具體的應(yīng)用場景是將BERT的CKPT轉(zhuǎn)化為pytorch的model。
把下面的代碼保存為transfer.py
比如下述訓(xùn)練好的check point。

執(zhí)行：transfer.py model.ckpt-2543 model/rf-bert 前面是訓(xùn)練第2543次的check point 后面是保存的路徑，就會得到：

transfer.py:

import tensorflow as tf
import deepdish as dd
import argparse
import os
import numpy as np

def tr(v):
    # tensorflow weights to pytorch weights
    if v.ndim == 4:
        return np.ascontiguousarray(v.transpose(3,2,0,1))
    elif v.ndim == 2:
        return np.ascontiguousarray(v.transpose())
    return v

def read_ckpt(ckpt):
    # https://github.com/tensorflow/tensorflow/issues/1823
    reader = tf.train.NewCheckpointReader(ckpt)
    weights = {n: reader.get_tensor(n) for (n, _) in reader.get_variable_to_shape_map().items()}
    pyweights = {k: tr(v) for (k, v) in weights.items()}
    return pyweights
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Converts ckpt weights to deepdish hdf5")
    parser.add_argument("infile", type=str,
                        help="Path to the ckpt.")  # ***model.ckpt-22177***
    parser.add_argument("outfile", type=str, nargs='?', default='',
                        help="Output file (inferred if missing).")
    args = parser.parse_args()
    if args.outfile == '':
        args.outfile = os.path.splitext(args.infile)[0] + '.h5'
    outdir = os.path.dirname(args.outfile)
    if not os.path.exists(outdir):
        os.makedirs(outdir)
    weights = read_ckpt(args.infile)
    dd.io.save(args.outfile, weights)
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版權(quán)聲明：本文為CSDN博主「 楊楊」的原創(chuàng)文章，遵循 CC 4.0 BY-SA 版權(quán)協(xié)議，轉(zhuǎn)載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/weixin_42699651/article/details/88932670