From 409804c2842ffc6460feb9a400a3c67e038066d5 Mon Sep 17 00:00:00 2001
From: sun <2818133456@qq.com>
Date: Thu, 29 Jun 2023 21:53:51 +0800
Subject: [PATCH 1/3] tensors and transforms

---
 docs/2.0/img/fashion_mnist.png                | Bin 0 -> 31452 bytes
 docs/2.0/img/fashion_mnist2.png               | Bin 0 -> 9750 bytes
 .../datasets_dataloaders.md                   | 189 ++++++++++++++++++
 .../Introduction_to_PyTorch/transforms.md     |  91 +++++++++
 mkdocs.yml                                    |   2 +
 5 files changed, 282 insertions(+)
 create mode 100644 docs/2.0/img/fashion_mnist.png
 create mode 100644 docs/2.0/img/fashion_mnist2.png
 create mode 100644 docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
 create mode 100644 docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md

diff --git a/docs/2.0/img/fashion_mnist.png b/docs/2.0/img/fashion_mnist.png
new file mode 100644
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diff --git a/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md b/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
new file mode 100644
index 000000000..1cc0212ec
--- /dev/null
+++ b/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
@@ -0,0 +1,189 @@
+# 数据集和数据加载器
+
+> 译者：[Daydaylight](https://github.com/Daydaylight)
+>
+> 项目地址：https://pytorch.apachecn.org/2.0/tutorials/datasets_dataloaders
+>
+> 原始地址：https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
+
+处理数据样本的代码可能会变得杂乱无章，难以维护；我们希望我们的数据集代码与我们的模型训练代码分段，以提高可读性和模块化。
+PyTorch提供了两个数据基元： ``torch.utils.data.DataLoader``和``torch.utils.data.Dataset``。允许你使用预先加载的数据集以及你自己的数据。
+``Dataset``存储样本和它们相应的标签，``DataLoader``在``Dataset``基础上添加了一个迭代器，迭代器可以迭代数据集，以便能够轻松地访问``Dataset``中的样本。
+
+PyTorch领域库提供了一些预加载的数据集（如FashionMNIST），这些数据集是``torch.utils.data.Dataset``的子类，并实现特定数据的功能。它们可以被用来为你的模型制作原型和基准。你可以找到它们这里：[Image Datasets](https://pytorch.org/vision/stable/datasets.html)、[Text Datasets](https://pytorch.org/text/stable/datasets.html)，和[Audio Datasets](https://pytorch.org/audio/stable/datasets.html)
+
+##加载一个数据集
+
+下面是一个如何从TorchVision加载[Fashion-MNIST](https://research.zalando.com/project/fashion_mnist/fashion_mnist/)数据集的例子。
+Fashion-MNIST是一个由60,000个训练实例和10,000个测试实例组成的Zalando的文章图像数据集。
+每个例子包括一个28×28的灰度图像和10个类别中的一个相关标签。
+我们加载 [FashionMNIST Dataset](https://pytorch.org/vision/stable/datasets.html#fashion-mnist) ，参数如下：
+ - ``root`` 是存储训练/测试数据的路径,
+ - ``train`` 指定训练或测试数据集,
+ - ``download=True`` 如果``root``没有数据，就从网上下载数据。
+ - ``transform`` 和 ``target_transform`` 指定特征和标签的转换。
+```py
+import torch
+from torch.utils.data import Dataset
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+import matplotlib.pyplot as plt
+
+
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor()
+)
+
+test_data = datasets.FashionMNIST(
+    root="data",
+    train=False,
+    download=True,
