wehub-resource-sync
65 lines
2.3 KiB
Python
65 lines
2.3 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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import numpy as np
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import nltk
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from nltk import word_tokenize
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import json
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import tensorrt as trt
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def preprocess(text):
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try:
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nltk.data.find("tokenizers/punkt_tab")
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except LookupError:
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nltk.download("punkt_tab")
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tokens = word_tokenize(text)
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# split into lower-case word tokens, in numpy array with shape of (seq, 1)
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words = np.asarray([w.lower() for w in tokens]).reshape(-1, 1)
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# split words into chars, in numpy array with shape of (seq, 1, 1, 16)
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chars = [[c for c in t][:16] for t in tokens]
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chars = [cs + [""] * (16 - len(cs)) for cs in chars]
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chars = np.asarray(chars).reshape(-1, 1, 1, 16)
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return words, chars
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def get_map_func(filepath):
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file = open(filepath)
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category_map = json.load(file)
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category_mapper = dict(
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zip(category_map["cats_strings"], category_map["cats_int64s"])
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)
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default_int64 = category_map["default_int64"]
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func = lambda s: category_mapper.get(s, default_int64)
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return np.vectorize(func)
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def get_inputs(context, query):
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cw, cc = preprocess(context)
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qw, qc = preprocess(query)
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context_word_func = get_map_func("CategoryMapper_4.json")
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context_char_func = get_map_func("CategoryMapper_5.json")
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query_word_func = get_map_func("CategoryMapper_6.json")
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query_char_func = get_map_func("CategoryMapper_7.json")
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cw_input = context_word_func(cw).astype(trt.nptype(trt.int32)).ravel()
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cc_input = context_char_func(cc).astype(trt.nptype(trt.int32)).ravel()
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qw_input = query_word_func(qw).astype(trt.nptype(trt.int32)).ravel()
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qc_input = query_char_func(qc).astype(trt.nptype(trt.int32)).ravel()
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return cw_input, cc_input, qw_input, qc_input
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