minor fix for cornernet & yolov4 (#621)

ppdet/modeling/anchor_heads/corner_head.py

@@ -23,12 +23,8 @@ from paddle.fluid.initializer import Constant
 from ..backbones.hourglass import _conv_norm, kaiming_init
 from ppdet.core.workspace import register
 import numpy as np
-try:
-    import cornerpool_lib
-except:
-    print(
-        "warning: cornerpool_lib not found, compile in ext_op at first if needed"
-    )
+import logging
+logger = logging.getLogger(__name__)
 
 __all__ = ['CornerHead']
 
@@ -247,6 +243,10 @@ class CornerHead(object):
                  ae_threshold=1,
                  num_dets=1000,
                  top_k=100):
+        try:
+            import cornerpool_lib
+        except:
+            logger.error("cornerpool_lib not found, compile in ext_op at first")
         self.train_batch_size = train_batch_size
         self.test_batch_size = test_batch_size
         self.num_classes = num_classes

ppdet/modeling/architectures/cornernet_squeeze.py

@@ -59,7 +59,7 @@ class CornerNetSqueeze(object):
         body_feats = self.backbone(im)
         if self.fpn is not None:
             body_feats, _ = self.fpn.get_output(body_feats)
-            body_feats = [body_feats.values()[-1]]
+            body_feats = [list(body_feats.values())[-1]]
         if mode == 'train':
             target_vars = [
                 'tl_heatmaps', 'br_heatmaps', 'tag_masks', 'tl_regrs',

ppdet/modeling/losses/yolo_loss.py

@@ -166,7 +166,7 @@ class YOLOv3Loss(object):
             #    self.scale_x_y, Sequence) else self.scale_x_y[i]
             loss_obj_pos, loss_obj_neg = self._calc_obj_loss(
                 output, obj, tobj, gt_box, self._batch_size, anchors,
-                num_classes, downsample, self._ignore_thresh, scale_x_y)
+                num_classes, downsample, self._ignore_thresh)
 
             loss_cls = fluid.layers.sigmoid_cross_entropy_with_logits(cls, tcls)
             loss_cls = fluid.layers.elementwise_mul(loss_cls, tobj, axis=0)
@@ -276,7 +276,7 @@ class YOLOv3Loss(object):
         return (tx, ty, tw, th, tscale, tobj, tcls)
 
     def _calc_obj_loss(self, output, obj, tobj, gt_box, batch_size, anchors,
-                       num_classes, downsample, ignore_thresh, scale_x_y):
+                       num_classes, downsample, ignore_thresh):
         # A prediction bbox overlap any gt_bbox over ignore_thresh, 
         # objectness loss will be ignored, process as follows: