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 Imageclass 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_dirself.label_dir = label_dirself.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_dirreturn image, labeldef __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 Imageclass 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_dirself.target_dir = target_dirself.label_dir = label_dirself.label_name = label_nameself.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 = transformsdef __call__(self, img):for t in self.transforms:img = t(img)return imgdef __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tensorprint(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 identifierimg_tensor (torch.Tensor, numpy.array, or string/blobname): Image dataglobal_step (int): Global step value to recordwalltime (float): Optional override default walltime (time.time())seconds after epoch of eventShape:img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` toconvert 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 ascorresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.Examples::from torch.utils.tensorboard import SummaryWriterimport numpy as npimg = np.zeros((3, 100, 100))img[0] = np.arange(0, 10000).reshape(100, 100) / 10000img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000img_HWC = np.zeros((100, 100, 3))img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000writer = 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 workspaceimg_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 = dataprint("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 identifierimg_tensor (torch.Tensor, numpy.array, or string/blobname): Image dataglobal_step (int): Global step value to recordwalltime (float): Optional override default walltime (time.time())seconds after epoch of eventdataformats (string): Image data format specification of the formNCHW, NHWC, CHW, HWC, HW, WH, etc.Shape:img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will beaccepted. e.g. NCHW or NHWC.Examples::from torch.utils.tensorboard import SummaryWriterimport numpy as npimg_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 * iimg_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * iwriter = 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 workspaceimg_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
注解的作用)
)
