type=float,
help='the ratio/percentage of points selected to be labelled (0.01=1%, '
'0.05=5%, 0.1=10%, 1=100%)[default: 0.01]',
default=0.01)
args = parser.parse_args()
if args.GPU != -1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.GPU)
#### Parameters
Model = 'DGCNN_RandomSamp'
# m = 1 # the ratio of points selected to be labelled
##### Load Training/Testing Data
# Loader = IO.ShapeNetIO('../Dataset/ShapeNet',batchsize = args.batchsize)
Loader = IO.ShapeNetIO('/home/xuxun/Dropbox/Dataset/ShapeNet',
batchsize=args.batchsize)
Loader.LoadTestFiles()
##### Evaluation Object
Eval = Evaluation.ShapeNetEval()
## Number of categories
PartNum = Loader.NUM_PART_CATS
output_dim = PartNum
ShapeCatNum = Loader.NUM_CATEGORIES
#### Save Directories
dt = str(datetime.now().strftime("%Y%m%d"))
BASE_PATH = os.path.expanduser('../Results/{}/ShapeNet/'.format(dt))
SUMMARY_PATH = os.path.join(BASE_PATH, Model,
'Summary_m-{:.3f}'.format(args.m))
default=0.01)
parser.add_argument('--log_name', type=str, help='log_name', default='gary')
args = parser.parse_args()
if args.GPU != -1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.GPU)
#### Parameters
Model = 'DGCNN_RandomSamp'
# m = 1 # the ratio of points selected to be labelled
##### Load Training/Testing Data
# Loader = IO.ShapeNetIO('../Dataset/ShapeNet',batchsize = args.batchsize)
Loader = IO.ShapeNetIO(os.path.abspath('./Dataset/ShapeNet'),
batchsize=args.batchsize)
Loader.LoadTestFiles()
##### Evaluation Object
Eval = Evaluation.ShapeNetEval()
## Number of categories
PartNum = Loader.NUM_PART_CATS
output_dim = PartNum
ShapeCatNum = Loader.NUM_CATEGORIES
#### Save Directories
#dt = str(datetime.now().strftime("%Y%m%d"))
BASE_PATH = os.path.expanduser(
os.path.abspath('./Results/{}/ShapeNet/').format(args.log_name))
SUMMARY_PATH = os.path.join(BASE_PATH, Model,
default='Plain')
parser.add_argument(
'--Network',
'-net',
type=str,
help='Network used for training the network [default: DGCNN]'
' [options: DGCNN, PointNet++(not supported yet)]',
default='DGCNN')
args = parser.parse_args()
##### Set specified GPU to be active
if args.GPU != -1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.GPU)
##### Load Training/Testing Data
Loader = IO.ShapeNetIO('./Dataset/ShapeNet', batchsize=args.batchsize)
Loader.LoadTestFiles()
##### Evaluation Object
Eval = Evaluation.ShapeNetEval()
## Number of categories
PartNum = Loader.NUM_PART_CATS
output_dim = PartNum
ShapeCatNum = Loader.NUM_CATEGORIES
#### Save Directories
dt = '2020-05-20_13-16-50'
BASE_PATH = os.path.expanduser('./Results/ShapeNet/{}_sty-{}_m-{}_{}'.format(
args.Network, args.Style, args.m, dt))
SUMMARY_PATH = os.path.join(BASE_PATH, 'Summary')