71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
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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import torchvision
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import torchvision.transforms as transforms
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(3, 16, 3)
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self.pool = nn.MaxPool2d(2, 2)
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self.conv2 = nn.Conv2d(16, 32, 3)
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self.fc1 = nn.Linear(32 * 14 * 14, 128)
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self.fc2 = nn.Linear(128, 5)
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def forward(self, x):
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x = self.pool(torch.relu(self.conv1(x)))
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x = self.pool(torch.relu(self.conv2(x)))
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x = x.view(-1, 32 * 14 * 14)
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x = torch.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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def main() -> None:
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transform = transforms.Compose([
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transforms.Resize((64, 64)),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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])
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path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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path = os.path.join(path, 'garbage_photos/train_set')
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trainset = torchvision.datasets.ImageFolder(root=path, transform=transform)
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)
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net = Net()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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net.to(device)
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for epoch in range(10):
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running_loss = 0.0
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for i, data in enumerate(trainloader, 0):
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inputs, labels = data[0].to(device), data[1].to(device)
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optimizer.zero_grad()
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outputs = net(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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if i % 200 == 199:
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print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 200:.3f}')
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running_loss = 0.0
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torch.save({
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'model_state_dict': net.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'epoch': epoch,
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'loss': loss,
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}, 'model.pt')
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print("Uczenie zakończone.")
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if __name__ == "__main__":
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main() |