使用pytorch处理自己的数据集

目录

1 返回本地文件中的数据集

2 根据当前已有的数据集创建每一个样本数据对应的标签

3 tensorboard的使用

4 transforms处理数据

tranfroms.Totensor的使用

transforms.Normalize的使用

transforms.Resize的使用

transforms.Compose使用

5 dataset_transforms使用

---

1 返回本地文件中的数据集

在这个操作中，当前数据集的上一级目录就是当前所有同一数据的label

import os
from torch.utils.data import Dataset
from PIL import Image

class MyDataset(Dataset):
    def __init__(self, root_dir, label_dir):
        """
        :param root_dir: 根目录文件
        :param label_dir: 分类标签目录
        """
        self.root_dir = root_dir
        self.label_dir = label_dir
        self.path = os.path.join(root_dir, label_dir)
        self.image_path_list = os.listdir(self.path)
    def __getitem__(self, idx):
        """
        :param idx: idx是自己文件夹下的每一个图片索引
        :return: 返回每一个图片对象和其对应的标签，对于返回类型可以直接调用image.show显示或者用于后续图像处理
        """
        img_name = self.image_path_list[idx]
        ever_image_path = os.path.join(self.root_dir, self.label_dir, img_name)
        image = Image.open(ever_image_path)
        label = self.label_dir
        return image, label
    def __len__(self):
        return len(self.image_path_list)

root_dir = 'G:\python_files\深度学习代码库\cats_and_dogs_small\\train'
label_dir = 'cats'
my_data = MyDataset(root_dir, label_dir)
first_pic, label = my_data[0]   # 自动调用__getitem__(self, idx)
first_pic.show()
print("当前图片中动物所属label", label)

F:\Anaconda\envs\py38\python.exe G:/python_files/深度学习代码库/dataset/MyDataSet.py
当前图片中动物所属label cats

2 根据当前已有的数据集创建每一个样本数据对应的标签

import os
from torch.utils.data import Dataset
from PIL import Image

class MyLabelData:
    def __init__(self, root_dir, target_dir, label_dir, label_name):
        """
        :param root_dir: 根目录
        :param target_dir: 生成标签的目录
        :param label_dir: 要生成为标签目录名称
        :param label_name: 生成的标签名称
        """
        self.root_dir = root_dir
        self.target_dir = target_dir
        self.label_dir = label_dir
        self.label_name = label_name
        self.image_name_list = os.listdir(os.path.join(root_dir, target_dir))
    def label(self):
        for name in self.image_name_list:
            file_name = name.split(".jpg", 1)[0]
            label_path = os.path.join(self.root_dir, self.label_dir)
            if not os.path.exists(label_path):
                os.makedirs(label_path)
            with open(os.path.join(label_path, '{}'.format(file_name)), 'w') as f:
                f.write(self.label_name)
                f.close()
root_dir = 'G:\python_files\深度学习代码库\cats_and_dogs_small\\train'
target_dir = 'cats'
label_dir = 'cats_label'
label_name = 'cat'
label = MyLabelData(root_dir, target_dir, label_dir, label_name)
label.label()

这样上面的代码中的训练集目录下的每一个样本都会在train的cats_label目录下创建其对应的分类标签

每一个标签中文件中都有一个cat字符串或者其他动物的分类名称，以确定它到底是哪一个动物

3 tensorboard的使用

# tensorboard --logdir=深度学习代码库/logs --port=2001
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('logs')
for i in range(100):
    writer.add_scalar('当前的函数表达式y=3*x',i*3,i)
writer.close()
#-----------------------------------------------------------
import numpy as np
from PIL import Image
image_PIL = Image.open('G:\python_files\深度学习代码库\cats_and_dogs_small\\train\cats\cat.1.jpg')
image_numpy = np.array(image_PIL)
print(type(image_numpy))
print(image_numpy.shape)
writer.add_image('cat图片', image_numpy,2, dataformats='HWC')

