def _build_cell(self, cell_desc: CellDesc, rand_ops: RandOps) -> None:
# sanity check: we have random ops for each node
assert len(cell_desc.nodes()) == len(rand_ops.ops_and_ins)
reduction = (cell_desc.cell_type == CellType.Reduction)
# Add random op for each edge
for node, (op_names, to_states) in zip(cell_desc.nodes(),
rand_ops.ops_and_ins):
# as we want cell to be completely random, remove previous edges
node.edges.clear()
# add random edges
for op_name, to_state in zip(op_names, to_states):
op_desc = OpDesc(op_name,
params={
'conv':
cell_desc.conv_params,
'stride':
2 if reduction and to_state < 2 else 1
},
in_len=1,
trainables=None,
children=None)
edge = EdgeDesc(op_desc, input_ids=[to_state])
node.edges.append(edge)
def _build_cell(self, cell_desc: CellDesc) -> None:
reduction = (cell_desc.cell_type == CellType.Reduction)
self._ensure_nonempty_nodes(cell_desc)
# we operate on last node, inserting another node before it
new_nodes = [n.clone() for n in cell_desc.nodes()]
petridish_node = NodeDesc(edges=[])
new_nodes.insert(len(new_nodes) - 1, petridish_node)
input_ids = list(range(len(new_nodes))) # 2 + len-2
assert len(input_ids) >= 2
op_desc = OpDesc('petridish_reduction_op' if reduction else 'petridish_normal_op',
params={
'conv': cell_desc.conv_params,
# specify strides for each input, later we will
# give this to each primitive
'_strides':[2 if reduction and j < 2 else 1 \
for j in input_ids],
}, in_len=len(input_ids), trainables=None, children=None)
edge = EdgeDesc(op_desc, input_ids=input_ids)
petridish_node.edges.append(edge)
# note that post op will be recreated which means there is no
# warm start for post op when number of nodes changes
cell_desc.reset_nodes(new_nodes, cell_desc.node_ch_out,
cell_desc.post_op.name)
def build_cell(self, in_shapes:TensorShapesList, conf_cell:Config,
cell_index:int) ->CellDesc:
stem_shapes, stems = self.build_cell_stems(in_shapes, conf_cell, cell_index)
cell_type = self.get_cell_type(cell_index)
if self.template is None:
node_count = self.get_node_count(cell_index)
in_shape = stem_shapes[0] # input shape to noded is same as cell stem
out_shape = stem_shapes[0] # we ask nodes to keep the output shape same
node_shapes, nodes = self.build_nodes(stem_shapes, conf_cell,
cell_index, cell_type, node_count, in_shape, out_shape)
else:
node_shapes, nodes = self.build_nodes_from_template(stem_shapes, conf_cell, cell_index)
post_op_shape, post_op_desc = self.build_cell_post_op(stem_shapes,
node_shapes, conf_cell, cell_index)
cell_desc = CellDesc(
id=cell_index, cell_type=self.get_cell_type(cell_index),
conf_cell=conf_cell,
stems=stems, stem_shapes=stem_shapes,
nodes=nodes, node_shapes=node_shapes,
post_op=post_op_desc, out_shape=post_op_shape,
trainables_from=self.get_trainables_from(cell_index)
)
# output same shape twice to indicate s0 and s1 inputs for next cell
in_shapes.append([post_op_shape])
return cell_desc
def finalize_cell(self, cell:Cell, cell_index:int,
model_desc:ModelDesc, *args, **kwargs)->CellDesc:
# first finalize each node, we will need to recreate node desc with final version
max_final_edges = model_desc.max_final_edges
node_descs:List[NodeDesc] = []
dcell = self._divnas_cells[id(cell)]
assert len(cell.dag) == len(list(dcell.node_covs.values()))
for i,node in enumerate(cell.dag):
node_cov = dcell.node_covs[id(node)]
node_desc = self.finalize_node(node, i, cell.desc.nodes()[i],
max_final_edges, node_cov)
node_descs.append(node_desc)
# (optional) clear out all activation collection information
dcell.clear_collect_activations()
desc = cell.desc
finalized = CellDesc(
id = desc.id, cell_type=desc.cell_type, conf_cell=desc.conf_cell,
stems=[cell.s0_op.finalize()[0], cell.s1_op.finalize()[0]],
stem_shapes=desc.stem_shapes,
nodes = node_descs, node_shapes=desc.node_shapes,
post_op=cell.post_op.finalize()[0],
out_shape=desc.out_shape,
trainables_from = desc.trainables_from
)
return finalized
def _build_cell(self, cell_desc: CellDesc) -> None:
for i, node in enumerate(cell_desc.nodes()):
input_ids = []
first_proj = False # if input node is connected then it needs projection
if self._cell_matrix[0,
i + 1]: # nadbench internal node starts at 1
input_ids.append(0) # connect to s0
first_proj = True
for j in range(i): # look at all internal vertex before us
if self._cell_matrix[j + 1, i + 1]: # if there is connection
input_ids.append(j + 2) # offset because of s0, s1
op_desc = OpDesc(
'nasbench101_op',
params={
'conv': cell_desc.conv_params,
'stride': 1,
'vertex_op':
self._vertex_ops[i + 1], # offset because of input node
'first_proj': first_proj
},
in_len=len(input_ids),
trainables=None,
children=None) # TODO: should we pass children here?
