HELL YEAH: project complete. Successful! Congrats nigs

18BCE243_244_245_259.csv

@@ -0,0 +1,51 @@
+Image, Prediction
+45.jpeg, Normal
+42.jpeg, Normal
+44.jpeg, Normal
+12.jpeg, Normal
+5.jpeg, Normal
+36.jpeg, Normal
+17.jpeg, Normal
+38.jpeg, Normal
+13.jpeg, Normal
+49.jpeg, Normal
+20.jpeg, Normal
+40.jpeg, Normal
+1.jpeg, Normal
+6.jpeg, Normal
+37.jpeg, Normal
+7.jpeg, Normal
+34.jpeg, Normal
+39.jpeg, Normal
+19.jpeg, Pneumonia
+41.jpeg, Pneumonia
+18.jpeg, Pneumonia
+3.jpeg, Pneumonia
+30.jpeg, Normal
+10.jpeg, Pneumonia
+50.jpeg, Pneumonia
+21.jpeg, Pneumonia
+8.jpeg, Pneumonia
+22.jpeg, Pneumonia
+27.jpeg, Pneumonia
+15.jpeg, Pneumonia
+24.jpeg, Pneumonia
+11.jpeg, Pneumonia
+33.jpeg, Pneumonia
+46.jpeg, Normal
+31.jpeg, Pneumonia
+35.jpeg, Pneumonia
+9.jpeg, Pneumonia
+25.jpeg, Pneumonia
+28.jpeg, Pneumonia
+4.jpeg, Pneumonia
+43.jpeg, Pneumonia
+23.jpeg, Pneumonia
+26.jpeg, Pneumonia
+14.jpeg, Pneumonia
+2.jpeg, Normal
+48.jpeg, Pneumonia
+47.jpeg, Normal
+29.jpeg, Normal
+16.jpeg, Normal
+32.jpeg, Normal

model.py

@@ -339,7 +339,7 @@ def train(
                 test_accuracies,
             )
 
-    def test(self, loss_fun, test_data, device="cuda"):
+    def test(self, loss_fun, test_data, device="cuda", has_labels=False):
         print("Starting Evaluating....")
         start = time.time()
         self.model.eval()
@@ -357,36 +357,41 @@ def test(self, loss_fun, test_data, device="cuda"):
             for batch in test_data:
                 test_images, test_labels = batch
                 test_images = test_images.to(device)
-                test_labels = test_labels.to(device)
+                if has_labels:
+                    test_labels = test_labels.to(device)
 
                 output = self.model(test_images)
-                loss = loss_fun(output, test_labels)
-                test_loss += loss.item()
+                if has_labels:
+                    loss = loss_fun(output, test_labels)
+                    test_loss += loss.item()
                 _, predicted = torch.max(output.data, 1)
-                predictions.extend(list(np.asarray(predicted.to('cpu'))))
-                total += test_labels.size(0)
-                correct += (predicted == test_labels).sum().item()
-                tp += ((test_labels == 1) & ((predicted) == 1)).sum().item()
-                fn += ((test_labels != 0) & ((predicted) == 0)).sum().item()
-                fp += ((test_labels != 1) & ((predicted) == 1)).sum().item()
-        testing_accuracy = correct / total * 100
-        testing_precision = tp / (tp + fp) * 100
-        testing_recall = tp / (tp + fn) * 100
-        testing_f1 = (
-            2.0
-            * testing_recall
-            * testing_precision
-            / (testing_recall + testing_precision)
-        )
+                predictions.extend(list(np.asarray(predicted.to("cpu"))))
+                if has_labels:
+                    total += test_labels.size(0)
+                    correct += (predicted == test_labels).sum().item()
+                    tp += ((test_labels == 1) & ((predicted) == 1)).sum().item()
+                    fn += ((test_labels != 0) & ((predicted) == 0)).sum().item()
+                    fp += ((test_labels != 1) & ((predicted) == 1)).sum().item()
+
+        if has_labels:
+            testing_accuracy = correct / total * 100
+            testing_precision = tp / (tp + fp) * 100
+            testing_recall = tp / (tp + fn) * 100
+            testing_f1 = (
+                2.0
+                * testing_recall
+                * testing_precision
+                / (testing_recall + testing_precision)
+            )
 
-        print(
-            f"Test Loss => {test_loss:08.4f} "
-            f"Test accuracy => {testing_accuracy:06.2f} "
-            f"Test Precision => {testing_precision:06.2f} "
-            f"Test Recall => {testing_recall:06.2f} "
-            f"Test F1 Score => {testing_f1:06.2f} "
-            f"Time Taken => {time.time() - start:08.4f}"
-        )
+            print(
+                f"Test Loss => {test_loss:08.4f} "
+                f"Test accuracy => {testing_accuracy:06.2f} "
+                f"Test Precision => {testing_precision:06.2f} "
+                f"Test Recall => {testing_recall:06.2f} "
+                f"Test F1 Score => {testing_f1:06.2f} "
+                f"Time Taken => {time.time() - start:08.4f}"
+            )
 
         return predictions
 

test.py

@@ -7,6 +7,15 @@
 
 from model import CoronaDetection
 
+if len(sys.argv) != 4:
+    print("usage: python test.py <data_directory_path> <model_name> <has_labels_flag>")
+    print("<data_directory_path>: Path to directory containing the test data")
+    print(
+        "<model_name>: Name of the trained model. See 'pretrained_models' for details"
+    )
+    print("<has_labels_flag>: Whether the directory contains test labels or not.")
+    exit(1)
+
 if torch.cuda.is_available():
     print("Using GPU")
     device = torch.device("cuda")
@@ -27,15 +36,28 @@
 )
 
 # get all file names
-dirname  = os.listdir(test_data_path)
+dirname = os.listdir(test_data_path)
 filename = []
 for d in dirname:
     filename.extend(os.listdir(os.path.join(test_data_path, d)))
 
 # testing the data
-predictions = model.test(torch.nn.CrossEntropyLoss(), test_data_loader, device=device)
+bool_dict = {
+    "True": True,
+    "False": False,
+    "true": True,
+    "false": False,
+    "t": True,
+    "f": False,
+}
+predictions = model.test(
+    torch.nn.CrossEntropyLoss(),
+    test_data_loader,
+    device=device,
+    has_labels=bool_dict[sys.argv[3]],
+)
 labels = ("Pneumonia", "Normal")
-predictions = [ labels[p] for p in predictions ]
+predictions = [labels[p] for p in predictions]
 
 # make the prediction file
 with open(f"test_predictions_{sys.argv[2]}.csv", "w") as f: