[MaskRCNN] small change on data loading

examples/FasterRCNN/README.md

@@ -12,7 +12,7 @@ with the support of:
 
 ## Dependencies
 + Python 3; TensorFlow >= 1.6 (1.4 or 1.5 can run but may crash due to a TF bug);
-+ [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/pycocotools), OpenCV.
++ [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/), OpenCV.
 + Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/FasterRCNN/)
   from tensorpack model zoo. Use the models with "-AlignPadding".
 + COCO data. It needs to have the following directory structure:

examples/FasterRCNN/coco.py

@@ -116,8 +116,9 @@ class COCODetection(object):
         Add 'boxes', 'class', 'is_crowd' of this image to the dict, used by detection.
         If add_mask is True, also add 'segmentation' in coco poly format.
         """
-        ann_ids = self.coco.getAnnIds(imgIds=img['id'], iscrowd=None)
-        objs = self.coco.loadAnns(ann_ids)
+        # ann_ids = self.coco.getAnnIds(imgIds=img['id'])
+        # objs = self.coco.loadAnns(ann_ids)
+        objs = self.coco.imgToAnns[img['id']]  # equivalent but faster than the above two lines
 
         # clean-up boxes
         valid_objs = []

examples/FasterRCNN/config.py

@@ -164,6 +164,7 @@ def finalize_configs(is_training):
     Run some sanity checks, and populate some configs from others
     """
     _C.DATA.NUM_CLASS = _C.DATA.NUM_CATEGORY + 1  # +1 background
+    _C.DATA.BASEDIR = os.path.expanduser(_C.DATA.BASEDIR)
 
     assert _C.BACKBONE.NORM in ['FreezeBN', 'SyncBN', 'GN'], _C.BACKBONE.NORM
     if _C.BACKBONE.NORM != 'FreezeBN':

examples/FasterRCNN/train.py

@@ -89,7 +89,7 @@ class DetectionModel(ModelDesc):
         with tf.name_scope('fg_sample_patch_viz'):
             fg_sampled_patches = crop_and_resize(
                 image, fg_rcnn_boxes,
-                tf.zeros(tf.shape(fg_rcnn_boxes)[0], dtype=tf.int32), 300)
+                tf.zeros([tf.shape(fg_rcnn_boxes)[0]], dtype=tf.int32), 300)
             fg_sampled_patches = tf.transpose(fg_sampled_patches, [0, 2, 3, 1])
             fg_sampled_patches = tf.reverse(fg_sampled_patches, axis=[-1])  # BGR->RGB
             tf.summary.image('viz', fg_sampled_patches, max_outputs=30)
@@ -517,7 +517,8 @@ if __name__ == '__main__':
         logger.warn("TF<1.6 has a bug which may lead to crash in FasterRCNN training if you're unlucky.")
 
     args = parser.parse_args()
-    cfg.update_args(args.config)
+    if args.config:
+        cfg.update_args(args.config)
 
     MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()