深度学习入门-04

PS：基于小土堆视频学习
1、add_image()的使用
读取样本集中的数据`

image_path = "data/train/ants_image/0013035.jpg"
from PIL import Image
img = Image.open(image_path)

在pycharm的控制台中运行该代码，运行后可以发现：

数据格式是JPRG，不满足add_image()函数的要求

writer.add_image()

add_image()，在pycharm中通过ctrl单击后可以获取详细要求。代码定义要求如下

def add_image(
    self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"
):
    """Add image data to summary.

    Note that this requires the ``pillow`` package.

    Args:
        tag (str): Data identifier
        img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
        global_step (int): Global step value to record
        walltime (float): Optional override default walltime (time.time())
          seconds after epoch of event
        dataformats (str): Image data format specification of the form
          CHW, HWC, HW, WH, etc.
    Shape:
        img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
        convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
        Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
        corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.

    Examples::

        from torch.utils.tensorboard import SummaryWriter
        import numpy as np
        img = np.zeros((3, 100, 100))
        img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
        img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000

        img_HWC = np.zeros((100, 100, 3))
        img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
        img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000

        writer = SummaryWriter()
        writer.add_image(&#