From fe6e51d6250f12ddcb5d42ee1509dd8e7fbc117d Mon Sep 17 00:00:00 2001
From: Till Raab <till.raab@uni-tuebingen.de>
Date: Thu, 26 Oct 2023 07:35:41 +0200
Subject: [PATCH] inference function with dataloader to optimizer inference.
 now generate plots ...

---
 inference.py | 48 ++++++++++++++++++++++++++++++------------------
 1 file changed, 30 insertions(+), 18 deletions(-)

diff --git a/inference.py b/inference.py
index 01e298f..36953cd 100644
--- a/inference.py
+++ b/inference.py
@@ -33,7 +33,19 @@ def show_sample(img_tensor, outputs, detection_threshold):
     plt.show()
 
 def infere_model(test_loader, model, detection_th=0.8):
-    pass
+
+    print('Validation')
+
+    prog_bar = tqdm(test_loader, total=len(test_loader))
+    for samples, targets in prog_bar:
+        images = list(image.to(DEVICE) for image in samples)
+        targets = [{k: v.to(DEVICE) for k, v in t.items()} for t in targets]
+
+        with torch.inference_mode():
+            outputs = model(images)
+
+        embed()
+
 
 if __name__ == '__main__':
     model = create_model(num_classes=NUM_CLASSES)
@@ -41,26 +53,26 @@ if __name__ == '__main__':
     model.load_state_dict(checkpoint["model_state_dict"])
     model.to(DEVICE).eval()
 
-    # test_data = create_train_or_test_dataset(TRAIN_DIR, train=False)
-    # test_loader = create_valid_loader(test_data)
-
-
-    # infere_model(test_loader, model)
+    test_data = create_train_or_test_dataset(TRAIN_DIR, train=False)
+    test_loader = create_valid_loader(test_data)
 
-    detection_threshold = 0.8
-    frame_count = 0
-    total_fps = 0
-    test_images = glob.glob(f"{TRAIN_DIR}/*.png")
 
-    for i in tqdm(np.arange(len(test_images))):
-        image_name = test_images[i].split(os.path.sep)[-1].split('.')[0]
+    infere_model(test_loader, model)
 
-        img = Image.open(test_images[i])
-        img_tensor = F.to_tensor(img.convert('RGB')).unsqueeze(dim=0)
-
-        with torch.inference_mode():
-            outputs = model(img_tensor.to(DEVICE))
+    # detection_threshold = 0.8
+    # frame_count = 0
+    # total_fps = 0
+    # test_images = glob.glob(f"{TRAIN_DIR}/*.png")
 
-        print(len(outputs[0]['boxes']))
+    # for i in tqdm(np.arange(len(test_images))):
+    #     image_name = test_images[i].split(os.path.sep)[-1].split('.')[0]
+    #
+    #     img = Image.open(test_images[i])
+    #     img_tensor = F.to_tensor(img.convert('RGB')).unsqueeze(dim=0)
+    #
+    #     with torch.inference_mode():
+    #         outputs = model(img_tensor.to(DEVICE))
+    #
+    #     print(len(outputs[0]['boxes']))
 
         # show_sample(img_tensor, outputs, detection_threshold)
\ No newline at end of file