38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from dogs_cats_ds import DogCatDataset
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from model import DogCatClassifier
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from consts import TEST_DATA
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import torch.optim as optim
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def test(model: nn.Module, test_loader: DataLoader, criterion, device):
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model.eval()
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test_loss = 0
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correct = 0
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total = 0
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with torch.no_grad():
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for img, lab in test_loader:
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img, lab = img.to(device), lab.to(device).float().view(-1, 1)
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out = model(img)
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loss = criterion(out, lab)
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test_loss += loss.item()
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pred = (out > 0.5).float()
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total += lab.size(0)
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correct += (pred == lab).sum().item()
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print(f'test loss: {test_loss / len(test_loader):.4f}, test_acc: {100*correct/total:.2f}%')
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model.train()
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if __name__ == "__main__":
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print('yo')
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f'Using device: {device}')
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dog_test_dataset = DogCatDataset(TEST_DATA)
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dog_test_loader = DataLoader(dog_test_dataset, batch_size = 32, shuffle = False) # since its test, bad to shuffle
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model = DogCatClassifier()
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criterion = nn.BCELoss()
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model.load_state_dict(torch.load('dog_cat_classifier.pth', map_location = device, weights_only = True))
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test(model, dog_test_loader, criterion, device)
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