+    transform=ToTensor()
+)
+```
+
+
+
+## 迭代和可视化数据集
+
+我们可以像列表一样手动索引``Datasets``：``training_data[index]``。
+我们使用``matplotlib``来可视化我们训练数据中的一些样本。
+
+
+```py
+labels_map = {
+    0: "T-Shirt",
+    1: "Trouser",
+    2: "Pullover",
+    3: "Dress",
+    4: "Coat",
+    5: "Sandal",
+    6: "Shirt",
+    7: "Sneaker",
+    8: "Bag",
+    9: "Ankle Boot",
+}
+figure = plt.figure(figsize=(8, 8))
+cols, rows = 3, 3
+for i in range(1, cols * rows + 1):
+    sample_idx = torch.randint(len(training_data), size=(1,)).item()
+    img, label = training_data[sample_idx]
+    figure.add_subplot(rows, cols, i)
+    plt.title(labels_map[label])
+    plt.axis("off")
+    plt.imshow(img.squeeze(), cmap="gray")
+plt.show()
+```
+![https://pytorch.apachecn.org/2.0/img/fashion_mnist.png](https://pytorch.apachecn.org/2.0/img/fashion_mnist.png)
+
+## 为你的文件创建一个自定义数据集
+一个自定义的数据集类必须实现三个函数： `__init__`, `__len__`, 和 `__getitem__`。
+看看这个实现；FashionMNIST的图片被存储在一个`img_dir'`的目录中，而它们的标签则分别存储在一个CSV文件``annotations_file`中。
+
+在接下来的章节中，我们将分解这些函数中的每一个发生了什么。
+```py
+import os
+import pandas as pd
+from torchvision.io import read_image
+
+class CustomImageDataset(Dataset):
+    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
+        self.img_labels = pd.read_csv(annotations_file)
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
+        image = read_image(img_path)
+        label = self.img_labels.iloc[idx, 1]
+        if self.transform:
+            image = self.transform(image)
+        if self.target_transform:
+            label = self.target_transform(label)
+        return image, label
+```
+### __init__
+在实例化数据集对象时，__init__函数被运行一次。我们初始化包含图像的目录、注释文件和两种转换（下一节将详细介绍）。
+
+标签.csv文件看起来像：
+```py
+tshirt1.jpg, 0
+tshirt2.jpg, 0
+......
+ankleboot999.jpg, 9
+
+```
+
+```py
+def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
+    self.img_labels = pd.read_csv(annotations_file)
+    self.img_dir = img_dir
+    self.transform = transform
+    self.target_transform = target_transform
+```
+### __len__
+函数__len__返回我们数据集中的样本数。
+Example:
+```py
+def __len__(self):
+    return len(self.img_labels)
+```
+### __getitem__
+
+
+函数 __getitem__ 从数据集中给定的索引``idx``处加载并返回一个样本。根据索引，它确定图像在磁盘上的位置，用``read_image``将其转换为张量，从``self.img_labels``的csv数据中获取相应的标签。从``self.img_labels``中的csv数据中获取相应的标签，对它们调用transform函数（如果适用），并返回张量图像和相应的标签的元组。
+
+
+```py
+def __getitem__(self, idx):
+    img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
+    image = read_image(img_path)
+    label = self.img_labels.iloc[idx, 1]
+    if self.transform:
+        image = self.transform(image)
+    if self.target_transform:
+        label = self.target_transform(label)
+    return image, label
+```
+## 用DataLoaders准备你的数据进行训练
+
+ ``Dataset``每次检索一个我们数据集的特征和标签样本。在训练一个模型时，我们通常希望以 "小批量 "的方式传递样本，在每个周期重新洗牌数据以减少模型的过拟合，并使用Python的``multiprocessing``来加快数据的检索速度。
