def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
option: 'vevaluator.VEvalContext' = None,
line=-1):
funcArgs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, funcArgs, line)
if inst.in_container:
raise Exception('Invalid operation')
old_v = inst.get_value()
new_v = functions.generate_value_with_same_type(old_v)
# estimate a type contained
if old_v.has_constant_value():
new_v.internal_value = list(old_v.internal_value)
for v in funcArgs.inputs[1:]:
new_v.append(v)
# update value
inst.revise(new_v)
new_v.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([new_v])
graph.add_node(node)
return values.NoneValue()
def vcall(self, module: 'Field', graph: 'Graph', inst: 'values.ValueRef', args: 'functions.FunctionArgInput', line=-1):
funcArgs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, funcArgs, line)
graph.add_node(node)
#value = functions.generate_value_with_same_type(vargs[0])
value = self.ret_value_func()
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.ValueRef(value)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
context: 'functions.VEvalContext' = None,
line=-1):
funcArgs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, funcArgs, line)
graph.add_node(node)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
context: 'functions.VEvalContext' = None,
line=-1):
funcArgs = self.args.merge_inputs(inst, args)
vargs = funcArgs.get_value().inputs
node = nodes.NodeCall(self, funcArgs, line)
graph.add_node(node)
value = functions.generate_value_with_same_type(vargs[0])
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.Object(value)
def vcall(self,
module: 'Field',
graph: 'Graph',
inst: 'values.ValueRef',
args: 'functions.FunctionArgInput',
line=-1):
args = functions.FunctionArgInput()
args.inputs.append(inst)
args.keywords['self'] = inst
node = nodes.NodeCall(self, args, line)
value = values.TupleValue()
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
graph.add_node(node)
return values.ValueRef(value)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
context: 'functions.VEvalContext' = None,
line=-1):
node = nodes.NodeCall(self, args, line)
graph.add_node(node)
item = args.get_value().inputs[0]
default_value = None
# constant propagation whenever possible
if item.has_constant_value():
default_value = len(item.internal_value)
value = values.NumberValue(default_value)
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.Object(value)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
context: 'functions.VEvalContext' = None,
line=-1):
args = functions.FunctionArgInput()
args.inputs.append(inst)
args.keywords['self'] = inst
node = nodes.NodeCall(self, args, line)
value = values.NumberValue(None)
value.dtype = np.array(0).dtype
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
graph.add_node(node)
return values.Object(value)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
option: 'vevaluator.VEvalContext' = None,
line=-1):
chainer_link = chainer_links[type(self.owner.inst)]
if len(self.args.args_list) == 0:
if chainer_link.args is None:
self.args.add_arg('self', None)
self.args.add_arg('x', None)
else:
for arg in chainer_link.args:
self.args.add_arg(arg[0], arg[1])
vargs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, vargs, line)
graph.add_node(node)
if chainer_link.get_ret is not None:
ret = chainer_link.get_ret()
node.set_outputs(ret)
return values.Object(
values.TupleValue([values.Object(v) for v in ret]))
else:
value = values.TensorValue()
estimate_shape = chainer_links[type(
self.owner.inst)].estimate_shape
if estimate_shape is not None:
value.shape = estimate_shape(self.owner.inst, vargs)
node.set_outputs([value])
return values.Object(value)
def vcall(self,
module: 'values.Field',
graph: 'graphs.Graph',
inst: 'values.Object',
args: 'functions.FunctionArgInput',
context: 'functions.VEvalContext' = None,
line=-1):
funcArgs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, funcArgs, line)
graph.add_node(node)
axis = funcArgs.keywords['axis']
if isinstance(axis, values.NoneValue):
value = values.NumberValue(None)
value.dtype = np.int64
else:
value = values.TensorValue()
value.dtype = np.int64
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.Object(value)