这里使用tensorboard的作用是为了更好的展示数据，但是对于函数的使用，比如上面的add_image中的参数，最好的方式是点击源码查看其对应的参数类型，然后根据实际需要将它所需的数据类型丢给add_image就好，而在源码中该函数的参数中所要求的图片类型必须是tensor类型或者是numpy，所以想要使用tensorboard展示数据就首先必须使用numpy或者使用transforms.Totensor将其转化为tensor，然后丢给add_image函数

还有一个需要注意的是，使用add_image函数，图片的tensor类型或者numpy类型必须和dataformats的默认数据类型一样，否则根据图片的数据类型修改后面的额dataformatas就好

4 transforms处理数据

tranfroms.Totensor的使用

import numpy as np
from torchvision import transforms
from PIL import Image
tran = transforms.ToTensor()
PIL_image = Image.open('G:\python_files\深度学习代码库\\cats\cat\cat.11.jpg')
tensor_pic = tran(PIL_image)
print(tensor_pic)
print(tensor_pic.shape)
from torch.utils.tensorboard import SummaryWriter
write = SummaryWriter('logs')
write.add_image('Tensor_picture',tensor_pic)

tensor([[[0.9216, 0.9059, 0.8353,  ..., 0.2392, 0.2275, 0.2078],
         [0.9765, 0.9216, 0.8118,  ..., 0.2431, 0.2392, 0.2235],
         [0.9490, 0.8745, 0.7608,  ..., 0.2471, 0.2471, 0.2314],
         ...,
         [0.3490, 0.4902, 0.6667,  ..., 0.7804, 0.7804, 0.7804],
         [0.3412, 0.4431, 0.5216,  ..., 0.7765, 0.7922, 0.7882],
         [0.3490, 0.4510, 0.5294,  ..., 0.7765, 0.7922, 0.7882]],

        [[0.9451, 0.9294, 0.8706,  ..., 0.2980, 0.2863, 0.2667],
         [1.0000, 0.9451, 0.8471,  ..., 0.3020, 0.2980, 0.2824],
         [0.9725, 0.8980, 0.7961,  ..., 0.2980, 0.2980, 0.2824],
         ...,
         [0.3725, 0.5137, 0.6902,  ..., 0.8431, 0.8431, 0.8431],
         [0.3647, 0.4667, 0.5451,  ..., 0.8392, 0.8549, 0.8510],
         [0.3608, 0.4627, 0.5412,  ..., 0.8392, 0.8549, 0.8510]],

        [[0.9294, 0.9137, 0.8588,  ..., 0.2235, 0.2118, 0.1922],
         [0.9922, 0.9373, 0.8353,  ..., 0.2275, 0.2235, 0.2078],
         [0.9725, 0.8980, 0.7922,  ..., 0.2275, 0.2275, 0.2118],
         ...,
         [0.4196, 0.5608, 0.7373,  ..., 0.9412, 0.9412, 0.9333],
         [0.4196, 0.5216, 0.6000,  ..., 0.9373, 0.9529, 0.9412],
         [0.4196, 0.5216, 0.6000,  ..., 0.9373, 0.9529, 0.9412]]])
torch.Size([3, 410, 431])

transforms.Normalize的使用

# 对应三个通道，每一个通道一个平均值和方差
# output[channel] = (input[channel] - mean[channel]) / std[channel]
nor = transforms.Normalize([0.5, 0.5, 0.5],[10, 0.5, 0.5])
print(tensor_pic[0][0][0])
x_nor = nor(tensor_pic)
write.add_image('nor_picture：', x_nor)
print(tensor_pic[0][0][0])
write.close()

打开源码查看

def forward(self, tensor: Tensor) -> Tensor:
    """
    Args:
        tensor (Tensor): Tensor image to be normalized.

