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_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():
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
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(is_resnet,
is_densenet,
is_senet,
model='tbdp',
parallel=False,
semff=False,
pcamff=False):
if is_resnet:
original_model = resnet.resnet50(pretrained=True)
Encoder = modules.E_resnet(original_model)
if model == 'tbdp':
model = net.TBDPNet(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048],
parallel=parallel,
pcamff=pcamff)
elif model == 'hu':
model = net.Hu(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048],
semff=semff,
pcamff=pcamff)
else:
raise NotImplementedError(
"Select model type in [\'tbdp\', \'hu\']")
if is_densenet:
original_model = densenet.densenet161(pretrained=True)
Encoder = modules.E_densenet(original_model)
if model == 'tbdp':
model = net.TBDPNet(Encoder,
num_features=2208,
block_channel=[192, 384, 1056, 2208],
parallel=parallel,
pcamff=pcamff)
elif model == 'hu':
model = net.Hu(Encoder,
num_features=2208,
block_channel=[192, 384, 1056, 2208],
semff=semff,
pcamff=pcamff)
else:
raise NotImplementedError(
"Select model type in [\'tbdp\', \'hu\']")
if is_senet:
original_model = senet.senet154(pretrained='imagenet')
Encoder = modules.E_senet(original_model)
if model == 'tbdp':
model = net.TBDPNet(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048],
parallel=parallel,
pcamff=pcamff)
elif model == 'hu':
model = net.Hu(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048],
semff=semff,
pcamff=pcamff)
else:
raise NotImplementedError(
"Select model type in [\'tbdp\', \'hu\']")
return model
import torch.nn.parallel
import torch.nn.functional
from utils import *
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")]