added testing pipeline, works too well

test.py

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