    Returns:
        Tensor: Normalized Tensor image.
    """
    return F.normalize(tensor, self.mean, self.std, self.inplace)

必须传入的是tensor数据类型

transforms.Resize的使用

size_tensor = transforms.Resize((512,512))
# 裁剪tensor
tensor_pic_size = size_tensor(tensor_pic)
# 裁剪Image
size_pic = transforms.Resize((512,512))
image_size = size_pic(PIL_image)
print(image_size)
write.add_image('tensor_pic_size',tensor_pic_size)
print(tensor_pic_size.shape)
np_image = np.array(image_size)
print('np_image.shape：', np_image.shape)
write.add_image('image_size', np_image, dataformats='HWC')

调用Resize的时候，需要传入的数据类型的要求，查看源码如下

def forward(self, img):
    """
    Args:
        img (PIL Image or Tensor): Image to be scaled.

    Returns:
        PIL Image or Tensor: Rescaled image.
    """
    return F.resize(img, self.size, self.interpolation)

<PIL.Image.Image image mode=RGB size=512x512 at 0x1A72B1E7D00>
torch.Size([3, 512, 512])
np_image.shape： (512, 512, 3)

transforms.Compose使用

nor = transforms.Normalize([0.5, 0.5, 0.5],[10, 0.5, 0.5])
trans_resize_2 = transforms.Resize((64,64))
trans_to_tensor = transforms.ToTensor()
trans_compose = transforms.Compose([trans_resize_2, trans_to_tensor])
tensor_pic_compose = trans_compose(PIL_image)
write.add_image('tensor_pic_compose',tensor_pic_compose,dataformats='CHW')

class Compose:
    """Composes several transforms together. This transform does not support torchscript.
    Please, see the note below.

    Args:
        transforms (list of ``Transform`` objects): list of transforms to compose.

    Example:
        >>> transforms.Compose([
        >>>     transforms.CenterCrop(10),
        >>>     transforms.ToTensor(),
        >>> ])

    .. note::
        In order to script the transformations, please use ``torch.nn.Sequential`` as below.

        >>> transforms = torch.nn.Sequential(
        >>>     transforms.CenterCrop(10),
        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        >>> )
        >>> scripted_transforms = torch.jit.script(transforms)

        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
        `lambda` functions or ``PIL.Image``.

    """

    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, img):
        for t in self.transforms:
            img = t(img)
        return img

    def __repr__(self):
        format_string = self.__class__.__name__ + '('
        for t in self.transforms:
            format_string += '\n'
            format_string += '    {0}'.format(t)
        format_string += '\n)'
        return format_string

5 dataset_transforms使用

from torch.utils.data import DataLoader
from torchvision import  transforms
import torchvision
data_transform = transforms.Compose([transforms.ToTensor()])
train_data = torchvision.datasets.CIFAR10('./data', train=True, download=True)
test_data = torchvision.datasets.CIFAR10('./data', train=False, download=True)

print("train_data", train_data)
# 原始的数据集中每一条数据中包含以一张图片和该图片所属的类别
print("train_data[0]", train_data[0])
print("train_data.classes", train_data.classes)
image, label = train_data[0]
print("label ",label)
image.show()
print("train_data.classes[label]", train_data.classes[label])

train_data Dataset CIFAR10
    Number of datapoints: 50000
    Root location: ./data
    Split: Train
train_data[0] (<PIL.Image.Image image mode=RGB size=32x32 at 0x144ED58D970>, 6)
train_data.classes ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
label  6
train_data.classes[label] frog

#%%
from torchvision import transforms
import torchvision
# 将整个数据集转化为tensor类型
data_transform1 = transforms.Compose([transforms.ToTensor()])
train_data = torchvision.datasets.CIFAR10('./data', train=True, transform=data_transform1, download=True)
test_data1 = torchvision.datasets.CIFAR10('./data', train=False, transform=data_transform1, download=True)
from torch.utils.tensorboard import SummaryWriter
write = SummaryWriter('batch_picture')
for i in range(10):
    tensor_pic, label = train_data[i]  # 经过前面的transforms成了tensor
    print(tensor_pic.shape)
    write.add_image('batch_picture', tensor_pic, i)
write.close()

Files already downloaded and verified
Files already downloaded and verified
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])

