[FasterRCNN] no need to lower bound fg_ratio

examples/FasterRCNN/config.py

@@ -40,7 +40,7 @@ FASTRCNN_BATCH_PER_IM = 64
 FASTRCNN_BBOX_REG_WEIGHTS = np.array([10, 10, 5, 5], dtype='float32')
 FASTRCNN_FG_THRESH = 0.5
 # keep fg ratio in a batch in this range
-FASTRCNN_FG_RATIO = (0.1, 0.25)
+FASTRCNN_FG_RATIO = 0.25
 
 # testing -----------------------
 TEST_PRE_NMS_TOPK = 6000

examples/FasterRCNN/model.py

@@ -261,16 +261,13 @@ def sample_fast_rcnn_targets(boxes, gt_boxes, gt_labels):
         tf.int64)], 0)
     num_fg = tf.size(fg_inds)
     num_fg = tf.minimum(int(
-        config.FASTRCNN_BATCH_PER_IM * config.FASTRCNN_FG_RATIO[1]),
+        config.FASTRCNN_BATCH_PER_IM * config.FASTRCNN_FG_RATIO),
         num_fg, name='num_fg')
     fg_inds = tf.slice(tf.random_shuffle(fg_inds), [0], [num_fg])
 
     bg_inds = tf.where(tf.logical_not(fg_mask))[:, 0]
     num_bg = tf.size(bg_inds)
-    num_bg = tf.minimum(config.FASTRCNN_BATCH_PER_IM - num_fg, num_bg)
-    num_bg = tf.minimum(
-        num_bg,
-        num_fg * int(1.0 / config.FASTRCNN_FG_RATIO[0]), name='num_bg')    # don't include too many bg
+    num_bg = tf.minimum(config.FASTRCNN_BATCH_PER_IM - num_fg, num_bg, name='num_bg')
     bg_inds = tf.slice(tf.random_shuffle(bg_inds), [0], [num_bg])
 
     add_moving_summary(num_fg, num_bg)

examples/FasterRCNN/train.py

@@ -252,6 +252,7 @@ if __name__ == '__main__':
     if args.evaluate is not None:
         assert args.evaluate.endswith('.json')
         assert args.load
+        # autotune is too slow for inference
         os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
         offline_evaluate(args.load, args.evaluate)
         sys.exit()
@@ -283,7 +284,7 @@ if __name__ == '__main__':
         ],
         steps_per_epoch=stepnum,
         max_epoch=205000 // stepnum,
-        session_init=get_model_loader(args.load),
+        session_init=get_model_loader(args.load) if args.load else None,
         nr_tower=nr_gpu
     )
     SyncMultiGPUTrainerReplicated(cfg, gpu_prefetch=False).train()