+ 
+`DataLoader'是一个可迭代的，它用一个简单的API为我们抽象出这种复杂性。
+```py
+from torch.utils.data import DataLoader
+
+train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)
+test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)
+```
+## 遍历DataLoader
+我们已经将该数据集加载到``DataLoader``中，并可以根据需要迭代该数据集。下面的每次迭代都会返回一批``train_features``和``train_labels``（分别包含``batch_size=64``的特征和标签）。因为我们指定了``shuffle=True``，在我们遍历所有批次后，数据会被洗牌（为了更精细地控制数据加载顺序的精细控制，请看[Samplers](https://pytorch.org/docs/stable/data.html#data-loading-order-and-sampler)）。
+
+```py
+# 显示图像和标签。
+train_features, train_labels = next(iter(train_dataloader))
+print(f"Feature batch shape: {train_features.size()}")
+print(f"Labels batch shape: {train_labels.size()}")
+img = train_features[0].squeeze()
+label = train_labels[0]
+plt.imshow(img, cmap="gray")
+plt.show()
+print(f"Label: {label}")
+```
+输出：
+
+![https://pytorch.apachecn.org/2.0/img/fashion_mnist2.png](https://pytorch.apachecn.org/2.0/img/fashion_mnist2.png)
+```py
+Feature batch shape: torch.Size([64, 1, 28, 28])
+Labels batch shape: torch.Size([64])
+Label: 5
+```
+## 阅读更多
+- [torch.utils.data API](https://pytorch.org/docs/stable/data.html)
\ No newline at end of file
diff --git a/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md b/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md
new file mode 100644
index 000000000..482039405
--- /dev/null
+++ b/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md
@@ -0,0 +1,91 @@
+# TRANSFORMS
+
+> 译者：[Daydaylight](https://github.com/Daydaylight)
+>
+> 项目地址：https://pytorch.apachecn.org/2.0/tutorials/transforms
+>
+> 原始地址：https://pytorch.org/tutorials/beginner/basics/transforms_tutorial.html
+
+
+数据并不总是以训练机器学习算法所需的最终处理形式出现。我们使用变换来对数据进行一些处理，使其适合训练。
+
+所有的TorchVision数据集都有两个参数-``transform``用于修改特征和``target_transform``用于修改标签,它们接受包含转换逻辑的callables。[torchvision.transforms](https://pytorch.org/vision/stable/transforms.html)模块提供了几个常用的转换，开箱即用。
+FashionMNIST的特征是PIL图像格式，而标签是整数。对于训练，我们需要将特征作为归一化的张量，将标签作为一热编码的张量。
+为了进行这些转换，我们使用 "ToTensor "和 "Lambda"。
+```py
+import torch
+from torchvision import datasets
+from torchvision.transforms import ToTensor, Lambda
+
+ds = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))
+)
+```
+
+输出：
+```py
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
+
+  0%|          | 0/26421880 [00:00<?, ?it/s]
+  0%|          | 65536/26421880 [00:00<01:12, 361690.02it/s]
+  1%|          | 229376/26421880 [00:00<00:38, 679756.53it/s]
+  2%|2         | 655360/26421880 [00:00<00:14, 1775435.30it/s]
+  7%|6         | 1736704/26421880 [00:00<00:06, 3785228.35it/s]
+ 15%|#4        | 3833856/26421880 [00:00<00:02, 8223694.86it/s]
+ 21%|##1       | 5570560/26421880 [00:00<00:02, 9088903.43it/s]
+ 32%|###1      | 8454144/26421880 [00:01<00:01, 13772389.09it/s]
