def __init__(self, op_desc:OpDesc, arch_params: Optional[ArchParams],
reduction:bool, affine:bool):
super().__init__()
# assume last PRIMITIVE is 'none' (this is used for finalize)
assert PetridishOp.PRIMITIVES[-1] == 'none'
# create edges for the op, each edge connects input state,
# within each edge we will have all N primitives
self._edges = nn.ModuleList()
for i in range(op_desc.in_len):
# edge contains all primitives with alphas
edge = nn.ModuleList()
self._edges.append(edge)
# for each input stride could be different,
# so we will make copy of our params and then set stride for this input
params = deepcopy(op_desc.params)
params['stride'] = op_desc.params['_strides'][i]
# create primitives for the edge
for primitive in PetridishOp.PRIMITIVES:
primitive_op = Op.create(OpDesc(primitive, params=params,
in_len=1, trainables=None),
affine=affine, arch_params=None)
# wrap primitive with sg
op = nn.Sequential(StopGradient(), primitive_op)
edge.append(op)
# TODO: check with Dey: Do we really need StopForwardReductionOp
# or StopGradientReductionOp because these two will only make sense
# for cell stems.
# NOTE: Consider the case where prev_prev is normal, prev is reduction
# then s_0 is twice as big in each dimension as s_1 and the number of channels
# won't match. So you have to use StopGradientReductionOp on s_1 to make it match.
self._sf = StopForward()
# we do this at the end so that we can capture all arch params registered by
# any previous child modules
self._setup_arch_params(arch_params, op_desc.in_len)
def __init__(self, op_desc: OpDesc, arch_params: Optional[ArchParams],
affine: bool):
super().__init__()
# assume last PRIMITIVE is 'none'
assert XnasOp.PRIMITIVES[-1] == 'none'
self._ops = nn.ModuleList()
for primitive in XnasOp.PRIMITIVES:
op = Op.create(OpDesc(primitive,
op_desc.params,
in_len=1,
trainables=None),
affine=affine,
arch_params=None)
self._ops.append(op)
# for getting gradients to non-leaf node
self._grad = None
# we do this at the end so that we can capture all arch params registered by
# any previous child modules
self._setup_arch_params(arch_params)
def __init__(self, op_desc: OpDesc, arch_params: Optional[ArchParams],
affine: bool):
super().__init__()
# assume last PRIMITIVE is 'none'
assert DivOp.PRIMITIVES[-1] == 'none'
conf = get_conf()
trainer = conf['nas']['search']['divnas']['archtrainer']
finalizer = conf['nas']['search']['finalizer']
if trainer == 'noalpha' and finalizer == 'default':
raise NotImplementedError(
'noalpha trainer is not implemented for the default finalizer')
if trainer != 'noalpha':
self._setup_arch_params(arch_params)
else:
self._alphas = None
self._ops = nn.ModuleList()
for primitive in DivOp.PRIMITIVES:
op = Op.create(OpDesc(primitive,
op_desc.params,
in_len=1,
trainables=None),
affine=affine,
arch_params=None)
self._ops.append(op)
# various state variables for diversity
self._collect_activations = False
self._forward_counter = 0
self._batch_activs = None
self._indices_of_notallowed()
self._create_mapping_valid_to_orig()
def pre_build(self, conf_model_desc: Config) -> None:
Op.register_op(
'div_op', lambda op_desc, arch_params, affine: DivOp(
op_desc, arch_params, affine))
def register_ops(self) -> None:
Op.register_op(
'gs_op', lambda op_desc, arch_params, affine: GsOp(
op_desc, arch_params, affine))
def _stem_reductions(stems:List[OpDesc])->List[int]:
# create stem ops to find out reduction factors
ops = [Op.create(stem, affine=False) for stem in stems]
assert all(isinstance(op, StemBase) for op in ops)
return list(op.reduction for op in ops)
def pre_build(self, conf_model_desc: Config) -> None:
Op.register_op(
'nasbench101_op',
lambda op_desc, arch_params, affine: NasBench101Op(
op_desc, arch_params, affine))
def register_ops(self) -> None:
Op.register_op(
'nasbench101_op',
lambda op_desc, arch_params, affine: NasBench101Op(
op_desc, arch_params, affine))
def register_ops(self) -> None:
Op.register_op(
'mixed_op', lambda op_desc, arch_params, affine: MixedOp(
op_desc, arch_params, affine))
