change mean/std to /255.0

fluid/PaddleCV/yolov3/image_utils.py

@@ -121,7 +121,7 @@ def random_interp(img, size, interp=None):
     img = cv2.resize(img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=interp)
     return img
 
-def random_expand(img, gtboxes, max_ratio=4., fill=None, keep_ratio=True, thresh=0.5):
+def random_expand(img, gtboxes, max_ratio=2., fill=None, keep_ratio=True, thresh=0.5):
     if random.random() > thresh:
         return img, gtboxes
 

fluid/PaddleCV/yolov3/reader.py

@@ -38,23 +38,23 @@ class DataSetReader(object):
         self.has_parsed_categpry = False
 
     def _parse_dataset_dir(self, mode):
-        cfg.data_dir = "dataset/coco"
-        cfg.train_file_list = 'annotations/instances_val2017.json'
-        cfg.train_data_dir = 'val2017'
-        cfg.dataset = "coco2017"
-        # if 'coco2014' in cfg.dataset:
-        #     cfg.train_file_list = 'annotations/instances_train2014.json'
-        #     cfg.train_data_dir = 'train2014'
-        #     cfg.val_file_list = 'annotations/instances_val2014.json'
-        #     cfg.val_data_dir = 'val2014'
-        # elif 'coco2017' in cfg.dataset:
-        #     cfg.train_file_list = 'annotations/instances_train2017.json'
-        #     cfg.train_data_dir = 'train2017'
-        #     cfg.val_file_list = 'annotations/instances_val2017.json'
-        #     cfg.val_data_dir = 'val2017'
-        # else:
-        #     raise NotImplementedError('Dataset {} not supported'.format(
-        #         cfg.dataset))
+        # cfg.data_dir = "dataset/coco"
+        # cfg.train_file_list = 'annotations/instances_val2017.json'
+        # cfg.train_data_dir = 'val2017'
+        # cfg.dataset = "coco2017"
+        if 'coco2014' in cfg.dataset:
+            cfg.train_file_list = 'annotations/instances_train2014.json'
+            cfg.train_data_dir = 'train2014'
+            cfg.val_file_list = 'annotations/instances_val2014.json'
+            cfg.val_data_dir = 'val2014'
+        elif 'coco2017' in cfg.dataset:
+            cfg.train_file_list = 'annotations/instances_train2017.json'
+            cfg.train_data_dir = 'train2017'
+            cfg.val_file_list = 'annotations/instances_val2017.json'
+            cfg.val_data_dir = 'val2017'
+        else:
+            raise NotImplementedError('Dataset {} not supported'.format(
+                cfg.dataset))
 
         if mode == 'train':
             cfg.train_file_list = os.path.join(cfg.data_dir, cfg.train_file_list)
@@ -157,10 +157,11 @@ class DataSetReader(object):
             im_scale_x = size / float(w)
             im_scale_y = size / float(h)
             out_img = cv2.resize(im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=cv2.INTER_LINEAR)
-            mean = np.array(mean).reshape((1, 1, -1))
-            std = np.array(std).reshape((1, 1, -1))
-            out_img = (out_img / 255.0 - mean) / std
-            out_img = out_img.transpose((2, 0, 1))
+            # mean = np.array(mean).reshape((1, 1, -1))
+            # std = np.array(std).reshape((1, 1, -1))
+            # out_img = (out_img / 255.0 - mean) / std
+            # out_img = out_img.transpose((2, 0, 1))
+            out_img = im.astype('float32').transpose((2, 0, 1)) / 255.0
 
             return (out_img, int(img['id']), (h, w))
 
@@ -184,10 +185,11 @@ class DataSetReader(object):
 
             im, gt_boxes, gt_labels, gt_scores = image_utils.image_augment(im, gt_boxes, gt_labels, gt_scores, size, mean)
 
-            mean = np.array(mean).reshape((1, 1, -1))
-            std = np.array(std).reshape((1, 1, -1))
-            out_img = (im / 255.0 - mean) / std
-            out_img = out_img.transpose((2, 0, 1)).astype('float32')
+            # mean = np.array(mean).reshape((1, 1, -1))
+            # std = np.array(std).reshape((1, 1, -1))
+            # out_img = (im / 255.0 - mean) / std
+            # out_img = out_img.transpose((2, 0, 1)).astype('float32')
+            out_img = im.astype('float32').transpose((2, 0, 1)) / 255.0
 
             return (out_img, gt_boxes, gt_labels, gt_scores)