+ 39%|###9      | 10420224/26421880 [00:01<00:01, 13068367.31it/s]
+ 50%|#####     | 13238272/26421880 [00:01<00:00, 16440554.97it/s]
+ 58%|#####7    | 15269888/26421880 [00:01<00:00, 14938744.03it/s]
+ 68%|######8   | 18055168/26421880 [00:01<00:00, 17703674.30it/s]
+ 76%|#######6  | 20119552/26421880 [00:01<00:00, 15854480.37it/s]
+ 87%|########6 | 22904832/26421880 [00:01<00:00, 18366169.37it/s]
+ 95%|#########4| 25034752/26421880 [00:01<00:00, 16404116.31it/s]
+100%|##########| 26421880/26421880 [00:02<00:00, 13106029.06it/s]
+Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
+
+  0%|          | 0/29515 [00:00<?, ?it/s]
+100%|##########| 29515/29515 [00:00<00:00, 326257.67it/s]
+Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
+
+  0%|          | 0/4422102 [00:00<?, ?it/s]
+  1%|1         | 65536/4422102 [00:00<00:12, 362747.74it/s]
+  5%|5         | 229376/4422102 [00:00<00:06, 681864.40it/s]
+ 15%|#4        | 655360/4422102 [00:00<00:02, 1798436.42it/s]
+ 40%|####      | 1769472/4422102 [00:00<00:00, 3872995.18it/s]
+ 79%|#######9  | 3506176/4422102 [00:00<00:00, 7404355.18it/s]
+100%|##########| 4422102/4422102 [00:00<00:00, 5422111.79it/s]
+Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
+
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
+Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
+
+  0%|          | 0/5148 [00:00<?, ?it/s]
+100%|##########| 5148/5148 [00:00<00:00, 35867569.75it/s]
+Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
+```
+
+
+## ToTensor()
+[ToTensor](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor)将PIL图像或NumPy的``ndarray``转换为``FloatTensor``。图像的像素强度值在[0., 1.]范围内缩放。
+## Lambda Transforms
+Lambda transforms 应用任何用户定义的lambda函数。在这里，我们定义了一个函数来把整数变成一个单热编码的张量。
+它首先创建一个大小为10（我们数据集中的标签数量）的零张量，然后调用[scatter_](https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html) ,指定了一个``value=1``在标签``y``所给的索引上。
+
+```py
+target_transform = Lambda(lambda y: torch.zeros(
+    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))
+```
+### 阅读更多
+- [torchvision.transforms API](https://pytorch.org/vision/stable/transforms.html)
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 4ab18329c..d5863b8f1 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -42,6 +42,8 @@ nav:
         - "学习基本知识": "2.0/tutorials/Introduction_to_PyTorch/learn_the_basics.md"
         - "快速入门": "2.0/tutorials/Introduction_to_PyTorch/quickstart_tutorial.md"
         - "Tensors介绍": "2.0/tutorials/Introduction_to_PyTorch/tensors.md"
+        - "数据集和数据加载器": "2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md"
+        - "Transforms介绍": "2.0/tutorials/Introduction_to_PyTorch/transforms.md"
         - "构建神经网络模型": "2.0/tutorials/Introduction_to_PyTorch/build_the_neural_network.md"
     - "中文文档": 
       - "介绍": "2.0/docs/README.md"

