目錄
1 返回本地文件中的數(shù)據(jù)集
2 根據(jù)當(dāng)前已有的數(shù)據(jù)集創(chuàng)建每一個(gè)樣本數(shù)據(jù)對(duì)應(yīng)的標(biāo)簽
3 tensorboard的使用
4 transforms處理數(shù)據(jù)
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 返回本地文件中的數(shù)據(jù)集
在這個(gè)操作中，當(dāng)前數(shù)據(jù)集的上一級(jí)目錄就是當(dāng)前所有同一數(shù)據(jù)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: 根目錄文件
:param label_dir: 分類(lèi)標(biāo)簽?zāi)夸? """
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是自己文件夾下的每一個(gè)圖片索引
:return: 返回每一個(gè)圖片對(duì)象和其對(duì)應(yīng)的標(biāo)簽，對(duì)于返回類(lèi)型可以直接調(diào)用image.show顯示或者用于后續(xù)圖像處理
"""
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\深度學(xué)習(xí)代碼庫(kù)\cats_and_dogs_small\\train'
label_dir = 'cats'
my_data = MyDataset(root_dir, label_dir)
first_pic, label = my_data[0] # 自動(dòng)調(diào)用__getitem__(self, idx)
first_pic.show()
print("當(dāng)前圖片中動(dòng)物所屬label", label)F:\Anaconda\envs\py38\python.exe G:/python_files/深度學(xué)習(xí)代碼庫(kù)/dataset/MyDataSet.py
當(dāng)前圖片中動(dòng)物所屬label cats
2 根據(jù)當(dāng)前已有的數(shù)據(jù)集創(chuàng)建每一個(gè)樣本數(shù)據(jù)對(duì)應(yīng)的標(biāo)簽
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: 根目錄
:param target_dir: 生成標(biāo)簽的目錄
:param label_dir: 要生成為標(biāo)簽?zāi)夸浢Q(chēng)
:param label_name: 生成的標(biāo)簽名稱(chēng)
"""
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\深度學(xué)習(xí)代碼庫(kù)\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()這樣上面的代碼中的訓(xùn)練集目錄下的每一個(gè)樣本都會(huì)在train的cats_label目錄下創(chuàng)建其對(duì)應(yīng)的分類(lèi)標(biāo)簽
每一個(gè)標(biāo)簽中文件中都有一個(gè)cat字符串或者其他動(dòng)物的分類(lèi)名稱(chēng)，以確定它到底是哪一個(gè)動(dòng)物
3 tensorboard的使用
# tensorboard --logdir=深度學(xué)習(xí)代碼庫(kù)/logs --port=2001
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('logs')
for i in range(100):
writer.add_scalar('當(dāng)前的函數(shù)表達(dá)式y(tǒng)=3*x',i*3,i)
writer.close()
#-----------------------------------------------------------
import numpy as np
from PIL import Image
image_PIL = Image.open('G:\python_files\深度學(xué)習(xí)代碼庫(kù)\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圖片', image_numpy,2, dataformats='HWC')這里使用tensorboard的作用是為了更好的展示數(shù)據(jù)，但是對(duì)于函數(shù)的使用，比如上面的add_image中的參數(shù)，最好的方式是點(diǎn)擊源碼查看其對(duì)應(yīng)的參數(shù)類(lèi)型，然后根據(jù)實(shí)際需要將它所需的數(shù)據(jù)類(lèi)型丟給add_image就好，而在源碼中該函數(shù)的參數(shù)中所要求的圖片類(lèi)型必須是tensor類(lèi)型或者是numpy，所以想要使用tensorboard展示數(shù)據(jù)就首先必須使用numpy或者使用transforms.Totensor將其轉(zhuǎn)化為tensor，然后丟給add_image函數(shù)
還有一個(gè)需要注意的是，使用add_image函數(shù)，圖片的tensor類(lèi)型或者numpy類(lèi)型必須和dataformats的默認(rèn)數(shù)據(jù)類(lèi)型一樣，否則根據(jù)圖片的數(shù)據(jù)類(lèi)型修改后面的額dataformatas就好
4 transforms處理數(shù)據(jù)
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\深度學(xué)習(xí)代碼庫(kù)\\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
# 對(duì)應(yīng)三個(gè)通道，每一個(gè)通道一個(gè)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()打開(kāi)源碼查看
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)必須傳入的是tensor數(shù)據(jù)類(lèi)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')調(diào)用Resize的時(shí)候，需要傳入的數(shù)據(jù)類(lèi)型的要求，查看源碼如下
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)
# 原始的數(shù)據(jù)集中每一條數(shù)據(jù)中包含以一張圖片和該圖片所屬的類(lèi)別
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
# 將整個(gè)數(shù)據(jù)集轉(zhuǎn)化為tensor類(lèi)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] # 經(jīng)過(guò)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
# 將整個(gè)數(shù)據(jù)集轉(zhuǎn)化為tensor類(lèi)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會(huì)將原始數(shù)據(jù)中一個(gè)batch中的圖片和圖片的Label分別放在一起，形成對(duì)應(yīng)
train_data_load = DataLoader(dataset=train_data, shuffle=True, batch_size=64,)
from torch.utils.tensorboard import SummaryWriter
write = SummaryWriter('dataLoad')
# 遍歷整個(gè)load，一次遍歷的圖片是64個(gè)
for batch_id, data in enumerate(train_data_load):
# 經(jīng)過(guò)DataLoda之后，每一個(gè)批次返回一批圖片和該圖片對(duì)應(yīng)的標(biāo)簽類(lèi)別
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()其中一個(gè)批次的輸出結(jié)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對(duì)所有批次的數(shù)據(jù)進(jìn)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時(shí)要注意默認(rèn)的通道數(shù)是3，如果經(jīng)過(guò)卷積層以后的圖片通道數(shù)大于3，那么是無(wú)法使用該函數(shù)進(jìn)行顯示的，會(huì)顯示斷言錯(cuò)誤的信息，所以此時(shí)要使用torch.reshape將通道數(shù)變?yōu)?，然后可以正常調(diào)用文章來(lái)源：http://www.zghlxwxcb.cn/news/detail-740291.html
對(duì)于還未涉及的方法也是這樣，查看其對(duì)應(yīng)的參數(shù)類(lèi)型（使用crtl+p，或者直接crtl+鼠標(biāo)點(diǎn)擊相應(yīng)的函數(shù)查看源碼），將所需要的參數(shù)類(lèi)型丟給它使用就好文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-740291.html
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