def generate_value_with_same_type(value : 'values.Value'):
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
return ret
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
return ret
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
return ret
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
return ret
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
return ret
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
return ret
if isinstance(value, values.Value):
ret = values.Value()
return ret
return None
def generate_copied_value(value : 'values.Value'):
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.shape = value.shape
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if isinstance(value, values.BoolValue):
copied = values.BoolValue(value.internal_value)
return copied
if isinstance(value, values.StrValue):
copied = values.StrValue(value.internal_value)
return copied
if isinstance(value, values.RangeValue):
copied = values.RangeValue()
return copied
if isinstance(value, values.TupleValue):
copied = values.TupleValue(value.values)
return copied
if config.show_warnings:
print('Unknown type {} is copied'.format(value))
return values.Value()
def generate_value_with_same_type(value: 'values.Value'):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
ret.name = value.name + '_st'
return ret
def vcall(self,
module: 'Field',
graph: 'Graph',
inst: 'values.ValueRef',
args: 'functions.FunctionArgInput',
line=-1):
assert (inst is None)
funcArgs = self.args.merge_inputs(inst, args)
vargs = funcArgs.get_value().inputs
dtype_value = vargs[1]
if dtype_value is not None and not isinstance(dtype_value,
values.NoneValue):
# TODO : make better
dtype = utils.int_2_numpy_type(dtype_value.internal_value)
else:
dtype = np.array(vargs[1].internal_value).dtype
node = nodes.NodeGenerate('zeros', funcArgs, line)
graph.add_node(node)
value = values.TensorValue()
value.dtype = dtype
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.ValueRef(value)
def generate_value_with_same_type(value: 'values.Value'):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
if value.internal_value is None:
ret.dtype = value.dtype
elif isinstance(value.internal_value, int):
ret.dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
ret.dtype = np.array(value.internal_value).dtype
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
ret.name = value.name + '_st'
return ret
def vcall(self, module : 'Field', graph : 'Graph', inst : 'values.Object', args = [], line = -1):
funcArgs = self.parse_args(args)
vargs = self.get_values(funcArgs)
node = nodes.NodeCall(self, vargs, line)
graph.add_node(node)
#value = functions.generate_value_with_same_type(vargs[0])
value = values.TensorValue()
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.Object(value)
def generate_copied_value(value: 'values.Value'):
assert (isinstance(value, values.Value))
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
copied.dtype = value.dtype
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.value = value.value
copied.shape = value.shape
copied.dtype = value.dtype
return copied
if isinstance(value, values.ListValue):
copied = values.ListValue()
copied.is_any = value.is_any
if value.internal_value is not None:
copied.internal_value = value.internal_value.copy()
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if isinstance(value, values.BoolValue):
copied = values.BoolValue(value.internal_value)
return copied
if isinstance(value, values.StrValue):
copied = values.StrValue(value.internal_value)
return copied
if isinstance(value, values.RangeValue):
copied = values.RangeValue()
return copied
if isinstance(value, values.TupleValue):
if value.internal_value is not None:
copied = values.TupleValue(value.internal_value.copy())
else:
copied = values.TupleValue(value.internal_value)
return copied
if config.show_warnings:
print('Unknown type {} is copied'.format(value))
return values.Value()
def vcall(self,
module: 'values.Field',
graph: 'Graph',
inst: 'Object',
args: 'functions.FunctionArgInput',
line=-1):
node = nodes.NodeCall(self, [v.get_value() for v in args.inputs], line)
graph.add_node(node)
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, args)
node.set_outputs([value])
return values.Object(value)
def generate_copied_value(value : 'values.Value'):
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.shape = value.shape
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if config.show_warnings:
print('Warning : Unimplemented copied_value {}'.format(value))
return values.Value()
