def __init__(self, loss_type="SYM", verbose=True):
self.loss_type = loss_type
# Define the loss type
if self.loss_type == "SYM":
if verbose:
my_utils.cyan_print("Using SYM chamfer")
self.forward = chamferL2
elif self.loss_type == "ASSYM_1":
if verbose:
my_utils.cyan_print("Using ASSYM_1 chamfer")
self.forward = chamferL2_assym1
elif self.loss_type == "ASSYM_2":
if verbose:
my_utils.cyan_print("Using ASSYM_2 chamfer")
self.forward = chamferL2_assym2
else:
my_utils.cyan_print("please provide a loss type")
def parser():
parser = argparse.ArgumentParser()
# Training parameters
parser.add_argument('--batch_size',
type=int,
default=64,
help='input batch size')
parser.add_argument('--workers',
type=int,
help='number of data loading workers',
default=24)
parser.add_argument('--nepoch',
type=int,
default=35,
help='number of epochs to train for')
parser.add_argument('--start_epoch',
type=int,
default=66,
help='number of epochs to train for')
parser.add_argument(
'--randomize',
type=int,
default=0,
help='if 1, projects predicted correspondences point on target mesh')
parser.add_argument('--lrate',
type=float,
default=0.001,
help='learning rate')
parser.add_argument('--lr_decay_1',
type=int,
default=80,
help='learning rate decay 1')
parser.add_argument('--lr_decay_2',
type=int,
default=90,
help='learning rate decay 2')
# Data
parser.add_argument('--number_points',
type=int,
default=2500,
help='Number of point sampled on the object')
parser.add_argument('--faust',
type=str,
default="INTER",
choices=["INTER", "INTRA"],
help='Faust eval')
# Save dirs and reload
parser.add_argument('--id', type=str, default="0", help='training name')
parser.add_argument('--env',
type=str,
default="CODED",
help='visdom environment')
parser.add_argument('--display',
type=int,
default=1,
help='visdom environment')
parser.add_argument('--port', type=int, default=8889, help='visdom port')
parser.add_argument('--dataset_path',
type=str,
default='./data/MPI-FAUST/test/scans/',
help='inference dataset')
parser.add_argument('--dir_name',
type=str,
default="./log/6_layer_with_bn/",
help='saving trained model to')
# Network
parser.add_argument('--model',
type=str,
default="./log/6_layer_with_bn/network.pth",
help='optional reload model path')
parser.add_argument('--point_translation',
type=int,
default=0,
help='point_translation')
parser.add_argument('--dim_template',
type=int,
default=3,
help='dim_template')
parser.add_argument('--patch_deformation',
type=int,
default=1,
help='patch_deformation')
parser.add_argument('--dim_out_patch',
type=int,
default=3,
help='dim_out_patch')
parser.add_argument('--template_mode',
type=str,
default="TEMPLATE",
choices=["TEMPLATE, SOUP, TRAINDATA, SPHERE"],
help='dim_out_patch')
# Loss
parser.add_argument(
"--accelerated_chamfer",
type=int,
default=1,
help="use custom build accelarated chamfer",
)
# Eval parameters
parser.add_argument('--HR',
type=int,
default=0,
help='Use high Resolution template')
parser.add_argument('--LR_input',
type=int,
default=1,
help='Use Low Resolution Input ')
parser.add_argument('--reg_num_steps',
type=int,
default=3000,
help='number of regression steps')
parser.add_argument('--inputA',
type=str,
default="data/example_0.ply",
help='your path to mesh 0')
parser.add_argument('--inputB',
type=str,
default="data/example_1.ply",
help='your path to mesh 1')
parser.add_argument(
'--num_angles',
type=int,
default=100,
help=
'number of angle in the search of optimal reconstruction. Set to 1, if you mesh are already facing the cannonical direction as in data/example_1.ply'
)
parser.add_argument(
'--clean',
type=int,
default=1,
help='if 1, remove points that dont belong to any edges')
parser.add_argument(
'--scale',
type=int,
default=1,
help='if 1, scale input mesh to have same volume as the template')
parser.add_argument(
'--project_on_target',
type=int,
default=0,
help='if 1, projects predicted correspondences point on target mesh')
parser.add_argument(
'--uniformize',
type=int,
default=1,
help='if 1, scale input mesh to have same volume as the template')
opt = parser.parse_args()
opt.HR = my_utils.int_2_boolean(opt.HR)
opt.LR_input = my_utils.int_2_boolean(opt.LR_input)
opt.clean = my_utils.int_2_boolean(opt.clean)
opt.scale = my_utils.int_2_boolean(opt.scale)
opt.project_on_target = my_utils.int_2_boolean(opt.project_on_target)
opt.randomize = my_utils.int_2_boolean(opt.randomize)
opt.accelerated_chamfer = my_utils.int_2_boolean(opt.accelerated_chamfer)
opt.display = my_utils.int_2_boolean(opt.display)
opt.point_translation = my_utils.int_2_boolean(opt.point_translation)
opt.patch_deformation = my_utils.int_2_boolean(opt.patch_deformation)
opt.uniformize = my_utils.int_2_boolean(opt.uniformize)
opt.date = str(datetime.datetime.now())
now = datetime.datetime.now()
if opt.dir_name == "":
opt.dir_name = os.path.join('log', opt.id + now.isoformat())
else:
print("Modifying input arguments to match network in dirname")
with open(os.path.join(opt.dir_name, "options.json"), 'r') as f:
my_opt_dict = json.load(f)
opt.point_translation = my_opt_dict["point_translation"]
opt.dim_template = my_opt_dict["dim_template"]
opt.patch_deformation = my_opt_dict["patch_deformation"]
opt.dim_out_patch = my_opt_dict["dim_out_patch"]
opt.start_epoch = my_opt_dict["start_epoch"]
my_utils.cyan_print("PARAMETER: ")
for a in my_opt_dict:
print(" " + colored(a, "yellow") + " : " +
colored(str(my_opt_dict[a]), "cyan"))
return opt
def print_loss_info(self):
my_utils.cyan_print("LOSS")
my_utils.cyan_print(" lambda_chamfer : " + str(self.opt.lambda_chamfer))
my_utils.cyan_print(" chamfer_loss_type : " + str(self.opt.chamfer_loss_type))
my_utils.cyan_print(" lambda_cycle_2 : " + str(self.opt.lambda_cycle_2))
my_utils.cyan_print(" lambda_cycle_3 : " + str(self.opt.lambda_cycle_3))
my_utils.cyan_print(" lambda_reconstruct : " + str(self.opt.lambda_reconstruct))