From 165bc3cc51d156d11452803a2d40940ca668b5dd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=89=87=E5=88=BB?= <jiang-s@163.com>
Date: Fri, 30 Jun 2023 13:45:28 +0800
Subject: [PATCH 2/3] Update datasets_dataloaders.md

---
 .../datasets_dataloaders.md                   | 29 ++++++++++++++-----
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md b/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
index 1cc0212ec..c5072855c 100644
--- a/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
+++ b/docs/2.0/tutorials/Introduction_to_PyTorch/datasets_dataloaders.md
@@ -2,9 +2,9 @@
 
 > 译者：[Daydaylight](https://github.com/Daydaylight)
 >
-> 项目地址：https://pytorch.apachecn.org/2.0/tutorials/datasets_dataloaders
+> 项目地址：<https://pytorch.apachecn.org/2.0/tutorials/datasets_dataloaders>
 >
-> 原始地址：https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
+> 原始地址：<https://pytorch.org/tutorials/beginner/basics/data_tutorial.html>
 
 处理数据样本的代码可能会变得杂乱无章，难以维护；我们希望我们的数据集代码与我们的模型训练代码分段，以提高可读性和模块化。
 PyTorch提供了两个数据基元： ``torch.utils.data.DataLoader``和``torch.utils.data.Dataset``。允许你使用预先加载的数据集以及你自己的数据。
@@ -22,6 +22,7 @@ Fashion-MNIST是一个由60,000个训练实例和10,000个测试实例组成的Z
  - ``train`` 指定训练或测试数据集,
  - ``download=True`` 如果``root``没有数据，就从网上下载数据。
  - ``transform`` 和 ``target_transform`` 指定特征和标签的转换。
+
 ```py
 import torch
 from torch.utils.data import Dataset
@@ -45,14 +46,11 @@ test_data = datasets.FashionMNIST(
 )
 ```
 
-
-
 ## 迭代和可视化数据集
 
 我们可以像列表一样手动索引``Datasets``：``training_data[index]``。
 我们使用``matplotlib``来可视化我们训练数据中的一些样本。
 
-
 ```py
 labels_map = {
     0: "T-Shirt",
@@ -77,13 +75,16 @@ for i in range(1, cols * rows + 1):
     plt.imshow(img.squeeze(), cmap="gray")
 plt.show()
 ```
+
 ![https://pytorch.apachecn.org/2.0/img/fashion_mnist.png](https://pytorch.apachecn.org/2.0/img/fashion_mnist.png)
 
 ## 为你的文件创建一个自定义数据集
+
 一个自定义的数据集类必须实现三个函数： `__init__`, `__len__`, 和 `__getitem__`。
 看看这个实现；FashionMNIST的图片被存储在一个`img_dir'`的目录中，而它们的标签则分别存储在一个CSV文件``annotations_file`中。
 
 在接下来的章节中，我们将分解这些函数中的每一个发生了什么。
+
 ```py
 import os
 import pandas as pd
@@ -109,10 +110,13 @@ class CustomImageDataset(Dataset):
             label = self.target_transform(label)
         return image, label
 ```
+
 ### __init__
+
 在实例化数据集对象时，__init__函数被运行一次。我们初始化包含图像的目录、注释文件和两种转换（下一节将详细介绍）。
 
 标签.csv文件看起来像：
+
 ```py
 tshirt1.jpg, 0
 tshirt2.jpg, 0
@@ -128,19 +132,21 @@ def __init__(self, annotations_file, img_dir, transform=None, target_transform=N
     self.transform = transform
     self.target_transform = target_transform
 ```
+
 ### __len__
+
 函数__len__返回我们数据集中的样本数。
+
 Example:
+
 ```py
 def __len__(self):
     return len(self.img_labels)
 ```
 ### __getitem__
 
-
 函数 __getitem__ 从数据集中给定的索引``idx``处加载并返回一个样本。根据索引，它确定图像在磁盘上的位置，用``read_image``将其转换为张量，从``self.img_labels``的csv数据中获取相应的标签。从``self.img_labels``中的csv数据中获取相应的标签，对它们调用transform函数（如果适用），并返回张量图像和相应的标签的元组。
 
-
 ```py
 def __getitem__(self, idx):
     img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
@@ -152,18 +158,22 @@ def __getitem__(self, idx):
         label = self.target_transform(label)
     return image, label
 ```
+
 ## 用DataLoaders准备你的数据进行训练
 
  ``Dataset``每次检索一个我们数据集的特征和标签样本。在训练一个模型时，我们通常希望以 "小批量 "的方式传递样本，在每个周期重新洗牌数据以减少模型的过拟合，并使用Python的``multiprocessing``来加快数据的检索速度。
  
 `DataLoader'是一个可迭代的，它用一个简单的API为我们抽象出这种复杂性。
+
 ```py
 from torch.utils.data import DataLoader
 
 train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)
 test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)
 ```
+
 ## 遍历DataLoader
+
 我们已经将该数据集加载到``DataLoader``中，并可以根据需要迭代该数据集。下面的每次迭代都会返回一批``train_features``和``train_labels``（分别包含``batch_size=64``的特征和标签）。因为我们指定了``shuffle=True``，在我们遍历所有批次后，数据会被洗牌（为了更精细地控制数据加载顺序的精细控制，请看[Samplers](https://pytorch.org/docs/stable/data.html#data-loading-order-and-sampler)）。
 
 ```py
@@ -177,13 +187,16 @@ plt.imshow(img, cmap="gray")
 plt.show()
 print(f"Label: {label}")
 ```
+
 输出：
 
