101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchvision
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from torchvision import datasets, models, transforms
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import multiprocessing
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def main():
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# Set the device to use (GPU if available, otherwise CPU)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Define data transformations
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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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"validation": 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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# Set the path to your vegetable images folder
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data_dir = "neural_network/dataset/vegetables"
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# Load the dataset from the folder
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image_datasets = {x: datasets.ImageFolder(f"{data_dir}/{x}", data_transforms[x])
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for x in ["train", "validation"]}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=multiprocessing.cpu_count())
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for x in ["train", "validation"]}
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dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "validation"]}
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class_names = image_datasets["train"].classes
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num_classes = len(class_names)
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# Load a pre-trained ResNet model
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model = models.resnet18(pretrained=True)
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num_ftrs = model.fc.in_features
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model.fc = nn.Linear(num_ftrs, num_classes)
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model = model.to(device)
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# Define the loss function and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
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# Train the model
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def train_model(model, criterion, optimizer, num_epochs=2):
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for epoch in range(num_epochs):
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print(f"Epoch {epoch+1}/{num_epochs}")
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print("-" * 10)
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for phase in ["train", "validation"]:
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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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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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torch.save(best_model_wts, "neural_network/save/trained_model.pth")
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# Start training
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train_model(model, criterion, optimizer, num_epochs=2)
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if __name__ == '__main__':
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multiprocessing.set_start_method('spawn') # Set start method for multiprocessing
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main()
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