def infer_type(x: Any) -> str:
"""Infer the type of the input 'x'.
Parameters:
----------
x: Any python object
Returns:
----------
dtype: string, the proper data type of 'x':
1. 'graphgallery.floatx()' if 'x' is floating,
2. 'graphgallery.intx()' if 'x' is integer,
3. 'graphgallery.boolx()' if 'x' is boolean.
"""
# For tensor or variable
if pytorch.is_tensor(x):
if x.dtype.is_floating_point:
return gg.floatx()
elif x.dtype == torch.bool:
return gg.boolx()
elif 'int' in str(x.dtype):
return gg.intx()
else:
raise TypeError(f"Invalid type of pytorch Tensor: '{type(x)}'")
elif tensorflow.is_tensor(x):
if x.dtype.is_floating:
return gg.floatx()
elif x.dtype.is_integer or x.dtype.is_unsigned:
return gg.intx()
elif x.dtype.is_bool:
return gg.boolx()
else:
raise TypeError(f"Invalid type of tensorflow Tensor: '{type(x)}'")
_x = x
if not hasattr(_x, 'dtype'):
_x = np.asarray(_x)
if _x.dtype.kind in {'f', 'c'}:
return gg.floatx()
elif _x.dtype.kind in {'i', 'u'}:
return gg.intx()
elif _x.dtype.kind == 'b':
return gg.boolx()
elif _x.dtype.kind == 'O':
raise TypeError(f"Invalid inputs of '{x}'.")
else:
raise TypeError(f"Invalid input of '{type(x).__name__}'.")
def __init__(self, *, device="cpu", seed=None, name=None, **kwargs):
"""
Parameters:
----------
device: string. optional
The device where the model running on.
seed: interger scalar. optional
Used to create a reproducible sequence of tensors
across multiple calls.
name: string. optional
Specified name for the model. (default: :str: `class name`)
kwargs: other custom keyword arguments.
"""
# if graph is not None and not isinstance(graph, gg.data.BaseGraph):
# raise ValueError(f"Unrecognized graph: {graph}.")
kwargs.pop("self", None)
kwargs.pop("__class__", None)
cfg = gg.CfgNode()
cfg.merge_from_dict(kwargs)
cfg.intx = self.intx = gg.intx()
cfg.floatx = self.floatx = gg.floatx()
cfg.boolx = self.boolx = gg.boolx()
cfg.seed = self.seed = seed
cfg.name = self.name = name or self.__class__.__name__
cfg.device = device
_backend = gg.backend()
cfg.backend = getattr(_backend, "name", None)
if seed:
gf.random_seed(seed, _backend)
self.device = gf.device(device, _backend)
self.data_device = self.device
self.backend = _backend
# data types, default: `float32`,`int32` and `bool`
self._cache = gf.BunchDict()
self.transform = gf.BunchDict()
self._model = None
self._graph = None
self.cfg = cfg
self.setup_cfg()
self.custom_setup()
def __init__(self, *graph, device="cpu:0", seed=None, name=None, **kwargs):
"""
Parameters:
----------
graph: Graph or MultiGraph, or a dict of them.
device: string. optional
The device where the model running on.
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors
across multiple calls.
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other custom keyword parameters.
"""
graph = parse_graph_inputs(*graph)
_backend = gg.backend()
gg.utils.raise_error.raise_if_kwargs(kwargs)
if seed is not None:
np.random.seed(seed)
random.seed(seed)
if _backend == "torch":
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
else:
tf.random.set_seed(seed)
if name is None:
name = self.__class__.__name__
self.seed = seed
self.name = name
self.graph = graph.copy()
self.device = gg.functional.device(device, _backend)
self.backend = _backend
# data types, default: `float32`,`int32` and `bool`
self.floatx = gg.floatx()
self.intx = gg.intx()
self.boolx = gg.boolx()
def __init__(self, graph, device="cpu", seed=None, name=None, **kwargs):
"""
Parameters:
----------
graph: Graph or MultiGraph.
device: string. optional
The device where the model running on.
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors
across multiple calls.
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other custom keyword arguments.
"""
if not isinstance(graph, gg.data.BaseGraph):
raise ValueError(f"Unrecognized graph: {graph}.")
_backend = gg.backend()
# It currently takes no keyword arguments
gg.utils.raise_error.raise_if_kwargs(kwargs)
if seed:
gf.random_seed(seed, _backend)
if name is None:
name = self.__class__.__name__
self.seed = seed
self.name = name
self.graph = graph.copy()
self.device = gf.device(device, _backend)
self.backend = _backend
# data types, default: `float32`,`int32` and `bool`
self.floatx = gg.floatx()
self.intx = gg.intx()
self.boolx = gg.boolx()
self._cache = gf.BunchDict()
def index_to_mask(indices: np.ndarray, shape: tuple) -> np.ndarray:
mask = np.zeros(shape, dtype=gg.boolx())
mask[indices] = True
return mask