#113 bug fix. Python 3 compartible.

libs/boxes/__init__.py

@@ -6,8 +6,8 @@
 # --------------------------------------------------------
 from . import cython_nms
 from . import cython_bbox
-import nms
-import timer
+from . import nms
+from . import timer
 from .anchor import anchors
 from .anchor import anchors_plane
 from .roi import roi_cropping

libs/boxes/anchor.py

@@ -35,7 +35,7 @@ def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
   base_anchor = np.array([1, 1, base_size, base_size]) - 1
   ratio_anchors = _ratio_enum(base_anchor, ratios)
   anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
-                       for i in xrange(ratio_anchors.shape[0])])
+                       for i in range(ratio_anchors.shape[0])])
   return anchors
 
 def _whctrs(anchor):

libs/boxes/blob.py

@@ -20,7 +20,7 @@ def im_list_to_blob(ims):
     num_images = len(ims)
     blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
                     dtype=np.float32)
-    for i in xrange(num_images):
+    for i in range(num_images):
         im = ims[i]
         blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
 

libs/boxes/gprof2dot.py

@@ -2681,7 +2681,7 @@ def parse(self):
                 caller = self.get_function(fn)
                 call = Call(callee.id)
                 if isinstance(value, tuple):
-                    for i in xrange(0, len(value), 4):
+                    for i in range(0, len(value), 4):
                         nc, cc, tt, ct = value[i:i+4]
                         if CALLS in call:
                             call[CALLS] += cc

libs/datasets/coco.py

@@ -22,6 +22,18 @@
 }
 
 
+def correct_decode_raw(data, dtype):
+
+  # BUG: THERE WAS A BUG HERE, tf.decode_raw('', tf.float32) returns [0.] tensor not the [] tensor
+  # So we use correct_decode_raw instead of tf.decode_raw
+
+  result = tf.cond(tf.equal(data, tf.constant("")),
+                     lambda: tf.constant([], dtype=dtype),
+                     lambda: tf.decode_raw(data, dtype))
+
+  return result
+
+
 def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
   if split_name not in SPLITS_TO_SIZES:
     raise ValueError('split name %s was not recognized.' % split_name)
@@ -48,15 +60,15 @@ def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
   }
 
   def _masks_decoder(keys_to_tensors):
-    masks = tf.decode_raw(keys_to_tensors['label/gt_masks'], tf.uint8)
+    masks = correct_decode_raw(keys_to_tensors['label/gt_masks'], tf.uint8)
     width = tf.cast(keys_to_tensors['image/width'], tf.int32)
     height = tf.cast(keys_to_tensors['image/height'], tf.int32)
     instances = tf.cast(keys_to_tensors['label/num_instances'], tf.int32)
     mask_shape = tf.stack([instances, height, width])
     return tf.reshape(masks, mask_shape)
 
   def _gt_boxes_decoder(keys_to_tensors):
-    bboxes = tf.decode_raw(keys_to_tensors['label/gt_boxes'], tf.float32)
+    bboxes = correct_decode_raw(keys_to_tensors['label/gt_boxes'], tf.float32)
     instances = tf.cast(keys_to_tensors['label/num_instances'], tf.int32)
     bboxes_shape = tf.stack([instances, 5])
     return tf.reshape(bboxes, bboxes_shape)
@@ -117,15 +129,15 @@ def read(tfrecords_filename):
   ih = tf.cast(features['image/height'], tf.int32)
   iw = tf.cast(features['image/width'], tf.int32)
   num_instances = tf.cast(features['label/num_instances'], tf.int32)
-  image = tf.decode_raw(features['image/encoded'], tf.uint8)
+  image = correct_decode_raw(features['image/encoded'], tf.uint8)
   imsize = tf.size(image)
   image = tf.cond(tf.equal(imsize, ih * iw), \
           lambda: tf.image.grayscale_to_rgb(tf.reshape(image, (ih, iw, 1))), \
           lambda: tf.reshape(image, (ih, iw, 3)))
 
