a few debugs

wjtscn · Sep 21, 2019 · ed3579a · ed3579a
1 parent bb9bc40
commit ed3579a
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/detect_yolo3.py b/detect_yolo3.py
@@ -123,7 +123,7 @@ def get_dataset(dataset_name):
 def get_dataloader(dataset, batch_size):
     width, height = FLAGS.data_shape, FLAGS.data_shape
     batchify_fn = Tuple(Stack(), Pad(pad_val=-1), Stack())
-    loader = gluon.data.DataLoader(dataset.transform(YOLO3VideoInferenceTransform(width, height, 3)),
+    loader = gluon.data.DataLoader(dataset.transform(YOLO3VideoInferenceTransform(width, height)),
                                    batch_size, False, last_batch='keep', num_workers=FLAGS.num_workers,
                                    batchify_fn=batchify_fn)
     return loader
@@ -253,7 +253,7 @@ def load_predictions(save_dir, dataset, max_do=-1):
 
 
 def visualise_predictions(save_dir, dataset, trained_on_dataset, boxes,
-                          max_do=-1, display_gt=False, detection_thresh=0.5):
+                          max_do=-1, display_gt=False, detection_threshold=0.5):
     colors = dict()
     for i in range(200):
         colors[i] = (int(256 * random.random()), int(256 * random.random()), int(256 * random.random()))
@@ -276,7 +276,7 @@ def visualise_predictions(save_dir, dataset, trained_on_dataset, boxes,
                                bboxes=[list(g) for g in y[:, :4]],
                                scores=[1]*len(y),
                                labels=[g for g in y[:, 4]],
-                               thresh=detection_thresh,
+                               thresh=detection_threshold,
                                colors=colors_gt,
                                class_names=dataset.classes,
                                absolute_coordinates=True)
@@ -286,7 +286,7 @@ def visualise_predictions(save_dir, dataset, trained_on_dataset, boxes,
                                bboxes=[b[2:] for b in boxes[img_path]],
                                scores=[b[1] for b in boxes[img_path]],
                                labels=[b[0] for b in boxes[img_path]],
-                               thresh=detection_thresh,
+                               thresh=detection_threshold,
                                colors=colors,
                                class_names=trained_on_dataset.classes,
                                absolute_coordinates=False)