fit for darknet

fluid/PaddleCV/yolov3/config/config.py

@@ -72,15 +72,15 @@ _C.pixel_stds = [0.229, 0.224, 0.225]
 _C.learning_rate = 0.001
 
 # maximum number of iterations
-_C.max_iter = 500200
+_C.max_iter = 500000
 
 # warm up to learning rate 
 _C.warm_up_iter = 4000
 _C.warm_up_factor = 0.
 
 # lr steps_with_decay
-_C.lr_steps = [400000, 450000]
-_C.lr_gamma = 0.1
+_C.lr_steps = [300000, 400000, 450000]
+_C.lr_gamma = [0.2, 0.5, 0.1]
 
 # L2 regularization hyperparameter
 _C.weight_decay = 0.0005

fluid/PaddleCV/yolov3/image_utils.py

@@ -105,7 +105,7 @@ def random_flip(img, gtboxes, thresh=0.5):
         gtboxes[:, 0] = 1.0 - gtboxes[:, 0]
     return img, gtboxes
 
-def random_interp(img, size):
+def random_interp(img, size, interp=None):
     interp_method = [
         cv2.INTER_NEAREST,
         cv2.INTER_LINEAR,
@@ -113,7 +113,8 @@ def random_interp(img, size):
         cv2.INTER_CUBIC,
         cv2.INTER_LANCZOS4,
         ]
-    interp = interp_method[random.randint(0, len(interp_method) - 1)]
+    if not interp or interp not in interp_method:
+        interp = interp_method[random.randint(0, len(interp_method) - 1)]
     h, w, _ = img.shape
     im_scale_x = size / float(w)
     im_scale_y = size / float(h)
@@ -151,6 +152,13 @@ def random_expand(img, gtboxes, max_ratio=4., fill=None, keep_ratio=True, thresh
 
     return out_img.astype('uint8'), gtboxes
 
+def shuffle_gtbox(gtbox, gtlabel, gtscore):
+    gt = np.concatenate([gtbox, gtlabel[:, np.newaxis], gtscore[:, np.newaxis]], axis=1)
+    idx = np.arange(gt.shape[1])
+    np.random.shuffle(idx)
+    gt = gt[idx, :]
+    return gt[:, :4], gt[:, 4], gt[:, 5]
+
 def image_mixup(img1, gtboxes1, gtlabels1, gtscores1, img2, gtboxes2, gtlabels2, gtscores2):
     factor = np.random.beta(1.5, 1.5)
     factor = max(0.0, min(1.0, factor))
@@ -201,8 +209,9 @@ def image_augment(img, gtboxes, gtlabels, gtscores,  size, means=None):
     img = random_distort(img)
     img, gtboxes = random_expand(img, gtboxes, fill=means)
     img, gtboxes, gtlabels, gtscores = random_crop(img, gtboxes, gtlabels, gtscores)
-    img = random_interp(img, size)
+    img = random_interp(img, size, cv2.INTER_LINEAR)
     img, gtboxes = random_flip(img, gtboxes)
+    gtboxes, gtlabels, gtscores = shuffle_gtbox(gtboxes, gtlabels, gtscores)
 
     return img.astype('float32'), gtboxes.astype('float32'), \
             gtlabels.astype('int32'), gtscores.astype('float32')

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)
@@ -156,7 +156,7 @@ class DataSetReader(object):
             h, w, _ = im.shape
             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_CUBIC)
+            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
@@ -184,11 +184,6 @@ class DataSetReader(object):
 
             im, gt_boxes, gt_labels, gt_scores = image_utils.image_augment(im, gt_boxes, gt_labels, gt_scores, size, mean)
 
-            # h, w, _ = im.shape
-            # im_scale_x = size / float(w)
-            # im_scale_y = size / float(h)
-            # im = cv2.resize(im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=cv2.INTER_CUBIC)
-            
             mean = np.array(mean).reshape((1, 1, -1))
             std = np.array(std).reshape((1, 1, -1))
             out_img = (im / 255.0 - mean) / std

fluid/PaddleCV/yolov3/utility.py

@@ -115,9 +115,9 @@ def parse_args():
     # TRAIN TEST INFER
     add_arg('input_size',       int,    608,    "Image input size of YOLOv3.")
     add_arg('random_shape',     bool,   True,     "Resize to random shape for train reader.")
-    add_arg('label_smooth',     bool,   True,     "Use label smooth in class label.")
-    add_arg('no_mixup_iter',    int,    40000,      "Disable mixup in last N iter.")
-    add_arg('valid_thresh',     float,  0.01,    "Valid confidence score for NMS.")
+    add_arg('label_smooth',     bool,   False,     "Use label smooth in class label.")
+    add_arg('no_mixup_iter',    int,    500200,      "Disable mixup in last N iter.")
+    add_arg('valid_thresh',     float,  0.005,    "Valid confidence score for NMS.")
     add_arg('nms_thresh',       float,  0.45,    "NMS threshold.")
     add_arg('nms_topk',         int,    400,    "The number of boxes to perform NMS.")
     add_arg('nms_posk',         int,    100,    "The number of boxes of NMS output.")