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 (string): Data identifier
        img_tensor (torch.Tensor, numpy.array, 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
    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('my_image', img, 0)

        # If you have non-default dimension setting, set the dataformats argument.
        writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
        writer.close()

    Expected result:

    .. image:: _static/img/tensorboard/add_image.png
       :scale: 50 %

    """
    torch._C._log_api_usage_once("tensorboard.logging.add_image")
    if self._check_caffe2_blob(img_tensor):
        from caffe2.python import workspace
        img_tensor = workspace.FetchBlob(img_tensor)
    self._get_file_writer().add_summary(
        image(tag, img_tensor, dataformats=dataformats), global_step, walltime)

from torchvision import transforms
import torchvision
# 将整个数据集转化为tensor类型
data_transform = transforms.Compose([transforms.ToTensor()])
train_data = torchvision.datasets.CIFAR10('./data', train=True, transform=data_transform, download=True)
test_data = torchvision.datasets.CIFAR10('./data', train=False, transform=data_transform, download=True)
# dataLoad会将原始数据中一个batch中的图片和图片的Label分别放在一起，形成对应
train_data_load = DataLoader(dataset=train_data, shuffle=True, batch_size=64,)
from torch.utils.tensorboard import SummaryWriter
write = SummaryWriter('dataLoad')
# 遍历整个load，一次遍历的图片是64个
for batch_id, data in enumerate(train_data_load):
    # 经过DataLoda之后，每一个批次返回一批图片和该图片对应的标签类别
    print('data',data)
    batch_image, batch_label = data
    print("batch_id",batch_id)
    print("image.shape", batch_image.shape)
    print("label.shape", batch_label.shape)
    write.add_images('batch_load_picture', batch_image, batch_id, dataformats='NCHW')
write.close()

其中一个批次的输出结果展示
batch_id 646
image.shape torch.Size([64, 3, 32, 32])
label.shape torch.Size([64])
data [tensor([[[[0.2510, 0.3804, 0.5176,  ..., 0.5529, 0.5451, 0.2980],
          [0.2706, 0.6000, 0.6667,  ..., 0.5686, 0.3961, 0.1176],
          [0.2745, 0.6627, 0.6980,  ..., 0.3961, 0.1608, 0.0824],
          ...,
          [0.6863, 0.6824, 0.5333,  ..., 0.2941, 0.4863, 0.5059],
          [0.5804, 0.6784, 0.4902,  ..., 0.1451, 0.2824, 0.3451],
          [0.4353, 0.4353, 0.5098,  ..., 0.1373, 0.1529, 0.2902]],

         [[0.3020, 0.4549, 0.6078,  ..., 0.6627, 0.6353, 0.3608],
          [0.3451, 0.6980, 0.7765,  ..., 0.6745, 0.4706, 0.1647],
          [0.3490, 0.7529, 0.8039,  ..., 0.4667, 0.2000, 0.1137],
          ...,
          [0.8196, 0.8157, 0.6157,  ..., 0.3608, 0.5529, 0.5804],
          [0.7137, 0.8039, 0.5686,  ..., 0.1922, 0.3373, 0.4078],
          [0.5412, 0.5333, 0.5765,  ..., 0.1765, 0.2000, 0.3490]],

         [[0.3098, 0.5490, 0.7412,  ..., 0.8314, 0.7373, 0.3765],
          [0.3765, 0.8392, 0.9569,  ..., 0.7686, 0.4941, 0.1216],
          [0.3843, 0.9176, 1.0000,  ..., 0.4627, 0.1490, 0.0588],
          ...,
          [0.9843, 0.9922, 0.7373,  ..., 0.3882, 0.6353, 0.7255],
          [0.8039, 0.9373, 0.6745,  ..., 0.1804, 0.3647, 0.4941],
          [0.6471, 0.6549, 0.6980,  ..., 0.1569, 0.2000, 0.3961]]],


