PyTorch Pipeline
王哲峰 / 2022-07-12
目录
Libraries
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
Data
Data Download
training_data = datasets.FashionMNIST(
root = "data",
train = True,
download = True,
transform = ToTensor(),
)
test_data = datasets.FashionMNIST(
root = "data",
train = False,
download = True,
transform = ToTensor(),
)
Data Loader
batch_size = 64
train_dataloader = DataLoader(
training_data,
batch_size = batch_size
)
test_dataloader = DataLoader(
test_data,
batch_size = batch_size
)
for X, y in test_dataloader:
print(f"Shape of X [N, C, H, W]: {X.shape}")
print(f"Shape of y: {y.shape}, {y.dtype}")
break
Model
# get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")
# Define model
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28 * 28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10),
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
model = NeuralNetwork().to(device)
print(model)
Model Parameters Optimizing
Loss and Optimizer
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 1e-3)
Model Training
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
for batch, (X, y) in enumerate(dataloader):
# data
X, y = X.to(devcie), y.to(device)
# Compute prediction error
pred = model(X) # 前向传播
loss = loss_fn(pred, y) # 损失函数
# Backpropagation
optimizer.zero_grad() # 梯度
loss.backward() # 反向传播
optimizer.step() # 优化
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.eval()
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in dataloader:
# data
X, y = X.to(device), y.to(device)
# Compute prediction error
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y) \
.type(torch.float) \
.sum() \
.item() \
# 计算评价指标
test_loss /= num_batches
correct /= size
print(f"Test Error: \n")
print(f"Accuracy: {(100 * correct):>0.1f}")
print(f"Avg loss: {test_loss:>8f}\n")
epochs = 5
for t in range(epochs):
print(f"Epoch {t+1}\n-----------------------------")
train(train_dataloader, model, loss_fn, optimizer)
test(test_dataloader, model, loss_fn)
print("Done!")
Model Saving
torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")
Model Loading
model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))
model.eval()
classes = [
"T-shirt/top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
pred = model(x)
predicted, actual = classes[pred[0].argmax(0)], classes[y]
print(f"Predicted: {predicted}, Actual: {actual}")