为什么在运行encoder.json小模型时找不到GPT2

Question

晚上好,

警告，我不是蟒蛇或机器学习专家。

在大肆宣传之后，我正在尝试运行GPT2的小实例。到目前为止我已经下载了所有的先决条件。Python、regex、tensorflow等，但当涉及运行脚本从模型im生成示例时，将引发以下错误

‘’file "C:*****\F******y\Desktop\Python\gpt-2\src\encoder.py"，行109，在get_encoder with open中(os.path.join(models_dir，model_name，'encoder.json')，'r')作为f: FileNotFoundError: Errno 2没有这样的文件或目录：‘models\124m\encoder.json’“

当我调用脚本时，我切换到保存文件的目录，并从命令行运行‘’generate_unconditional_samples.py --top_k 40‘’“

脚本本身看起来如下所示

#!/usr/bin/env python3

import fire
import json
import os
import numpy as np
import tensorflow as tf

import model, sample, encoder

def sample_model(
    model_name='124M',
    seed=None,
    nsamples=0,
    batch_size=1,
    length=None,
    temperature=1,
    top_k=0,
    top_p=1,
    models_dir='U**r\F****y\Desktop\Python\gpt-2\models',
):
    """
    Run the sample_model
    :model_name=124M : String, which model to use
    :seed=None : Integer seed for random number generators, fix seed to
     reproduce results
    :nsamples=0 : Number of samples to return, if 0, continues to
     generate samples indefinately.
    :batch_size=1 : Number of batches (only affects speed/memory).
    :length=None : Number of tokens in generated text, if None (default), is
     determined by model hyperparameters
    :temperature=1 : Float value controlling randomness in boltzmann
     distribution. Lower temperature results in less random completions. As the
     temperature approaches zero, the model will become deterministic and
     repetitive. Higher temperature results in more random completions.
    :top_k=0 : Integer value controlling diversity. 1 means only 1 word is
     considered for each step (token), resulting in deterministic completions,
     while 40 means 40 words are considered at each step. 0 (default) is a
     special setting meaning no restrictions. 40 generally is a good value.
     :models_dir : path to parent folder containing model subfolders
     (i.e. contains the <model_name> folder)
    """
    models_dir = os.path.expanduser(os.path.expandvars(models_dir))
    enc = encoder.get_encoder(model_name, models_dir)
    hparams = model.default_hparams()
    with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
        hparams.override_from_dict(json.load(f))

    if length is None:
        length = hparams.n_ctx
    elif length > hparams.n_ctx:
        raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)

    with tf.Session(graph=tf.Graph()) as sess:
        np.random.seed(seed)
        tf.set_random_seed(seed)

        output = sample.sample_sequence(
            hparams=hparams, length=length,
            start_token=enc.encoder['<|endoftext|>'],
            batch_size=batch_size,
            temperature=temperature, top_k=top_k, top_p=top_p
        )[:, 1:]

        saver = tf.train.Saver()
        ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
        saver.restore(sess, ckpt)

        generated = 0
        while nsamples == 0 or generated < nsamples:
            out = sess.run(output)
            for i in range(batch_size):
                generated += batch_size
                text = enc.decode(out[i])
                print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
                print(text)

if __name__ == '__main__':
    fire.Fire(sample_model)

“”“

有人能告诉我可能做错了什么吗?我确信这是很明显的，但我已经尝试了大约4个小时，没有运气。

任何建议都是非常感谢的

Answer 1

在运行scrpt之前，您必须下载该模型：

python3 download_model.py 124M

“download_model.py”位于项目的根目录中。