def vcall(self, module : 'Field', graph : 'Graph', inst : 'values.Object', args = [], line = -1):
funcArgs = self.parse_args(args)
vargs = self.get_values(funcArgs)
dtype_value = vargs[1]
if dtype_value is not None and not isinstance(dtype_value, values.NoneValue):
# TODO : make better
dtype = utils.int_2_numpy_type(dtype_value.internal_value)
else:
dtype = None
node = nodes.NodeGenerate('array', vargs, line)
graph.add_node(node)
value = values.TensorValue()
value.dtype = dtype
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.Object(value)
def vcall(self,
module: 'values.Field',
graph: 'Graph',
inst: 'values.ValueRef',
args: 'functions.FunctionArgInput',
line=-1):
vargs = self.args.merge_inputs(inst, args)
node = nodes.NodeCall(self, vargs, line)
graph.add_node(node)
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.ValueRef(value)
def vcall(self,
module: 'values.Field',
graph: 'Graph',
inst: 'Object',
args=[],
line=-1):
node = nodes.NodeCall(self, [v.obj.get_value() for v in args], line)
graph.add_node(node)
value = values.TensorValue()
# TODO refactor
if (isinstance(self.owner.inst, chainer.links.Linear)):
cn = self.owner.inst # type: chainer.links.Linear
if isinstance(args[0].obj.get_value(), values.TensorValue) and len(
args[0].obj.get_value().shape) >= 2:
value.shape = (args[0].obj.get_value().shape[0], cn.out_size)
if (isinstance(self.owner.inst, chainer.links.Convolution2D)):
value = functions.generate_tensor_value_with_undefined_shape_size(
args[0].obj.get_value())
node.set_outputs([value])
return values.Object(value)
def generate_tensor_value_with_undefined_shape_size(value : 'values.TensorValue'):
assert(isinstance(value, values.TensorValue))
ret = values.TensorValue()
ret.shape = tuple([-1 for v in value.shape])
return ret
def generate_value_with_same_type(value: 'values.Value',
is_dummy_value=False,
suffix_type=SuffixType.Unknown):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
ret.dtype = value.dtype
elif isinstance(value, values.NumberValue):
dtype = None
if value.internal_value is None:
dtype = value.dtype
elif isinstance(value.internal_value, int):
dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
dtype = np.array(value.internal_value).dtype
ret = values.NumberValue(None)
ret.dtype = dtype
elif isinstance(value, values.StrValue):
ret = values.StrValue(None)
elif isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
elif isinstance(value, values.ListValue):
ret = values.ListValue(None)
elif isinstance(value, values.NoneValue):
ret = values.NoneValue()
elif isinstance(value, values.TupleValue):
ret = values.TupleValue()
elif isinstance(value, values.RangeValue):
ret = values.RangeValue()
elif isinstance(value, values.UnknownValue):
ret = values.UnknownValue()
elif ret is None and isinstance(value, values.Value):
ret = values.Value()
else:
assert (False)
assert (ret is not None)
ret.is_dummy_value = is_dummy_value
if suffix_type == SuffixType.Unknown:
ret.name = value.name + '_st'
elif suffix_type == SuffixType.Unused:
ret.name = value.name + '_unused'
elif suffix_type == SuffixType.Dummy:
ret.name = value.name + '_dummy'
elif suffix_type == SuffixType.Input:
ret.name = value.name + '_in'
else:
assert (False)
return ret
def create_return_value_in_chainer_function():
return values.TensorValue()
def generate_value_with_same_type(value: 'values.Value',
has_default=False,
suffix_type=SuffixType.Unknown):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
ret.dtype = value.dtype
if isinstance(value, values.NumberValue):
dtype = None
if value.internal_value is None:
dtype = value.dtype
elif isinstance(value.internal_value, int):
dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
dtype = np.array(value.internal_value).dtype
if has_default:
if dtype == np.array(0).dtype:
ret = values.NumberValue(0)
elif dtype == np.array(0.0).dtype:
ret = values.NumberValue(0.0)
else:
ret = values.NumberValue(None)
else:
ret = values.NumberValue(None)
ret.dtype = dtype
if isinstance(value, values.StrValue):
if has_default:
ret = values.StrValue('')
else:
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
if has_default:
ret = values.BoolValue(False)
else:
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if isinstance(value, values.TupleValue):
ret = values.TupleValue()
if isinstance(value, values.UnknownValue):
ret = values.UnknownValue()
if has_default:
ret.internal_value = 0
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
if suffix_type == SuffixType.Unknown:
ret.name = value.name + '_st'
if suffix_type == SuffixType.Unused:
ret.name = value.name + '_unused'
return ret