        [[[0.9608, 0.9490, 0.9529,  ..., 0.8314, 0.8196, 0.8235],
          [0.9255, 0.9216, 0.9333,  ..., 0.8275, 0.8196, 0.8235],
          [0.9137, 0.9137, 0.9294,  ..., 0.8392, 0.8314, 0.8353],
          ...,
          [0.4118, 0.4353, 0.4431,  ..., 0.4157, 0.4431, 0.4275],
          [0.4667, 0.4667, 0.4627,  ..., 0.3961, 0.3804, 0.3882],
          [0.4392, 0.4235, 0.4235,  ..., 0.5490, 0.4471, 0.4706]],

         [[0.9647, 0.9529, 0.9529,  ..., 0.8745, 0.8667, 0.8667],
          [0.9294, 0.9255, 0.9333,  ..., 0.8627, 0.8549, 0.8549],
          [0.9137, 0.9176, 0.9294,  ..., 0.8627, 0.8588, 0.8549],
          ...,
          [0.4196, 0.4392, 0.4471,  ..., 0.4314, 0.4627, 0.4510],
          [0.4745, 0.4745, 0.4706,  ..., 0.4078, 0.4039, 0.4118],
          [0.4471, 0.4314, 0.4314,  ..., 0.5608, 0.4667, 0.4863]],

         [[0.9765, 0.9686, 0.9647,  ..., 0.9412, 0.9373, 0.9569],
          [0.9451, 0.9412, 0.9529,  ..., 0.9216, 0.9216, 0.9373],
          [0.9451, 0.9451, 0.9569,  ..., 0.9176, 0.9176, 0.9333],
          ...,
          [0.4078, 0.4314, 0.4353,  ..., 0.4353, 0.4706, 0.4588],
          [0.4627, 0.4627, 0.4588,  ..., 0.4118, 0.4118, 0.4157],
          [0.4353, 0.4196, 0.4196,  ..., 0.5569, 0.4627, 0.4863]]],


        [[[0.9569, 0.9569, 0.9647,  ..., 0.8510, 0.8353, 0.8235],
          [0.9569, 0.9569, 0.9608,  ..., 0.8627, 0.8431, 0.8392],
          [0.9804, 0.9725, 0.9725,  ..., 0.8745, 0.8627, 0.8549],
          ...,
          [0.3725, 0.3882, 0.3922,  ..., 0.3647, 0.3725, 0.3686],
          [0.3882, 0.4000, 0.4157,  ..., 0.3882, 0.3804, 0.3608],
          [0.3882, 0.4000, 0.4118,  ..., 0.3725, 0.3608, 0.3490]],

         [[0.9608, 0.9608, 0.9686,  ..., 0.8706, 0.8549, 0.8392],
          [0.9608, 0.9608, 0.9686,  ..., 0.8784, 0.8549, 0.8510],
          [0.9843, 0.9765, 0.9804,  ..., 0.8863, 0.8745, 0.8627],
          ...,
          [0.3804, 0.3922, 0.3961,  ..., 0.3255, 0.3529, 0.3686],
          [0.3961, 0.4078, 0.4235,  ..., 0.3647, 0.3686, 0.3647],
          [0.3961, 0.4078, 0.4196,  ..., 0.3843, 0.3686, 0.3569]],

         [[0.9843, 0.9765, 0.9804,  ..., 0.9294, 0.9176, 0.9137],
          [0.9804, 0.9686, 0.9725,  ..., 0.9216, 0.9059, 0.9098],
          [0.9961, 0.9804, 0.9765,  ..., 0.9137, 0.9098, 0.9098],
          ...,
          [0.3725, 0.3882, 0.3922,  ..., 0.2902, 0.3255, 0.3686],
          [0.3922, 0.4039, 0.4196,  ..., 0.3412, 0.3490, 0.3608],
          [0.3922, 0.4039, 0.4157,  ..., 0.3843, 0.3686, 0.3529]]],


