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