dict = stereoModel.state_dict() encoder_dict = torch.load('./models/monodepth_resnet18_001.pth',map_location='cpu' ) new_dict = {} for key in encoder_dict: if key in model_dict: new_dict[key] = encoder_dict[key] stereoModel.load_state_dict(new_dict ) Encoder = stereoModel model = net.model(Encoder, num_features=512, block_channel = [64, 128, 256, 512]) if is_densenet: original_model = densenet.densenet161(pretrained=True) Encoder = modules.E_densenet(original_model) model = net.model(Encoder, num_features=2208, block_channel = [192, 384, 1056, 2208]) if is_senet: original_model = senet.senet154(pretrained='imagenet') Encoder = modules.E_senet(original_model) model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048]) return model
def define_model(encoder='resnet'): if encoder is 'resnet': original_model = resnet.resnet50(pretrained=True) Encoder = modules.E_resnet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) if encoder is 'densenet': original_model = densenet.densenet161(pretrained=True) Encoder = modules.E_densenet(original_model) model = net.model(Encoder, num_features=2208, block_channel=[192, 384, 1056, 2208]) if encoder is 'senet': original_model = senet.senet154(pretrained='imagenet') Encoder = modules.E_senet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) if encoder is 'resnet4': original_model = resnet4.resnet50(pretrained=True) Encoder = modules.E_resnet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) return model
def define_test_model(): #archs = {"Resnet", "Densenet", "SEnet", "Custom"} is_resnet = args.arch == "Resnet" #True #False #True is_densenet = args.arch == "Densenet" # #False #True #False # False is_senet = args.arch == "SEnet" # True #False #True #False is_custom = args.arch == "Custom" if is_resnet: #original_model = resnet.resnet18(pretrained = pretrain_logical) #Encoder = modules.E_resnet(original_model) #model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048]) stereoModel = Resnet18Encoder(3) model_dict = stereoModel.state_dict() encoder_dict = torch.load('./models/monodepth_resnet18_001.pth', map_location='cpu') new_dict = {} for key in encoder_dict: # print(key) if key in model_dict: new_dict[key] = encoder_dict[key] stereoModel.load_state_dict(new_dict) Encoder = stereoModel model = net.model(Encoder, num_features=512, block_channel=[64, 128, 256, 512]) print("Loading a model...") print("/model_epoch_{}.pth".format(str(args.load_epoch))) model = model.cuda().float() #print(stereoModel) #print(model) model_dict = torch.load( args.load_dir + "/original_model_epoch_{}.pth".format(str(args.load_epoch))) new_dict = model_dict #new_dict = {} #for key in model_dict: # new_dict[key[7:]] = model_dict[key] model.load_state_dict(new_dict) if is_densenet: # TODO: no dot bug original_model = densenet.densenet161(pretrained=True) Encoder = modules.E_densenet(original_model) model = net.model(Encoder, num_features=2208, block_channel=[192, 384, 1056, 2208]) if is_senet: original_model = senet.senet154(pretrained='imagenet') Encoder = modules.E_senet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) return model
def define_model(is_resnet, is_densenet, is_senet): if is_resnet: original_model = resnet.resnet50(pretrained=True) Encoder = modules.E_resnet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) if is_densenet: original_model = densenet.densenet161(pretrained=True) Encoder = modules.E_densenet(original_model) model = net.model(Encoder, num_features=2208, block_channel=[192, 384, 1056, 2208]) if is_senet: original_model = senet.senet154(pretrained=None) Encoder = modules.E_senet(original_model) model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) return model
def define_model(): original_model = resnet.resnet50(pretrained=True) Encoder = modules.E_resnet(original_model) model = net.model(None, Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048]) return model
from options import get_args from dataloader import nyudv2_dataloader from models.backbone_dict import backbone_dict from models import modules from models import net args = get_args('test') # lode nyud v2 test set TestImgLoader = nyudv2_dataloader.getTestingData_NYUDV2(args.batch_size, args.testlist_path, args.root_path) # model backbone = backbone_dict[args.backbone]() Encoder = modules.E_resnet(backbone) if args.backbone in ['resnet50']: model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048], refinenet=args.refinenet) elif args.backbone in ['resnet18', 'resnet34']: model = net.model(Encoder, num_features=512, block_channel=[64, 128, 256, 512], refinenet=args.refinenet) model = nn.DataParallel(model).cuda() # load test model if args.loadckpt is not None and args.loadckpt.endswith('.pth.tar'): print("loading the specific model in checkpoint_dir: {}".format(args.loadckpt)) state_dict = torch.load(args.loadckpt) model.load_state_dict(state_dict) elif os.path.isdir(args.loadckpt): all_saved_ckpts = [ckpt for ckpt in os.listdir(args.loadckpt) if ckpt.endswith(".pth.tar")] print(all_saved_ckpts) all_saved_ckpts = sorted(all_saved_ckpts, key=lambda x:int(x.split('_')[-1].split('.')[0])) loadckpt = os.path.join(args.loadckpt, all_saved_ckpts[-1])