        ...,


        [[[0.8902, 0.8863, 0.8824,  ..., 0.8314, 0.8392, 0.8353],
          [0.8902, 0.8863, 0.8863,  ..., 0.8353, 0.8431, 0.8392],
          [0.8902, 0.8863, 0.8902,  ..., 0.8392, 0.8431, 0.8431],
          ...,
          [0.9569, 0.9529, 0.9569,  ..., 0.5765, 0.5843, 0.5961],
          [0.9686, 0.9647, 0.9608,  ..., 0.9412, 0.9255, 0.9255],
          [0.9804, 0.9765, 0.9725,  ..., 0.9255, 0.9176, 0.9176]],

         [[0.9176, 0.9137, 0.9098,  ..., 0.8667, 0.8745, 0.8706],
          [0.9176, 0.9137, 0.9137,  ..., 0.8706, 0.8784, 0.8745],
          [0.9176, 0.9137, 0.9176,  ..., 0.8784, 0.8824, 0.8784],
          ...,
          [0.9608, 0.9569, 0.9608,  ..., 0.6392, 0.6667, 0.6706],
          [0.9765, 0.9725, 0.9647,  ..., 0.9608, 0.9765, 0.9725],
          [0.9882, 0.9843, 0.9804,  ..., 0.9255, 0.9451, 0.9490]],

         [[0.9412, 0.9373, 0.9333,  ..., 0.9255, 0.9333, 0.9294],
          [0.9412, 0.9373, 0.9373,  ..., 0.9294, 0.9373, 0.9333],
          [0.9412, 0.9373, 0.9412,  ..., 0.9294, 0.9333, 0.9333],
          ...,
          [0.9686, 0.9647, 0.9686,  ..., 0.6667, 0.6824, 0.6863],
          [0.9725, 0.9686, 0.9647,  ..., 0.9804, 0.9804, 0.9804],
          [0.9843, 0.9804, 0.9765,  ..., 0.9373, 0.9451, 0.9490]]],


        [[[0.1725, 0.1725, 0.1804,  ..., 0.1255, 0.1255, 0.1255],
          [0.1922, 0.1882, 0.1843,  ..., 0.1333, 0.1373, 0.1333],
          [0.1961, 0.1922, 0.1882,  ..., 0.1412, 0.1412, 0.1333],
          ...,
          [0.4471, 0.4902, 0.5137,  ..., 0.5647, 0.5725, 0.5961],
          [0.4431, 0.4706, 0.4824,  ..., 0.5608, 0.5529, 0.5569],
          [0.4275, 0.4431, 0.4392,  ..., 0.6078, 0.5608, 0.5176]],

         [[0.0980, 0.0980, 0.1059,  ..., 0.0353, 0.0353, 0.0392],
          [0.1137, 0.1137, 0.1098,  ..., 0.0431, 0.0471, 0.0471],
          [0.1216, 0.1176, 0.1137,  ..., 0.0549, 0.0549, 0.0549],
          ...,
          [0.2471, 0.2824, 0.3529,  ..., 0.5490, 0.5451, 0.5608],
          [0.2510, 0.2980, 0.3765,  ..., 0.5569, 0.5294, 0.5255],
          [0.2471, 0.3059, 0.3765,  ..., 0.6078, 0.5451, 0.4902]],

         [[0.0431, 0.0431, 0.0510,  ..., 0.0118, 0.0118, 0.0118],
          [0.0588, 0.0588, 0.0549,  ..., 0.0118, 0.0118, 0.0118],
          [0.0667, 0.0627, 0.0588,  ..., 0.0118, 0.0118, 0.0118],
          ...,
          [0.2431, 0.2745, 0.3176,  ..., 0.5373, 0.5608, 0.5804],
          [0.2510, 0.2824, 0.3294,  ..., 0.5490, 0.5412, 0.5412],
          [0.2510, 0.2863, 0.3216,  ..., 0.6000, 0.5529, 0.4980]]],


