from deepmd.infer.deep_tensor import DeepTensor
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from pathlib import Path
[docs]class DeepWFC(DeepTensor):
"""Constructor.
Parameters
----------
model_file : Path
The name of the frozen model file.
load_prefix: str
The prefix in the load computational graph
default_tf_graph : bool
If uses the default tf graph, otherwise build a new tf graph for evaluation
Warnings
--------
For developers: `DeepTensor` initializer must be called at the end after
`self.tensors` are modified because it uses the data in `self.tensors` dict.
Do not chanage the order!
"""
def __init__(
self, model_file: "Path", load_prefix: str = "load", default_tf_graph: bool = False
) -> None:
# use this in favor of dict update to move attribute from class to
# instance namespace
self.tensors = dict(
{
# output tensor
"t_tensor": "o_wfc:0",
},
**self.tensors
)
DeepTensor.__init__(
self,
model_file,
load_prefix=load_prefix,
default_tf_graph=default_tf_graph,
)
[docs] def get_dim_fparam(self) -> int:
"""Unsupported in this model."""
raise NotImplementedError("This model type does not support this attribute")
[docs] def get_dim_aparam(self) -> int:
"""Unsupported in this model."""
raise NotImplementedError("This model type does not support this attribute")