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