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baseline.py
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baseline.py
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import time
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from src.model import resnet18
from torch.utils.data import DataLoader
from torchvision import datasets
from src.utils import save_checkpoint_classifier
device = 'cuda' if torch.cuda.is_available else 'cpu'
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])])
train_dataset = datasets.STL10(
'./data', split='train', download=False, transform=data_transform)
train_loader = DataLoader(
train_dataset, batch_size=128, num_workers=8)
model = resnet18().to(device)
### Train ###
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
epochs = 50
for epoch in range(epochs):
t = time.time()
for x, label in train_loader:
x, label = x.to(device), label.to(device)
preds = model(x) # [batch, num_classes]
loss = criterion(preds, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("[%d/%d] baseline loss : %.4f | time : %.2fs" %
(epoch + 1, epochs, loss.item(), time.time() - t))
save_checkpoint_classifier(model, 'checkpoints/baseline.pt')
### Test ###
test_dataset = datasets.STL10(
'./data', split='test', download=False, transform=data_transform)
test_loader = DataLoader(
test_dataset, batch_size=128, num_workers=8)
with torch.no_grad():
correct, total = 0, 0
for x, label in test_loader:
x, label = x.to(device), label.to(device)
output = model(x)
preds = torch.argmax(output, dim=1)
correct += int(torch.sum(preds == label))
total += int(label.size(0))
print('Test Accuracy : %.4f'%(correct / total))