 ![https://pytorch.apachecn.org/2.0/img/fashion_mnist2.png](https://pytorch.apachecn.org/2.0/img/fashion_mnist2.png)
+
 ```py
 Feature batch shape: torch.Size([64, 1, 28, 28])
 Labels batch shape: torch.Size([64])
 Label: 5
 ```
+
 ## 阅读更多
-- [torch.utils.data API](https://pytorch.org/docs/stable/data.html)
\ No newline at end of file
+- [torch.utils.data API](https://pytorch.org/docs/stable/data.html)

From ace0cc11be59262a2febdcbbaabbb70d148841bb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=89=87=E5=88=BB?= <jiang-s@163.com>
Date: Fri, 30 Jun 2023 13:46:37 +0800
Subject: [PATCH 3/3] Update transforms.md

---
 .../Introduction_to_PyTorch/transforms.md          | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md b/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md
index 482039405..5ac5e4c5c 100644
--- a/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md
+++ b/docs/2.0/tutorials/Introduction_to_PyTorch/transforms.md
@@ -2,16 +2,16 @@
 
 > 译者：[Daydaylight](https://github.com/Daydaylight)
 >
-> 项目地址：https://pytorch.apachecn.org/2.0/tutorials/transforms
+> 项目地址：<https://pytorch.apachecn.org/2.0/tutorials/transforms>
 >
-> 原始地址：https://pytorch.org/tutorials/beginner/basics/transforms_tutorial.html
-
+> 原始地址：<https://pytorch.org/tutorials/beginner/basics/transforms_tutorial.html>
 
 数据并不总是以训练机器学习算法所需的最终处理形式出现。我们使用变换来对数据进行一些处理，使其适合训练。
 
 所有的TorchVision数据集都有两个参数-``transform``用于修改特征和``target_transform``用于修改标签,它们接受包含转换逻辑的callables。[torchvision.transforms](https://pytorch.org/vision/stable/transforms.html)模块提供了几个常用的转换，开箱即用。
 FashionMNIST的特征是PIL图像格式，而标签是整数。对于训练，我们需要将特征作为归一化的张量，将标签作为一热编码的张量。
 为了进行这些转换，我们使用 "ToTensor "和 "Lambda"。
+
 ```py
 import torch
 from torchvision import datasets
@@ -27,6 +27,7 @@ ds = datasets.FashionMNIST(
 ```
 
 输出：
+
 ```py
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
@@ -76,10 +77,12 @@ Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labe
 Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 ```
 
-
 ## ToTensor()
+
 [ToTensor](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor)将PIL图像或NumPy的``ndarray``转换为``FloatTensor``。图像的像素强度值在[0., 1.]范围内缩放。
+
 ## Lambda Transforms
+
 Lambda transforms 应用任何用户定义的lambda函数。在这里，我们定义了一个函数来把整数变成一个单热编码的张量。
 它首先创建一个大小为10（我们数据集中的标签数量）的零张量，然后调用[scatter_](https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html) ,指定了一个``value=1``在标签``y``所给的索引上。
 
@@ -87,5 +90,6 @@ Lambda transforms 应用任何用户定义的lambda函数。在这里，我们
 target_transform = Lambda(lambda y: torch.zeros(
     10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))
 ```
+
 ### 阅读更多
-- [torchvision.transforms API](https://pytorch.org/vision/stable/transforms.html)
\ No newline at end of file
+- [torchvision.transforms API](https://pytorch.org/vision/stable/transforms.html)