edge = EdgeDesc(op_desc, input_ids=input_ids)
node.edges.append(edge)
def finalize_cell(self, cell: Cell, *args, **kwargs) -> CellDesc:
# first finalize each node, we will need to recreate node desc with final version
logger.info(f'cell id {cell.desc.id}')
node_descs: List[NodeDesc] = []
dcell = self._divnas_cells[cell]
assert len(cell.dag) == len(list(dcell.node_covs.values()))
for i, node in enumerate(cell.dag):
node_cov = dcell.node_covs[id(node)]
logger.info(f'node {i}')
node_desc = self.finalize_node(
node, cell.desc.max_final_edges, node_cov, cell, i)
node_descs.append(node_desc)
# (optional) clear out all activation collection information
dcell.clear_collect_activations()
finalized = CellDesc(
cell_type=cell.desc.cell_type,
id=cell.desc.id,
nodes=node_descs,
s0_op=cell.s0_op.finalize()[0],
s1_op=cell.s1_op.finalize()[0],
template_cell=cell.desc.template_cell,
max_final_edges=cell.desc.max_final_edges,
node_ch_out=cell.desc.node_ch_out,
post_op=cell.post_op.finalize()[0]
)
return finalized
def finalize_cell(self, cell: Cell, cell_index: int, model_desc: ModelDesc,
*args, **kwargs) -> CellDesc:
# first finalize each node, we will need to recreate node desc with final version
max_final_edges = model_desc.max_final_edges
node_descs: List[NodeDesc] = []
for i, node in enumerate(cell.dag):
node_desc = self.finalize_node(node, i,
cell.desc.nodes()[i],
max_final_edges)
node_descs.append(node_desc)
desc = cell.desc
finalized = CellDesc(
id=desc.id,
cell_type=desc.cell_type,
conf_cell=desc.conf_cell,
stems=[cell.s0_op.finalize()[0],
cell.s1_op.finalize()[0]],
stem_shapes=desc.stem_shapes,
nodes=node_descs,
node_shapes=desc.node_shapes,
post_op=cell.post_op.finalize()[0],
out_shape=desc.out_shape,
trainables_from=desc.trainables_from)
return finalized
def _build_cell(self, cell_desc:CellDesc)->None:
reduction = (cell_desc.cell_type==CellType.Reduction)
# add xnas op for each edge
for i, node in enumerate(cell_desc.nodes()):
for j in range(i+2):
op_desc = OpDesc('xnas_op',
params={
'conv': cell_desc.conv_params,
'stride': 2 if reduction and j < 2 else 1
}, in_len=1, trainables=None, children=None)
edge = EdgeDesc(op_desc, input_ids=[j])
node.edges.append(edge)
def __init__(self, desc: CellDesc, affine: bool, droppath: bool,
template_cell: Optional['Cell']
): # template cell, if any, to use for arch params
super().__init__()
# some of these members are public as finalizer needs access
self.desc = desc
self.s0_op = Op.create(desc.s0_op, affine=affine)
self.s1_op = Op.create(desc.s1_op, affine=affine)
self.dag = Cell._create_dag(desc.nodes(),
affine=affine,
droppath=droppath,
template_cell=template_cell)
self.post_op = Op.create(desc.post_op, affine=affine)
def __init__(self, desc:CellDesc,
affine:bool, droppath:bool,
trainables_from:Optional['Cell']): # template cell, if any, to use for arch params
super().__init__()
# some of these members are public as finalizer needs access
self.desc = desc
# TODO: support any number of stems
assert len(desc.stems)==2, "Cell compiler currently only supports 2 stems"
self.s0_op = Op.create(desc.stems[0], affine=affine)
self.s1_op = Op.create(desc.stems[1], affine=affine)
self.dag = Cell._create_dag(desc.nodes(),
affine=affine, droppath=droppath,
trainables_from=trainables_from)
self.post_op = Op.create(desc.post_op, affine=affine)
def _build_cell(self, cell_desc: CellDesc) -> None:
reduction = (cell_desc.cell_type == CellType.Reduction)
# add mixed op for each edge in each node
# how does the stride works? For all ops connected to s0 and s1, we apply
# reduction in WxH. All ops connected elsewhere automatically gets
# reduced WxH (because all subsequent states are derived from s0 and s1).
# Note that channel is increased via conv_params for the cell
for i, node in enumerate(cell_desc.nodes()):
for j in range(i + 2):
op_desc = OpDesc('mixed_op',
params={
'conv': cell_desc.conv_params,
'stride': 2 if reduction and j < 2 else 1
},
in_len=1,
trainables=None,
children=None)
edge = EdgeDesc(op_desc, input_ids=[j])
node.edges.append(edge)
def _ensure_nonempty_nodes(self, cell_desc: CellDesc):
assert len(cell_desc.nodes()) > 0
for node in cell_desc.nodes():
assert len(node.edges) > 0