更新 README.md
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README.md
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README.md
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@ -21,144 +21,7 @@ The model is trained on a dataset of cat and dog images. The training process in
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Here is a simplified version of the training code:
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```python
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import os
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, Dataset
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from torchvision import models, transforms
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from PIL import Image
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from safetensors.torch import save_file
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class CatDogDataset(Dataset):
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def __init__(self, root_dir, transform=None):
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self.root_dir = root_dir
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self.transform = transform
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self.image_paths = []
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self.labels = []
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for filename in os.listdir(root_dir):
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if 'cat' in filename:
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self.image_paths.append(os.path.join(root_dir, filename))
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self.labels.append(0) # cat
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elif 'dog' in filename:
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self.image_paths.append(os.path.join(root_dir, filename))
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self.labels.append(1) # dog
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def __len__(self):
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return len(self.image_paths)
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def __getitem__(self, idx):
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img_path = self.image_paths[idx]
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image = Image.open(img_path).convert('RGB')
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label = self.labels[idx]
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if self.transform:
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image = self.transform(image)
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return image, label
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# Data preprocessing
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data_transforms = {
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'train': transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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'val': transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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data_dir = 'dog-cat'
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image_datasets = {x: CatDogDataset(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
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dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4) for x in ['train', 'val']}
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dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
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class_names = ['cat', 'dog']
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Load and modify ResNet-50
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model_ft = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
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num_ftrs = model_ft.fc.in_features
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model_ft.fc = nn.Linear(num_ftrs, len(class_names))
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model_ft = model_ft.to(device)
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criterion = nn.CrossEntropyLoss()
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# Optimizer
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optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
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# Learning rate scheduler
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exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
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# Training function
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def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
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best_model_wts = model.state_dict()
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best_acc = 0.0
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for epoch in range(num_epochs):
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print(f'Epoch {epoch}/{num_epochs - 1}')
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print('-' * 10)
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for phase in ['train', 'val']:
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if phase == 'train':
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model.train()
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else:
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model.eval()
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running_loss = 0.0
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running_corrects = 0
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for inputs, labels in dataloaders[phase]:
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inputs = inputs.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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with torch.set_grad_enabled(phase == 'train'):
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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loss = criterion(outputs, labels)
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if phase == 'train':
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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if phase == 'train':
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scheduler.step()
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epoch_loss = running_loss / dataset_sizes[phase]
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epoch_acc = running_corrects.double() / dataset_sizes[phase]
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print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
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if phase == 'val' and epoch_acc > best_acc:
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best_acc = epoch_acc
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best_model_wts = model.state_dict()
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print()
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print(f'Best val Acc: {best_acc:4f}')
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model.load_state_dict(best_model_wts)
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return model
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# Train and evaluate the model
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model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
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# Save the model
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torch.save(model_ft.state_dict(), 'model_cat_dog_classifier.pt')
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save_file(model_ft.state_dict(), 'model_cat_dog_classifier.safetensors')
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```
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[train.py](./train.py)
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## Inference
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