46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
from torch import no_grad, max
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from torch.utils.data import DataLoader
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from dataset import get_data
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def test(test_loader, net, criterion, device):
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val_loss = 0.0
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val_steps = 0
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total = 0
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correct = 0
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for i, data in enumerate(test_loader, 0):
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with no_grad():
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inputs, labels = data
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inputs, labels = inputs.to(device), labels.to(device)
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outputs = net(inputs)
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_, predicted = max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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loss = criterion(outputs, labels)
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val_loss += loss.cpu().numpy()
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val_steps += 1
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return val_loss / val_steps, correct / total
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def test_accuracy(net, data_root, device):
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train_set, test_set = get_data(data_root)
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test_loader = DataLoader(
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test_set, batch_size=4, shuffle=False, num_workers=2)
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correct = 0
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total = 0
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with no_grad():
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for data in test_loader:
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images, labels = data
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images, labels = images.to(device), labels.to(device)
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outputs = net(images)
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_, predicted = max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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return correct / total
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