def __call__(self, parser, raw_node, node_scope=''):
nndct_node = Node(name=get_full_name(node_scope, raw_node.name),
dtype=convert_dtype(raw_node.dtype),
idx=raw_node.idx)
nndct_node.raw_kind = raw_node.kind
nndct_node.schema = raw_node.schema
nndct_node.is_custom_extension = raw_node.is_custom_pyop
nndct_node.caller = raw_node.pyobj
blob_tensor_convertor = TensorConvertor()
for op in raw_node.outputs:
nndct_tensor = blob_tensor_convertor(node_scope, op)
nndct_tensor.node = nndct_node
nndct_node.out_tensors.append(nndct_tensor)
parser.visited_blob_tensors[op.name] = nndct_tensor
for ip in raw_node.flatten_inputs:
if ip.name in parser.visited_blob_tensors:
nndct_node.in_tensors.append(
parser.visited_blob_tensors[ip.name])
elif ip.name in parser.visited_param_tensors:
parser.node_params[nndct_node].append(
parser.visited_param_tensors[ip.name])
nndct_node.in_tensors.append(
parser.visited_param_tensors[ip.name])
if not raw_node.inputs:
parser.node_input_args[nndct_node].append(
nndct_node.out_tensors[0])
else:
parser.node_input_args[nndct_node].extend(
[parser.get_nndct_value(i) for i in raw_node.inputs])
return nndct_node
def _convert_node(self, raw_node, scope=None):
if scope is None:
assert self.cur_graph
node_scope = self.cur_graph.name
else:
node_scope = scope
nndct_node = Node(
name=get_full_name(node_scope, raw_node.name),
dtype=self.convert_dtype(raw_node.dtype),
)
nndct_node.source_range = raw_node.source_range
nndct_node.scope_name = raw_node.scope_name
if nndct_node.name in self.cur_graph:
return self.cur_graph.node(nndct_node.name)
# nndct_node.raw_kind = raw_node.kind
# self.converted_node.add(raw_node)
nndct_node.schema = raw_node.schema
nndct_node.is_custom_extension = raw_node.is_custom_pyop
nndct_node.caller = raw_node.pyobj
nndct_node.owning_block = self.cur_block
nndct_node.owning_graph = self.cur_graph
for out in raw_node.outputs:
full_name = get_full_name(node_scope, out.name)
if self.cur_graph and self.cur_graph.is_tensor_in_graph(full_name):
nndct_node.add_out_tensor(self.cur_graph.tensor(full_name))
else:
nndct_tensor = self._convert_tensor(out, node_scope)
nndct_node.add_out_tensor(nndct_tensor)
for ip in raw_node.flatten_inputs:
full_name = get_full_name(node_scope, ip.name)
if self.cur_graph and self.cur_graph.is_tensor_in_graph(full_name):
nndct_node.add_in_tensor(self.cur_graph.tensor(full_name))
elif not raw_node.outputs:
# For Return node
nndct_tensor = self._convert_tensor(ip, node_scope)
nndct_node.add_in_tensor(nndct_tensor)
if self.cur_graph and full_name in self.cur_graph.param_names():
self.node_params[nndct_node].append(
self.cur_graph.tensor(full_name))
#from ipdb import set_trace
#set_trace()
node_input_args = []
if not raw_node.inputs:
node_input_args.extend(
[self.get_nndct_value(i) for i in raw_node.outputs])
else:
node_input_args.extend(
[self.get_nndct_value(i) for i in raw_node.inputs])
nndct_node.op = self._create_op(raw_node.kind, nndct_node,
node_input_args)
return nndct_node
