small updates

examples/A3C-Gym/README.md

@@ -38,9 +38,9 @@ Watch the agent play:
 Dump some videos:
 `./train-atari.py --task dump_video --load Breakout-v0.npz --env Breakout-v0 --output output_dir --episode 3`
 
-This table lists available pretrained models and scores (average over 100 episodes),
+This table lists available pretrained models and __scores__ (average over 100 episodes),
 with their submission links.
-The site is not maintained any more so the links might become invalid any time.
+The old submission site is not maintained any more so the links might become invalid any time.
 
 | | | | |
 | - | - | - | - |

examples/FasterRCNN/eval.py

@@ -71,7 +71,7 @@ def detect_one_image(img, model_func):
     orig_shape = img.shape[:2]
     resizer = CustomResize(cfg.PREPROC.SHORT_EDGE_SIZE, cfg.PREPROC.MAX_SIZE)
     resized_img = resizer.augment(img)
-    scale = (resized_img.shape[0] * 1.0 / img.shape[0] + resized_img.shape[1] * 1.0 / img.shape[1]) / 2
+    scale = np.sqrt(resized_img.shape[0] * 1.0 / img.shape[0] * resized_img.shape[1] / img.shape[1])
     boxes, probs, labels, *masks = model_func(resized_img)
     boxes = boxes / scale
     # boxes are already clipped inside the graph, but after the floating point scaling, this may not be true any more.
@@ -113,8 +113,8 @@ def eval_coco(df, detect_func):
                 res = {
                     'image_id': img_id,
                     'category_id': cat_id,
-                    'bbox': list(map(lambda x: float(round(x, 1)), box)),
-                    'score': float(round(r.score, 2)),
+                    'bbox': list(map(lambda x: round(float(x), 2), box)),
+                    'score': round(r.score, 3),
                 }
 
                 # also append segmentation to results

examples/FasterRCNN/train.py

@@ -240,8 +240,7 @@ class ResNetC4Model(DetectionModel):
             total_cost = tf.add_n([
                 rpn_label_loss, rpn_box_loss,
                 fastrcnn_label_loss, fastrcnn_box_loss,
-                mrcnn_loss,
-                wd_cost], 'total_cost')
+                mrcnn_loss, wd_cost], 'total_cost')
 
             add_moving_summary(total_cost, wd_cost)
             return total_cost * (1. / cfg.TRAIN.NUM_GPUS)