        [[[0.6353, 0.6314, 0.6314,  ..., 0.6157, 0.6157, 0.6157],
          [0.6353, 0.6314, 0.6314,  ..., 0.6157, 0.6157, 0.6157],
          [0.6353, 0.6314, 0.6314,  ..., 0.6157, 0.6157, 0.6157],
          ...,
          [0.6471, 0.6431, 0.6431,  ..., 0.6392, 0.6392, 0.6392],
          [0.6471, 0.6431, 0.6431,  ..., 0.6392, 0.6392, 0.6392],
          [0.6471, 0.6431, 0.6431,  ..., 0.6392, 0.6392, 0.6392]],

         [[0.7804, 0.7765, 0.7765,  ..., 0.7725, 0.7725, 0.7686],
          [0.7804, 0.7765, 0.7765,  ..., 0.7725, 0.7725, 0.7686],
          [0.7804, 0.7765, 0.7765,  ..., 0.7725, 0.7725, 0.7686],
          ...,
          [0.7922, 0.7882, 0.7882,  ..., 0.7843, 0.7843, 0.7843],
          [0.7922, 0.7882, 0.7882,  ..., 0.7843, 0.7843, 0.7843],
          [0.7922, 0.7882, 0.7882,  ..., 0.7843, 0.7843, 0.7843]],

         [[0.9882, 0.9804, 0.9843,  ..., 0.9765, 0.9765, 0.9765],
          [0.9882, 0.9804, 0.9843,  ..., 0.9765, 0.9765, 0.9765],
          [0.9882, 0.9804, 0.9843,  ..., 0.9765, 0.9765, 0.9765],
          ...,
          [0.9961, 0.9882, 0.9922,  ..., 0.9882, 0.9882, 0.9882],
          [0.9961, 0.9882, 0.9922,  ..., 0.9882, 0.9882, 0.9882],
          [0.9961, 0.9882, 0.9922,  ..., 0.9882, 0.9882, 0.9882]]]]), tensor([2, 8, 9, 6, 9, 3, 8, 3, 7, 7, 7, 3, 9, 2, 3, 1, 0, 1, 9, 6, 7, 6, 7, 9,
        1, 1, 8, 9, 2, 7, 5, 0, 1, 5, 9, 4, 2, 5, 7, 6, 3, 2, 2, 9, 4, 2, 1, 1,
        9, 5, 2, 5, 0, 8, 1, 7, 3, 5, 8, 0, 5, 0, 5, 0])]

使用add_images对所有批次的数据进行展示

def add_images(self, tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW'):
    """Add batched image data to summary.

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

    Args:
        tag (string): Data identifier
        img_tensor (torch.Tensor, numpy.array, 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 (string): Image data format specification of the form
          NCHW, NHWC, CHW, HWC, HW, WH, etc.
    Shape:
        img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
        accepted. e.g. NCHW or NHWC.

    Examples::

        from torch.utils.tensorboard import SummaryWriter
        import numpy as np

        img_batch = np.zeros((16, 3, 100, 100))
        for i in range(16):
            img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
            img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i

        writer = SummaryWriter()
        writer.add_images('my_image_batch', img_batch, 0)
        writer.close()

    Expected result:

    .. image:: _static/img/tensorboard/add_images.png
       :scale: 30 %

    """
    torch._C._log_api_usage_once("tensorboard.logging.add_images")
    if self._check_caffe2_blob(img_tensor):
        from caffe2.python import workspace
        img_tensor = workspace.FetchBlob(img_tensor)
    self._get_file_writer().add_summary(
        image(tag, img_tensor, dataformats=dataformats), global_step, walltime)

在使用add_images时要注意默认的通道数是3，如果经过卷积层以后的图片通道数大于3，那么是无法使用该函数进行显示的，会显示断言错误的信息，所以此时要使用torch.reshape将通道数变为3，然后可以正常调用

对于还未涉及的方法也是这样，查看其对应的参数类型（使用crtl+p，或者直接crtl+鼠标点击相应的函数查看源码），将所需要的参数类型丢给它使用就好文章来源地址https://www.toymoban.com/news/detail-740291.html

到了这里，关于使用pytorch处理自己的数据集的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！