Replace all tf.size with tf.shape.

tf.size is not supported by TFLite builtin.

Author: mingxingtan

efficientdet/anchors.py

@@ -130,7 +130,7 @@ def nms_tf(dets, thresh):
 
   keep = tf.TensorArray(tf.int32, size=0, dynamic_size=True)
   index = 0
-  while tf.size(order) > 0:
+  while tf.shape(order)[0] > 0:
     i = order[0]
     keep = keep.write(index, i)
     xx1 = tf.maximum(x1[i], tf.gather(x1, order[1:]))
@@ -334,7 +334,7 @@ def _generate_detections_tf(cls_outputs,
   width = boxes[:, 3] - boxes[:, 1]
 
   detections = tf.stack([
-      tf.cast(tf.tile(image_id, [tf.size(top_detection_idx)]), tf.float32),
+      tf.cast(tf.tile(image_id, [tf.shape(top_detection_idx)[0]]), tf.float32),
       boxes[:, 0] * image_scale,
       boxes[:, 1] * image_scale,
       height * image_scale,

efficientdet/aug/autoaugment.py

@@ -657,7 +657,7 @@ def _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func,
 
   # If the bboxes are empty, then just give it _INVALID_BOX. The result
   # will be thrown away.
-  bboxes = tf.cond(tf.equal(tf.size(bboxes), 0),
+  bboxes = tf.cond(tf.equal(tf.shape(bboxes)[0], 0),
                    lambda: tf.constant(_INVALID_BOX),
                    lambda: bboxes)
 
@@ -1352,7 +1352,7 @@ def apply_bbox_cutout(image, bboxes, pad_fraction):
     return image
 
   # Check to see if there are boxes, if so then apply boxcutout.
-  image = tf.cond(tf.equal(tf.size(bboxes), 0), lambda: image,
+  image = tf.cond(tf.equal(tf.shape(bboxes)[0], 0), lambda: image,
                   lambda: apply_bbox_cutout(image, bboxes, pad_fraction))
 
   return image, bboxes

efficientdet/object_detection/matcher.py

@@ -92,7 +92,7 @@ def matched_column_indicator(self):
 
   def num_matched_columns(self):
     """Returns number (int32 scalar tensor) of matched columns."""
-    return tf.size(self.matched_column_indices())
+    return tf.shape(self.matched_column_indices())[0]
 
   def unmatched_column_indices(self):
     """Returns column indices that do not match any row.
@@ -114,7 +114,7 @@ def unmatched_column_indicator(self):
 
   def num_unmatched_columns(self):
     """Returns number (int32 scalar tensor) of unmatched columns."""
-    return tf.size(self.unmatched_column_indices())
+    return tf.shape(self.unmatched_column_indices())[0]
 
   def ignored_column_indices(self):
     """Returns column indices that are ignored (neither Matched nor Unmatched).
@@ -137,7 +137,7 @@ def ignored_column_indicator(self):
 
   def num_ignored_columns(self):
     """Returns number (int32 scalar tensor) of matched columns."""
-    return tf.size(self.ignored_column_indices())
+    return tf.shape(self.ignored_column_indices())[0]
 
   def unmatched_or_ignored_column_indices(self):
     """Returns column indices that are unmatched or ignored.

efficientdet/object_detection/tf_example_decoder.py

@@ -79,7 +79,7 @@ def _decode_png_mask(png_bytes):
     width = parsed_tensors['image/width']
     masks = parsed_tensors['image/object/mask']
     return tf.cond(
-        tf.greater(tf.size(masks), 0),
+        tf.greater(tf.shape(masks)[0], 0),
         lambda: tf.map_fn(_decode_png_mask, masks, dtype=tf.float32),
         lambda: tf.zeros([0, height, width], dtype=tf.float32))