学校网站建设注意点,网站图片多大合适,十大聊天软件排行榜,上海建设厅网站目录 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… 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 --port2001
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y3*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, dataformatsHWC) 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, dataformatsHWC) 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 modeRGB size512x512 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,dataformatsCHW) 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 requirelambda 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 \nformat_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, trainTrue, downloadTrue)
test_data torchvision.datasets.CIFAR10(./data, trainFalse, downloadTrue)
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 modeRGB size32x32 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, trainTrue, transformdata_transform1, downloadTrue)
test_data1 torchvision.datasets.CIFAR10(./data, trainFalse, transformdata_transform1, downloadTrue)
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_stepNone, walltimeNone, dataformatsCHW):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, dataformatsHWC)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, dataformatsdataformats), 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, trainTrue, transformdata_transform, downloadTrue)
test_data torchvision.datasets.CIFAR10(./data, trainFalse, transformdata_transform, downloadTrue)
# 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(datasettrain_data, shuffleTrue, batch_size64,)
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, dataformatsNCHW)
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_stepNone, walltimeNone, dataformatsNCHW):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, dataformatsdataformats), 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crtlp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
