from typing import List
from . import _dispatch
from ._classes import (
Labels,
TensorBlock,
TensorMap,
check_isinstance,
torch_jit_is_scripting,
)
[docs]
def drop_blocks(tensor: TensorMap, keys: Labels, copy: bool = False) -> TensorMap:
"""
Drop specified key/block pairs from a TensorMap.
:param tensor:
the TensorMap to drop the key-block pair from.
:param keys:
a :py:class:`Labels` object containing the keys of the blocks to drop
:param copy:
if :py:obj:`True`, the returned :py:class:`TensorMap` is constructed by
copying the blocks from the input `tensor`. If :py:obj:`False`
(default), the values of the blocks in the output :py:class:`TensorMap`
reference the same data as the input `tensor`. The latter can be useful
for limiting memory usage, but should be used with caution when
manipulating the underlying data.
:return:
the input :py:class:`TensorMap` with the specified key/block pairs
dropped.
"""
# Check arg types
if not torch_jit_is_scripting():
if not check_isinstance(tensor, TensorMap):
raise TypeError(
f"`tensor` must be a metatensor TensorMap, not {type(tensor)}"
)
if not check_isinstance(keys, Labels):
raise TypeError(f"`keys` must be a metatensor Labels, not {type(keys)}")
if not isinstance(copy, bool):
raise TypeError(f"`copy` must be a boolean, not {type(copy)}")
# Find the indices of keys to remove
tensor_keys = tensor.keys
_, to_remove, used_in_intersection = tensor_keys.intersection_and_mapping(keys)
to_remove_indices: List[int] = (_dispatch.where(to_remove != -1)[0]).tolist()
not_present_in_tensor = _dispatch.where(used_in_intersection == -1)[0]
if len(not_present_in_tensor) != 0:
key = keys.entry(not_present_in_tensor[0])
raise ValueError(f"{key.print()} is not present in this tensor")
# Create the new TensorMap
new_blocks: List[TensorBlock] = []
new_keys_values = []
for i in range(len(tensor_keys)):
if i in to_remove_indices:
continue
new_keys_values.append(tensor_keys.entry(i).values)
block = tensor[i]
if copy:
new_blocks.append(block.copy())
else:
# just increase the reference count on everything
new_block = TensorBlock(
values=block.values,
samples=block.samples,
components=block.components,
properties=block.properties,
)
for parameter, gradient in block.gradients():
if len(gradient.gradients_list()) != 0:
raise NotImplementedError(
"gradients of gradients are not supported"
)
new_block.add_gradient(
parameter=parameter,
gradient=TensorBlock(
values=gradient.values,
samples=gradient.samples,
components=gradient.components,
properties=new_block.properties,
),
)
new_blocks.append(new_block)
if len(new_keys_values) != 0:
new_keys = Labels(keys.names, _dispatch.stack(new_keys_values, 0))
else:
new_keys = Labels.empty(keys.names)
return TensorMap(keys=new_keys, blocks=new_blocks)