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