#
# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from polygraphy import mod, util
np = mod.lazy_import("numpy")
[docs]
@mod.export()
class PostprocessFunc:
"""
Provides functions that can apply post-processing to `IterationResult` s.
"""
[docs]
@staticmethod
# This function returns a top_k function that can be used as a postprocess_func.
def top_k(k=None):
"""
Creates a function that applies a Top-K operation to a IterationResult.
Top-K will return the indices of the k largest values in the array.
Args:
k (Union[int, Tuple[int, int], Dict[str, int], Dict[str, Tuple[int, int]]]):
The number of indices to keep and optionally the axis on which to operate.
For example, a value of ``(5, 0)`` would keep the top 5 indices along axis 0.
If this exceeds the axis length, it will be clamped.
This can be specified on a per-output basis by providing a dictionary. In that case,
use an empty string ("") as the key to specify default top-k value for outputs not explicitly listed.
If no default is present, unspecified outputs will not be modified.
Defaults to 10.
Returns:
Callable(IterationResult) -> IterationResult: The top-k function.
"""
k = util.default(k, 10)
axis = -1
# Top-K implementation.
def top_k_impl(iter_result):
for name, output in iter_result.items():
k_val = util.value_or_from_dict(k, name)
if k_val:
nonlocal axis
if util.is_sequence(k_val):
k_val, axis = k_val
iter_result[name] = util.array.topk(output, k_val, axis)[1]
return iter_result
return top_k_impl