fix for test

Author: longcw

datasets/imdb.py

@@ -64,9 +64,10 @@ def next_batch(self, size_index):
 
                 # multi-scale
                 w, h = cfg.multi_scale_inp_size[size_index]
-                gt_boxes = gt_boxes.astype(float)
-                gt_boxes[:, 0::2] *= float(w) / images.shape[1]
-                gt_boxes[:, 1::2] *= float(h) / images.shape[0]
+                gt_boxes = np.asarray(gt_boxes, dtype=np.float)
+                if len(gt_boxes) > 0:
+                    gt_boxes[:, 0::2] *= float(w) / images.shape[1]
+                    gt_boxes[:, 1::2] *= float(h) / images.shape[0]
                 images = cv2.resize(images, (w, h))
 
                 batch['images'].append(images)

test.py

@@ -24,7 +24,7 @@
 imdb_name = cfg.imdb_test
 # trained_model = cfg.trained_model
 trained_model = os.path.join(cfg.train_output_dir,
-                             'darknet19_voc07trainval_exp3_118.h5')
+                             'darknet19_voc07trainval_exp3_73.h5')
 output_dir = cfg.test_output_dir
 
 max_per_image = 300
@@ -126,7 +126,7 @@ def test_net(net, imdb, max_per_image=300, thresh=0.5, vis=False):
     # data loader
     imdb = VOCDataset(imdb_name, cfg.DATA_DIR, cfg.batch_size,
                       yolo_utils.preprocess_test,
-                      processes=2, shuffle=False, dst_size=cfg.multi_scale_inp_size)
+                      processes=1, shuffle=False, dst_size=cfg.multi_scale_inp_size)
 
     net = Darknet19()
     net_utils.load_net(trained_model, net)

utils/yolo.py

@@ -88,12 +88,13 @@ def preprocess_train(data, size_index):
 def preprocess_test(data, size_index):
 
     im, _, inp_size = data
-    inp_size = inp_size[size_index]
+
     if isinstance(im, str):
         im = cv2.imread(im)
     ori_im = np.copy(im)
 
-    if inp_size is not None:
+    if inp_size is not None and size_index is not None:
+        inp_size = inp_size[size_index]
         w, h = inp_size
         im = cv2.resize(im, (w, h))
     im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)