Python matplotlib子地块非常慢

下面的代码

from torch.utils.data import DataLoader
from defect_segmentation.data_loading.DatasetSingleImage import dataset_single_image_default
import numpy as np


if __name__ == "__main__":
    import matplotlib.pyplot as plt

    def main():
        dataset = dataset_single_image_default()
        batch_size = 16
        shuffle = True
        num_workers = 0
        loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
        for i_batch, sample_batched in enumerate(loader):
            fig, axs = plt.subplots(int(np.sqrt(batch_size)), batch_size // int(np.sqrt(batch_size)))
            fig.suptitle(f"i_batch = {i_batch}")
            for i_sample, ax in zip(range(sample_batched.shape[0]), axs.flat):
                ax.set_title(f"Sample #{i_sample}")
                ax.axis("off")
                ax.imshow(sample_batched[i_sample, :, :])
                plt.pause(0.001)

    main()

工作和输出数据如下

这很好

问题是到了第10个数字时，填充这些数字变得非常缓慢。我不知道是什么原因造成的

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为完整起见，以下是创建数据集（跨越单个图像）的代码：

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是什么导致绘图速度变慢，以及如何修复它？

是否在每批之后关闭图形？如果所有数据仍处于“活动”状态-这可能会显著降低您的机器速度。尝试将图形写入磁盘（使用例如），并在每批之后关闭它们。这会影响速度吗？@Shai就是这样。你能解释一下为什么会这样吗？是否有一个使图保持打开的解决方法？打开的图（窗口）需要一些系统资源。如果您有许多打开的窗口，这相当于大量的系统资源@Gulzar@Shai这对我来说毫无意义，因为Windows可以轻松处理20多个窗口，但速度大约为10位数。不仅仅是窗口，还有所有图形元素：轴、标题、图例等。

from torch.utils.data import Dataset
from Utils.ConfigProvider import ConfigProvider
import cv2
import os
from overrides import overrides # pip install overrides


class DatasetSingleImage(Dataset):
    def __init__(self, image_path: str, sample_shape: tuple, strides: tuple):
        self._path = image_path
        assert os.path.isfile(self._path)
        self._im = cv2.imread(self._path)
        self._shape = self._im.shape
        self._rows, self._cols = self._shape[0], self._shape[1]

        self._sample_shape = sample_shape
        self._sample_rows, self._sample_cols = self._sample_shape[0], self._sample_shape[1]

        self._strides = strides
        self._stride_rows, self._stride_cols = self._strides[0], self._strides[1]
        # self._rows_start_range = range(0, self._rows, self._stride_rows)
        # self._cols_start_range = range(0, self._cols, self._stride_cols)

        self._rows_tuples_range = \
            [(c, min(c + self._sample_rows, self._rows)) for c in range(0, self._rows - self._sample_rows, self._stride_rows)]
        self._cols_tuples_range = \
            [(r, min(r + self._sample_cols, self._cols)) for r in range(0, self._cols - self._sample_cols, self._stride_cols)]

        self._n_strides_rows = len(self._rows_tuples_range)
        self._n_strides_cols = len(self._cols_tuples_range)
        self._total_strides = self._n_strides_rows * self._n_strides_cols

    def __len__(self):
        return self._total_strides

    @overrides # pip install overrides
    def __getitem__(self, ind):
        row_ind = ind // self._n_strides_cols
        col_ind = ind % self._n_strides_cols
        sample_x = self._rows_tuples_range[row_ind]
        sample_y = self._cols_tuples_range[col_ind]
        sample = self._im[sample_x[0]:sample_x[1], sample_y[0]:sample_y[1]]
        assert sample.shape[:2] == self._sample_shape
        return sample


def dataset_single_image_default():
    path = ConfigProvider.config().data.defective_inspected_path1
    sample_shape = (50, 50)
    strides = (25, 25)
    dataset = DatasetSingleImage(path, sample_shape, strides)
    return dataset