[FasterRCNN] suppress tf logging; fix nan precision (#494)

examples/FasterRCNN/data.py

@@ -226,7 +226,7 @@ def get_train_dataflow(add_mask=False):
             if not len(boxes):
                 raise MalformedData("No valid gt_boxes!")
         except MalformedData as e:
-            log_once("Input {} is invalid for training: {}".format(fname, str(e)), 'warn')
+            log_once("Input {} is filtered for training: {}".format(fname, str(e)), 'warn')
             return None
 
         ret = [im, fm_labels, fm_boxes, boxes, klass]

examples/FasterRCNN/model.py

@@ -78,16 +78,16 @@ def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
         with tf.device('/cpu:0'):
             for th in [0.5, 0.2, 0.1]:
                 valid_prediction = tf.cast(valid_label_prob > th, tf.int32)
-                nr_pos_prediction = tf.count_nonzero(valid_prediction, name='num_pos_prediction')
+                nr_pos_prediction = tf.reduce_sum(valid_prediction, name='num_pos_prediction')
                 pos_prediction_corr = tf.count_nonzero(tf.logical_and(
                     valid_label_prob > th,
                     tf.equal(valid_prediction, valid_anchor_labels)))
                 summaries.append(tf.truediv(
                     pos_prediction_corr,
                     nr_pos, name='recall_th{}'.format(th)))
-                summaries.append(tf.truediv(
-                    pos_prediction_corr,
-                    nr_pos_prediction, name='precision_th{}'.format(th)))
+                precision = tf.truediv(pos_prediction_corr, nr_pos_prediction)
+                precision = tf.where(tf.equal(nr_pos_prediction, 0), 0, precision, name='precision_th{}'.format(th))
+                summaries.append(precision)
 
     label_loss = tf.nn.sigmoid_cross_entropy_with_logits(
         labels=tf.to_float(valid_anchor_labels), logits=valid_label_logits)

examples/FasterRCNN/utils/box_ops.py

@@ -75,10 +75,12 @@ def get_iou_callable():
     """
     Get a pairwise box iou callable.
     """
-    os.environ['CUDA_VISIBLE_DEVICES'] = ''
+    os.environ['CUDA_VISIBLE_DEVICES'] = ''  # we don't want the dataflow process to touch CUDA
     with tf.Graph().as_default(), tf.device('/cpu:0'):
         A = tf.placeholder(tf.float32, shape=[None, 4])
         B = tf.placeholder(tf.float32, shape=[None, 4])
         iou = pairwise_iou(A, B)
+        tf.logging.set_verbosity(tf.logging.FATAL)  # TF will warn about GPU not found
         sess = tf.Session(config=get_default_sess_config())
+        tf.logging.set_verbosity(tf.logging.INFO)
         return sess.make_callable(iou, [A, B])