default=['chair'],
help='list of object classes to use')
parser.add_argument('-vis',
action='store_true',
help='Visualize each model while evaluating')
parser.add_argument('-batchsize', type=int, default=16, help='Batch size.')
args = parser.parse_args()
# Data
valid_set = ShapeNet_ODMS(root ='../../datasets/',categories = args.categories, \
download = True, train = False, high = 128, low = 32, split=.97, voxels = True)
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False, \
num_workers=8)
# Model
model = upscale(128, 32)
model = model.to(args.device)
# Load saved weights
model.load_state_dict(torch.load('log/{0}/best.pth'.format(args.expid)))
iou_epoch = 0.
iou_NN_epoch = 0.
num_batches = 0
model.eval()
with torch.no_grad():
for data in tqdm(dataloader_val):
tgt_odms = data['odms_128'].to(args.device)
tgt_voxels = data['voxels_128'].to(args.device)
inp_odms = data['odms_32'].to(args.device)
"""
train_set = ModelNet_ODMS(root ='../../datasets/',categories = args.categories, \
download = True)
dataloader_train = DataLoader(train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=8)
valid_set = ModelNet_ODMS(root ='../../datasets/',categories = args.categories, \
download = True, train = False)
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False, \
num_workers=8)
"""
Model settings
"""
model = upscale(30, 15).to(args.device)
loss_fn = torch.nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Create log directory, if it doesn't already exist
args.logdir = os.path.join(args.logdir, args.expid)
if not os.path.isdir(args.logdir):
os.makedirs(args.logdir)
print('Created dir:', args.logdir)
# Log all commandline args
with open(os.path.join(args.logdir, 'args.txt'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
"""
Dataset
"""
train_set = ShapeNet_ODMS(root ='../../datasets/',categories = args.categories, \
download = True, high = 128, low = 32, split=.97, voxels = True)
dataloader_train = DataLoader(train_set, batch_size=args.batchsize, shuffle=True, \
num_workers=8)
valid_set = ShapeNet_ODMS(root ='../../datasets/',categories = args.categories, \
download = True, train = False, high = 128, low = 32, split=.97, voxels = True)
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False, \
num_workers=8)
"""
Model settings
"""
model = upscale(128, 32).to(args.device)
loss_fn = torch.nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Create log directory, if it doesn't already exist
args.logdir = os.path.join(args.logdir, args.expid)
if not os.path.isdir(args.logdir):
os.makedirs(args.logdir)
print('Created dir:', args.logdir)
# Log all commandline args
with open(os.path.join(args.logdir, 'args.txt'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
parser.add_argument('-categories',
type=str,
nargs='+',
default=['chair'],
help='list of object classes to use')
parser.add_argument('-batchsize', type=int, default=16, help='Batch size.')
args = parser.parse_args()
# Data
valid_set = ModelNet_ODMS(root ='../../datasets/',categories = ['chair'], \
download = True, train = False)
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False, \
num_workers=8)
# Model
model_res = upscale(30, 15)
model_res = model_res.to(args.device)
model_occ = upscale(30, 15)
model_occ = model_occ.to(args.device)
# Load saved weights
model_res.load_state_dict(torch.load('log/{0}/resbest.pth'.format(args.expid)))
model_occ.load_state_dict(torch.load('log/{0}/occbest.pth'.format(args.expid)))
iou_epoch = 0.
iou_NN_epoch = 0.
num_batches = 0
model_res.eval()
model_occ.eval()
with torch.no_grad():
for data in tqdm(dataloader_val):
parser.add_argument('-expid', type=str, default='MVD', help='Unique experiment identifier.')
parser.add_argument('-device', type=str, default='cuda', help='Device to use')
parser.add_argument('-categories', type=str,nargs='+', default=['chair'], help='list of object classes to use')
parser.add_argument('-vis', action='store_true', help='Visualize each model while evaluating')
parser.add_argument('-batchsize', type=int, default=16, help='Batch size.')
args = parser.parse_args()
# Data
valid_set = ShapeNet_ODMS(root ='../..//datasets',categories = args.categories, \
download = True, train = False, high = 128, low = 32, split=.97, voxels = True)
dataloader_val = DataLoader(valid_set, batch_size=args.batchsize, shuffle=False, \
num_workers=8)
# Model
model_res = upscale(128,32)
model_res = model_res.to(args.device)
model_occ = upscale(128,32)
model_occ = model_occ.to(args.device)
# Load saved weights
model_res.load_state_dict(torch.load('log/{0}/resbest.pth'.format(args.expid)))
model_occ.load_state_dict(torch.load('log/{0}/occbest.pth'.format(args.expid)))
iou_epoch = 0.
iou_NN_epoch = 0.
num_batches = 0
model_res.eval()
model_occ.eval()