def init_model(self): if self.hyper_params['init_fbp']: fbp = fbp_op( self.non_normed_ray_trafo, filter_type=self.hyper_params['init_filter_type'], frequency_scaling=self.hyper_params['init_frequency_scaling']) if self.normalize_by_opnorm: fbp = OperatorRightScalarMult(fbp, self.opnorm) self.init_mod = OperatorModule(fbp) else: self.init_mod = None self.model = PrimalDualNet( n_iter=self.niter, op=self.ray_trafo_mod, op_adj=self.ray_trafo_adj_mod, op_init=self.init_mod, n_primal=self.hyper_params['nprimal'], n_dual=self.hyper_params['ndual'], use_sigmoid=self.hyper_params['use_sigmoid'], internal_ch=self.hyper_params['internal_ch'], kernel_size=self.hyper_params['kernel_size'], batch_norm=self.hyper_params['batch_norm'], prelu=self.hyper_params['prelu'], lrelu_coeff=self.hyper_params['lrelu_coeff']) def weights_init(m): if isinstance(m, torch.nn.Conv2d): m.bias.data.fill_(0.0) torch.nn.init.xavier_uniform_(m.weight) self.model.apply(weights_init) if self.use_cuda: # WARNING: using data-parallel here doesn't work because of astra-gpu self.model = self.model.to(self.device)
def __init__(self, func, scalar): """Initialize a new instance. Parameters ---------- func : `Functional` The functional which will have its argument scaled. scalar : float, nonzero The scaling parameter with which the argument is scaled. """ if not isinstance(func, Functional): raise TypeError('`func` {!r} is not a `Functional` instance' ''.format(func)) scalar = func.domain.field.element(scalar) Functional.__init__(self, space=func.domain, linear=func.is_linear, grad_lipschitz=( np.abs(scalar) * func.grad_lipschitz)) OperatorRightScalarMult.__init__(self, operator=func, scalar=scalar)
def __init__(self, func, scalar): """Initialize a new instance. Parameters ---------- func : `Functional` The functional which will have its argument scaled. scalar : float, nonzero The scaling parameter with which the argument is scaled. """ if not isinstance(func, Functional): raise TypeError('`func` {!r} is not a `Functional` instance' ''.format(func)) scalar = func.domain.field.element(scalar) Functional.__init__(self, space=func.domain, linear=func.is_linear, grad_lipschitz=( np.abs(scalar) * func.grad_lipschitz)) OperatorRightScalarMult.__init__(self, operator=func, scalar=scalar)
def init_model(self): self.op_mod = OperatorModule(self.op) self.op_adj_mod = OperatorModule(self.op.adjoint) partial0 = odl.PartialDerivative(self.op.domain, axis=0) partial1 = odl.PartialDerivative(self.op.domain, axis=1) self.reg_mod = OperatorModule(partial0.adjoint * partial0 + partial1.adjoint * partial1) if self.hyper_params['init_fbp']: fbp = fbp_op( self.non_normed_op, filter_type=self.hyper_params['init_filter_type'], frequency_scaling=self.hyper_params['init_frequency_scaling']) if self.normalize_by_opnorm: fbp = OperatorRightScalarMult(fbp, self.opnorm) self.init_mod = OperatorModule(fbp) else: self.init_mod = None self.model = IterativeNet(n_iter=self.niter, n_memory=5, op=self.op_mod, op_adj=self.op_adj_mod, op_init=self.init_mod, op_reg=self.reg_mod, use_sigmoid=self.hyper_params['use_sigmoid'], n_layer=self.hyper_params['nlayer'], internal_ch=self.hyper_params['internal_ch'], kernel_size=self.hyper_params['kernel_size'], batch_norm=self.hyper_params['batch_norm'], prelu=self.hyper_params['prelu'], lrelu_coeff=self.hyper_params['lrelu_coeff']) def weights_init(m): if isinstance(m, torch.nn.Conv2d): m.bias.data.fill_(0.0) if self.hyper_params['init_weight_xavier_normal']: torch.nn.init.xavier_normal_( m.weight, gain=self.hyper_params['init_weight_gain']) self.model.apply(weights_init) if self.use_cuda: # WARNING: using data-parallel here doesn't work, probably # astra_cuda is not thread-safe self.model = self.model.to(self.device)