如何修复sketch_rnn算法中的“ allow_pickle = False时无法加载对象数组”

Question

我在jupyter笔记本上运行sketch_rnn.ipynb时，在加载环境以加载训练后的数据集时，返回错误“ allow_pickle = False时无法加载对象数组”

这是Google开发人员在开发sketch_rnn算法（甚至在google colab中运行）时已经使用的代码。过去我自己在google colab上运行过它，但是它似乎无法在我自己的jupyter笔记本上运行

from magenta.models.sketch_rnn.sketch_rnn_train import *
from magenta.models.sketch_rnn.model import *
from magenta.models.sketch_rnn.utils import *
from magenta.models.sketch_rnn.rnn import * 

model_params.batch_size = 1
eval_model_params = sketch_rnn_model.copy_hparams(model_params)
eval_model_params.use_input_dropout = 0
eval_model_params.use_recurrent_dropout = 0
eval_model_params.use_output_dropout = 0
eval_model_params.is_training = 0
sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
sample_model_params.max_seq_len = 1
return [model_params, eval_model_params, sample_model_params]


[train_set, valid_set, test_set, hps_model, eval_hps_model, 
sample_hps_model] = load_env_compatible(data_dir, model_dir)

我期望输出是

INFO:tensorflow:Downloading http://github.com/hardmaru/sketch-rnn- 
datasets/raw/master/aaron_sheep/aaron_sheep.npz
INFO:tensorflow:Loaded 7400/300/300 from aaron_sheep.npz
INFO:tensorflow:Dataset combined: 8000 (7400/300/300), avg len 125
INFO:tensorflow:model_params.max_seq_len 250.
total images <= max_seq_len is 7400
total images <= max_seq_len is 300
total images <= max_seq_len is 300
INFO:tensorflow:normalizing_scale_factor 18.5198.

但这给了我

ValueError: Object arrays cannot be loaded when allow_pickle=False

Answer 1

使用 allow_pickle=True 作为 np.load() 的参数之一。

Answer 2

这段代码解决了我这边的问题。

# Downgrate numpy to fix a problem
!pip install numpy==1.16.2
import numpy as np
print(np.__version__)

我只是将numpy降级，因为问题是由于某些内部冲突引起的。

Answer 3

所以我认为这只是由于numpy对load（）的更改而浮出水面，如果您观察到发生错误的行，它引用的是类似

    with np.load(path) as f:
        x_train, labels_train = f['x_train'], f['y_train']
        x_test, labels_test = f['x_test'], f['y_test']

但是Keras源代码，例如在第58行：https : //github.com/keras-team/keras/blob/master/keras/datasets/imdb.py

现在使用

    with np.load(path, allow_pickle=True) as f:
        x_train, labels_train = f['x_train'], f['y_train']
        x_test, labels_test = f['x_test'], f['y_test']

其中，np.load(path)成为np.load(path, boolean)

从简短的阅读中可以看出，的增加pickles与安全性有关，因为它pickles可以包含将在加载某些内容时运行的任意Python代码。（可能类似于执行SQL注入的方式）

用新的参数列表更新np.load之后，它就可以用于我的项目了