wehub-resource-sync
190 lines
6.2 KiB
Python
190 lines
6.2 KiB
Python
import torch
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import torchvision
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import torch.nn as nn
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import numpy as np
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import torchvision.transforms as transforms
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# ================================================================== #
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# Table of Contents #
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# ================================================================== #
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# 1. Basic autograd example 1 (Line 25 to 39)
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# 2. Basic autograd example 2 (Line 46 to 83)
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# 3. Loading data from numpy (Line 90 to 97)
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# 4. Input pipline (Line 104 to 129)
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# 5. Input pipline for custom dataset (Line 136 to 156)
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# 6. Pretrained model (Line 163 to 176)
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# 7. Save and load model (Line 183 to 189)
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# ================================================================== #
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# 1. Basic autograd example 1 #
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# ================================================================== #
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# Create tensors.
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x = torch.tensor(1., requires_grad=True)
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w = torch.tensor(2., requires_grad=True)
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b = torch.tensor(3., requires_grad=True)
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# Build a computational graph.
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y = w * x + b # y = 2 * x + 3
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# Compute gradients.
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y.backward()
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# Print out the gradients.
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print(x.grad) # x.grad = 2
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print(w.grad) # w.grad = 1
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print(b.grad) # b.grad = 1
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# ================================================================== #
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# 2. Basic autograd example 2 #
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# ================================================================== #
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# Create tensors of shape (10, 3) and (10, 2).
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x = torch.randn(10, 3)
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y = torch.randn(10, 2)
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# Build a fully connected layer.
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linear = nn.Linear(3, 2)
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print ('w: ', linear.weight)
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print ('b: ', linear.bias)
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# Build loss function and optimizer.
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criterion = nn.MSELoss()
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optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
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# Forward pass.
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pred = linear(x)
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# Compute loss.
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loss = criterion(pred, y)
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print('loss: ', loss.item())
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# Backward pass.
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loss.backward()
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# Print out the gradients.
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print ('dL/dw: ', linear.weight.grad)
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print ('dL/db: ', linear.bias.grad)
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# 1-step gradient descent.
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optimizer.step()
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# You can also perform gradient descent at the low level.
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# linear.weight.data.sub_(0.01 * linear.weight.grad.data)
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# linear.bias.data.sub_(0.01 * linear.bias.grad.data)
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# Print out the loss after 1-step gradient descent.
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pred = linear(x)
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loss = criterion(pred, y)
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print('loss after 1 step optimization: ', loss.item())
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# ================================================================== #
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# 3. Loading data from numpy #
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# ================================================================== #
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# Create a numpy array.
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x = np.array([[1, 2], [3, 4]])
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# Convert the numpy array to a torch tensor.
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y = torch.from_numpy(x)
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# Convert the torch tensor to a numpy array.
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z = y.numpy()
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# ================================================================== #
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# 4. Input pipeline #
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# ================================================================== #
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# Download and construct CIFAR-10 dataset.
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train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
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train=True,
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transform=transforms.ToTensor(),
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download=True)
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# Fetch one data pair (read data from disk).
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image, label = train_dataset[0]
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print (image.size())
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print (label)
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# Data loader (this provides queues and threads in a very simple way).
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
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batch_size=64,
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shuffle=True)
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# When iteration starts, queue and thread start to load data from files.
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data_iter = iter(train_loader)
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# Mini-batch images and labels.
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images, labels = data_iter.next()
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# Actual usage of the data loader is as below.
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for images, labels in train_loader:
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# Training code should be written here.
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pass
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# ================================================================== #
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# 5. Input pipeline for custom dataset #
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# ================================================================== #
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# You should build your custom dataset as below.
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class CustomDataset(torch.utils.data.Dataset):
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def __init__(self):
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# TODO
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# 1. Initialize file paths or a list of file names.
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pass
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def __getitem__(self, index):
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# TODO
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# 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
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# 2. Preprocess the data (e.g. torchvision.Transform).
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# 3. Return a data pair (e.g. image and label).
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pass
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def __len__(self):
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# You should change 0 to the total size of your dataset.
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return 0
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# You can then use the prebuilt data loader.
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custom_dataset = CustomDataset()
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train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
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batch_size=64,
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shuffle=True)
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# ================================================================== #
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# 6. Pretrained model #
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# ================================================================== #
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# Download and load the pretrained ResNet-18.
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resnet = torchvision.models.resnet18(pretrained=True)
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# If you want to finetune only the top layer of the model, set as below.
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for param in resnet.parameters():
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param.requires_grad = False
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# Replace the top layer for finetuning.
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resnet.fc = nn.Linear(resnet.fc.in_features, 100) # 100 is an example.
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# Forward pass.
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images = torch.randn(64, 3, 224, 224)
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outputs = resnet(images)
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print (outputs.size()) # (64, 100)
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# ================================================================== #
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# 7. Save and load the model #
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# ================================================================== #
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# Save and load the entire model.
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torch.save(resnet, 'model.ckpt')
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model = torch.load('model.ckpt')
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# Save and load only the model parameters (recommended).
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torch.save(resnet.state_dict(), 'params.ckpt')
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resnet.load_state_dict(torch.load('params.ckpt'))
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