wehub-resource-sync
124 lines
5.0 KiB
Python
124 lines
5.0 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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import argparse
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import os
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import numpy as np
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from paddle import inference
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from scipy.special import softmax
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from paddlenlp.transformers import AutoTokenizer
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from paddlenlp.utils.env import (
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PADDLE_INFERENCE_MODEL_SUFFIX,
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PADDLE_INFERENCE_WEIGHTS_SUFFIX,
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)
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_dir", required=True, help="The directory of model.")
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parser.add_argument("--model_prefix", type=str, default="model", help="Prefix of the model file (no extension).")
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parser.add_argument("--device", choices=["gpu", "cpu"], default="cpu", help="Device for inference.")
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parser.add_argument("--device_id", type=int, default=0, help="GPU device ID if using GPU.")
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parser.add_argument("--cpu_threads", type=int, default=4, help="CPU threads if using CPU.")
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parser.add_argument("--batch_size", type=int, default=2, help="Batch size for inference.")
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parser.add_argument("--max_length", type=int, default=128, help="Max sequence length.")
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return parser.parse_args()
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def batchfy_text(texts, batch_size):
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return [texts[i : i + batch_size] for i in range(0, len(texts), batch_size)]
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class Predictor(object):
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def __init__(self, args):
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self.batch_size = args.batch_size
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self.max_length = args.max_length
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self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir)
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model_file = os.path.join(args.model_dir, args.model_prefix + f"{PADDLE_INFERENCE_MODEL_SUFFIX}")
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params_file = os.path.join(args.model_dir, args.model_prefix + f"{PADDLE_INFERENCE_WEIGHTS_SUFFIX}")
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if not os.path.exists(model_file):
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raise FileNotFoundError(f"Model file not found: {model_file}")
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if not os.path.exists(params_file):
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raise FileNotFoundError(f"Params file not found: {params_file}")
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config = inference.Config(model_file, params_file)
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if args.device == "gpu":
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config.enable_use_gpu(100, args.device_id)
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else:
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config.disable_gpu()
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config.set_cpu_math_library_num_threads(args.cpu_threads)
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config.switch_use_feed_fetch_ops(False)
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self.predictor = inference.create_predictor(config)
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self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()]
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self.output_handle = self.predictor.get_output_handle(self.predictor.get_output_names()[0])
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def preprocess(self, texts):
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encoded = self.tokenizer(
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texts,
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padding=True,
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truncation=True,
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max_length=self.max_length,
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return_token_type_ids=True,
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)
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input_ids = np.array(encoded["input_ids"], dtype="int64")
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token_type_ids = np.array(encoded["token_type_ids"], dtype="int64")
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return input_ids, token_type_ids
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def infer(self, input_ids, token_type_ids):
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self.input_handles[0].copy_from_cpu(input_ids)
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self.input_handles[1].copy_from_cpu(token_type_ids)
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self.predictor.run()
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return self.output_handle.copy_to_cpu()
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def postprocess(self, logits):
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probs = softmax(logits, axis=1)
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return {"label": probs.argmax(axis=1), "confidence": probs}
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def predict(self, texts):
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input_ids, token_type_ids = self.preprocess(texts)
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logits = self.infer(input_ids, token_type_ids)
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return self.postprocess(logits)
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if __name__ == "__main__":
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args = parse_arguments()
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predictor = Predictor(args)
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texts_ds = [
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"against shimmering cinematography that lends the setting the ethereal beauty of an asian landscape painting",
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"the situation in a well-balanced fashion",
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"at achieving the modest , crowd-pleasing goals it sets for itself",
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"so pat it makes your teeth hurt",
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"this new jangle of noise , mayhem and stupidity must be a serious contender for the title .",
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]
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label_map = {0: "negative", 1: "positive"}
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batch_texts = batchfy_text(texts_ds, args.batch_size)
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for bs, texts in enumerate(batch_texts):
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outputs = predictor.predict(texts)
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for i, sentence in enumerate(texts):
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label = outputs["label"][i]
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confidence = outputs["confidence"][i]
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print(
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f"Batch id: {bs}, example id: {i}, sentence: {sentence}, "
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f"label: {label_map[label]}, negative prob: {confidence[0]:.4f}, positive prob: {confidence[1]:.4f}."
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)
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