import torch
import torchvision
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
# ================================================================== #
# Table of Contents #
# ================================================================== #
# 1. Basic autograd example 1 (Line 25 to 39)
# 2. Basic autograd example 2 (Line 46 to 83)
# 3. Loading data from numpy (Line 90 to 97)
# 4. Input pipline (Line 104 to 129)
# 5. Input pipline for custom dataset (Line 136 to 156)
# 6. Pretrained model (Line 163 to 176)
# 7. Save and load model (Line 183 to 189)
# ================================================================== #
# 1. Basic autograd example 1 #
# ================================================================== #
# Create tensors.
x = torch.tensor(1., requires_grad=True)
w = torch.tensor(2., requires_grad=True)
b = torch.tensor(3., requires_grad=True)
# Build a computational graph.
y = w * x + b # y = 2 * x + 3
# Compute gradients.
y.backward()
# Print out the gradients.
print(x.grad) # x.grad = 2
print(w.grad) # w.grad = 1
print(b.grad) # b.grad = 1
# ================================================================== #
# 2. Basic autograd example 2 #
# ================================================================== #
# Create tensors of shape (10, 3) and (10, 2).
x = torch.randn(10, 3)
y = torch.randn(10, 2)
# Build a fully connected layer.
linear = nn.Linear(3, 2)
print ('w: ', linear.weight)
print ('b: ', linear.bias)
# Build loss function and optimizer.
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
# Forward pass.
pred = linear(x)
# Compute loss.
loss = criterion(pred, y)
print('loss: ', loss.item())
# Backward pass.
loss.backward()
# Print out the gradients.
print ('dL/dw: ', linear.weight.grad)
print ('dL/db: ', linear.bias.grad)
# 1-step gradient descent.
optimizer.step()
# You can also perform gradient descent at the low level.
# linear.weight.data.sub_(0.01 * linear.weight.grad.data)
# linear.bias.data.sub_(0.01 * linear.bias.grad.data)
# Print out the loss after 1-step gradient descent.
pred = linear(x)
loss = criterion(pred, y)
print('loss after 1 step optimization: ', loss.item())
# ================================================================== #
# 3. Loading data from numpy #
# ================================================================== #
# Create a numpy array.
x = np.array([[1, 2], [3, 4]])
# Convert the numpy array to a torch tensor.
y = torch.from_numpy(x)
# Convert the torch tensor to a numpy array.
z = y.numpy()
# ================================================================== #
# 4. Input pipeline #
# ================================================================== #
# Download and construct CIFAR-10 dataset.
train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
train=True,
transform=transforms.ToTensor(),
download=True)
# Fetch one data pair (read data from disk).
image, label = train_dataset[0]
print (image.size())
print (label)
# Data loader (this provides queues and threads in a very simple way).
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=64,
shuffle=True)
# When iteration starts, queue and thread start to load data from files.
data_iter = iter(train_loader)
# Mini-batch images and labels.
images, labels = data_iter.next()
# Actual usage of the data loader is as below.
for images, labels in train_loader:
# Training code should be written here.
pass
# ================================================================== #
# 5. Input pipeline for custom dataset #
# ================================================================== #
# You should build your custom dataset as below.
class CustomDataset(torch.utils.data.Dataset):
def __init__(self):
# TODO
# 1. Initialize file paths or a list of file names.
pass
def __getitem__(self, index):
# TODO
# 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
# 2. Preprocess the data (e.g. torchvision.Transform).
# 3. Return a data pair (e.g. image and label).
pass
def __len__(self):
# You should change 0 to the total size of your dataset.
return 0
# You can then use the prebuilt data loader.
custom_dataset = CustomDataset()
train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
batch_size=64,
shuffle=True)
# ================================================================== #
# 6. Pretrained model #
# ================================================================== #
# Download and load the pretrained ResNet-18.
resnet = torchvision.models.resnet18(pretrained=True)
# If you want to finetune only the top layer of the model, set as below.
for param in resnet.parameters():
param.requires_grad = False
# Replace the top layer for finetuning.
resnet.fc = nn.Linear(resnet.fc.in_features, 100) # 100 is an example.
# Forward pass.
images = torch.randn(64, 3, 224, 224)
outputs = resnet(images)
print (outputs.size()) # (64, 100)
# ================================================================== #
# 7. Save and load the model #
# ================================================================== #
# Save and load the entire model.
torch.save(resnet, 'model.ckpt')
model = torch.load('model.ckpt')
# Save and load only the model parameters (recommended).
torch.save(resnet.state_dict(), 'params.ckpt')
resnet.load_state_dict(torch.load('params.ckpt'))
Pytorch-pytorch深度学习教程之基本操作.zip
需积分: 1 106 浏览量
2024-05-22
06:08:44
上传
评论
收藏 2KB ZIP 举报
![avatar](https://profile-avatar.csdnimg.cn/default.jpg!1)
DdddJMs__135
- 粉丝: 1501
- 资源: 396
最新资源
- 图标生成工具 Windows 版本(支持.ico和.icns)IconBuilder v1.1
- 666662222277777
- 图标生成工具 Mac 版本(支持.ico和.icns)IconBuilder v1.1
- 求最大公约数(简单).cpp
- elasticsearch数据库下载、配置、使用案例
- springboot的概要介绍与分析
- C语言的概要介绍与分析
- 第一个较大的Android项目,基于Android平台的图书管理系统(Android studio).zip
- Cisco Packet Tracer 6.2 for Windows Instructor Version
- 使⽤pyIAST计算⽓体吸附选择性
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![feedback](https://img-home.csdnimg.cn/images/20220527035711.png)
![feedback](https://img-home.csdnimg.cn/images/20220527035711.png)
![feedback-tip](https://img-home.csdnimg.cn/images/20220527035111.png)