自定义数据集的代码如下:
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]
#如果需要transform。则这里传入class当中的transform函数进行transform if self.transform: image = self.transform(image)
#另一种transform if self.target_transform: label = self.target_transform(label)
#先返回每一张图片,然后再返回当前图片的label return image, label
现在我们的自定义数据集即将做好了,然后使用dataloader模块打包数据集:
from torch.utils.data import DataLoader train_dataloader = DataLoader(CustomImageDataset(annotations_file, img_dir, transform=None, target_transform=None), batch_size=64, shuffle=True)
test_dataloader = DataLoader(CustomImageDataset(annotations_file, img_dir, transform=None, target_transform=None) batch_size=64, shuffle=True)
接下来就可以开始训练啦!!!
train fuction的代码:
def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 100 == 0: loss, current = loss.item(), batch * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
test function的代码:
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: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
一共使用5个epoch。因此代码如下:
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!")
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