Pytorch-pytorch深度学习教程之基本操作.zip

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'))