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])
