def __init__(self, args, inputs, gt):
self.args = args
self.features = encoders[args.ENCODER_ID](inputs, args)
if not hasattr(args, 'enc_params'):
args.enc_params = num_params()
self.outputs = self.create_decoder(self.features)
self.dist1, self.dist2, self.loss, self.emd_cost = self.create_loss(
self.outputs, gt)
def __init__(self, args, inputs, gt):
self.args = args
self.num_coarse = args.num_coarse
self.grid_size = args.grid_size
self.num_fine = (self.grid_size**2) * self.num_coarse
self.features = create_pcn_encoder(inputs, args)
if not hasattr(args, 'enc_params'):
args.enc_params = num_params()
self.coarse, self.fine = self.create_decoder(self.features)
self.dist1, self.dist2, self.loss, self.emd_cost = self.create_loss(
self.coarse, self.fine, gt)
self.outputs = self.fine
def __init__(self, args, inputs, gt):
self.args = args
self.grid_size = args.grid_size
self.grid_scale = 0.5
self.num_output_points = args.npts
self.features = create_pcn_encoder(inputs, args)
if not hasattr(args, 'enc_params'):
args.enc_params = num_params()
fold1, fold2 = self.create_decoder(self.features)
self.outputs = fold2
self.dist1, self.dist2, self.loss, self.emd_cost = self.create_loss(
self.outputs, gt)
def PCN_create_model(args):
create_multigpu_model(PCN, args)
args.nparams = num_params()
print('Total number of parameters: {}'.format(args.nparams))
def Folding_create_model(args):
create_multigpu_model(Folding, args)
args.nparams = num_params()
print('Total number of parameters: {}'.format(args.nparams))
return None
