minor changes & more cleanup

model.py

@@ -715,7 +715,7 @@ def refine_detections_graph(rois, probs, deltas, window, config):
     conf_keep = tf.where(class_scores >= config.DETECTION_MIN_CONFIDENCE)[:,0]
 
     if config.DETECTION_MIN_CONFIDENCE:
-    keep = tf.sparse_tensor_to_dense(tf.sets.set_intersection(
+        keep = tf.sparse_tensor_to_dense(tf.sets.set_intersection(
             tf.expand_dims(keep, 0), tf.expand_dims(conf_keep, 0)))[0]
 
     # Apply per-class NMS
@@ -744,7 +744,7 @@ def refine_detections_graph(rois, probs, deltas, window, config):
                 iou_threshold=config.DETECTION_NMS_THRESHOLD)
 
         # Map indicies
-        cur_keep_indexes = tf.gather(keep, tf.gather(ixs, class_keep))
+        cur_keep_indexes = tf.gather(tf.cast(keep,tf.int32), tf.gather(ixs, class_keep))
         return i+1, tf.concat([ret,cur_keep_indexes], axis=0)
 
     nums_iters = tf.shape(uniq_pre_nms_class_ids)[0] # unique class ids
@@ -759,9 +759,9 @@ def refine_detections_graph(rois, probs, deltas, window, config):
 
     # remove initial_value background
     nms_keep = tf.gather(nms_keep, tf.where(nms_keep >= 0)[:,0])
-
-    keep = tf.sparse_tensor_to_dense(tf.sets.set_intersection(tf.expand_dims(keep,0), tf.expand_dims(nms_keep,0)))[0]
     keep = tf.cast(keep, tf.int32)
+    keep = tf.sparse_tensor_to_dense(tf.sets.set_intersection(tf.expand_dims(keep,0), tf.expand_dims(nms_keep,0)))[0]
+
 
     # Keep top detections
     roi_count = tf.convert_to_tensor(config.DETECTION_MAX_INSTANCES)
@@ -775,7 +775,6 @@ def refine_detections_graph(rois, probs, deltas, window, config):
     refined_rois_keep = tf.gather(tf.to_float(refined_rois), keep)
     class_ids_keep = tf.gather(tf.to_float(class_ids), keep)[..., tf.newaxis]
     class_scores_keep = tf.gather(class_scores, keep)[..., tf.newaxis]
-
     # Arrange output as [N, (y1, x1, y2, x2, class_id, score)]
     # Coordinates are in image domain.
     detections = tf.concat((refined_rois_keep, class_ids_keep,
@@ -784,14 +783,12 @@ def refine_detections_graph(rois, probs, deltas, window, config):
     # Pad with zeros if detections < DETECTION_MAX_INSTANCES
     num_detections = tf.shape(detections)[0]
     gap = roi_count - num_detections
-    print(gap, roi_count, num_detections)
     pred = tf.less(tf.constant(0), gap)
     #assert gap >= 0
     #if gap > 0:
     #    paddings = tf.constant([[0, gap], [0, 0]])
     #    detections = tf.pad(detections, paddings, "CONSTANT")
     def pad_detections():
-        print(detections.shape)
         return tf.pad(detections, [(0, gap), (0, 0)], "CONSTANT")
 
     detections = tf.cond(pred, pad_detections, lambda: detections)
@@ -816,11 +813,9 @@ class DetectionLayer(KE.Layer):
         mrcnn_class = inputs[1]
         mrcnn_bbox  = inputs[2]
         image_meta  = inputs[3]
-        print(rois.shape, mrcnn_class.shape, mrcnn_bbox.shape, image_meta.shape)
 
         #parse_image_meta can be reused as slicing works same way in TF & numpy
-        _, _, window, _ = parse_image_meta(image_meta)
-        print('window after: ', window.shape)
+        _, _, window, _ = parse_image_meta_graph(image_meta)
         detections_batch = utils.batch_slice(
                 [rois, mrcnn_class, mrcnn_bbox, window],
                 lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
@@ -832,7 +827,6 @@ class DetectionLayer(KE.Layer):
         #detections_batch = np.array(detections_batch).astype(np.float32)
         # Reshape output
         # [batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
-
         return tf.reshape(
                 detections_batch,
                 [self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6])