-  gt_boxes = tf.decode_raw(features['label/gt_boxes'], tf.float32)
+  gt_boxes = correct_decode_raw(features['label/gt_boxes'], tf.float32)
   gt_boxes = tf.reshape(gt_boxes, [num_instances, 5])
-  gt_masks = tf.decode_raw(features['label/gt_masks'], tf.uint8)
+  gt_masks = correct_decode_raw(features['label/gt_masks'], tf.uint8)
   gt_masks = tf.cast(gt_masks, tf.int32)
   gt_masks = tf.reshape(gt_masks, [num_instances, ih, iw])
 

libs/datasets/dataset_factory.py

@@ -16,6 +16,8 @@ def get_dataset(dataset_name, split_name, dataset_dir,
         file_pattern = dataset_name + '_' + split_name + '*.tfrecord' 
 
     tfrecords = glob.glob(dataset_dir + '/records/' + file_pattern)
+    assert len(tfrecords)>0, "haven't found any tfrecord(did you run train.py from code root?). we were looking at %s." % dataset_dir + '/records/' + file_pattern
+
     image, ih, iw, gt_boxes, gt_masks, num_instances, img_id = coco.read(tfrecords)
 
     image, gt_boxes, gt_masks = coco_preprocess.preprocess_image(image, gt_boxes, gt_masks, is_training)

libs/datasets/download_and_convert_coco.py

@@ -31,8 +31,7 @@
 ]
 
 FLAGS = tf.app.flags.FLAGS
-tf.app.flags.DEFINE_boolean('vis',  False,
-                          'Show some visual masks')
+tf.app.flags.DEFINE_boolean('vis',  False, 'Show some visual masks')
 
 
 def download_and_uncompress_zip(zip_url, dataset_dir):
@@ -305,15 +304,16 @@ def _add_to_tfrecord(record_dir, image_dir, annotation_dir, split_name):
 
             img_raw = img.tostring()
             mask_raw = mask.tostring()
-            
+
             example = _to_tfexample_coco_raw(
               img_id,
               img_raw,
               mask_raw,
               height, width, gt_boxes.shape[0],
               gt_boxes.tostring(), masks.tostring())
-            
+
             tfrecord_writer.write(example.SerializeToString())
+
   sys.stdout.write('\n')
   sys.stdout.flush()
 

libs/layers/sample.py

@@ -114,14 +114,14 @@ def sample_rpn_outputs_wrt_gt_boxes(boxes, scores, gt_boxes, is_training=False,
             gt_argmax_overlaps = overlaps.argmax(axis=0) # G
             fg_inds = np.union1d(gt_argmax_overlaps, fg_inds)
 
-	fg_rois = int(min(fg_inds.size, cfg.FLAGS.rois_per_image * cfg.FLAGS.fg_roi_fraction))
-      	if fg_inds.size > 0 and fg_rois < fg_inds.size:
-       	   fg_inds = np.random.choice(fg_inds, size=fg_rois, replace=False)
-      	
-	# TODO: sampling strategy
-      	bg_inds = np.where((max_overlaps < cfg.FLAGS.bg_threshold))[0]
-      	bg_rois = max(min(cfg.FLAGS.rois_per_image - fg_rois, fg_rois * 3), 8)#64
-      	if bg_inds.size > 0 and bg_rois < bg_inds.size:
+        fg_rois = int(min(fg_inds.size, cfg.FLAGS.rois_per_image * cfg.FLAGS.fg_roi_fraction))
+        if fg_inds.size > 0 and fg_rois < fg_inds.size:
+            fg_inds = np.random.choice(fg_inds, size=fg_rois, replace=False)
+
+        # TODO: sampling strategy
+        bg_inds = np.where((max_overlaps < cfg.FLAGS.bg_threshold))[0]
+        bg_rois = max(min(cfg.FLAGS.rois_per_image - fg_rois, fg_rois * 3), 8)#64
+        if bg_inds.size > 0 and bg_rois < bg_inds.size:
            bg_inds = np.random.choice(bg_inds, size=bg_rois, replace=False)
 
         keep_inds = np.append(fg_inds, bg_inds)

libs/nets/pyramid_network.py

@@ -123,7 +123,9 @@ def _filter_negative_samples(labels, tensors):
 
     filtered = []
     for t in tensors:
-        tf.assert_equal(tf.shape(t)[0], tf.shape(labels)[0])
+        # tf.assert_equal(tf.shape(t)[0], tf.shape(labels)[0]) - I removed this assertion because it was never used.
+        # assertion is not automatically checked you should execute it in graph as any other operation
+
         f = tf.gather(t, keeps)
         filtered.append(f)
 

libs/setup.py

@@ -47,7 +47,7 @@ def locate_cuda():
     cudaconfig = {'home':home, 'nvcc':nvcc,
                   'include': pjoin(home, 'include'),
                   'lib64': pjoin(home, 'lib64')}
-    for k, v in cudaconfig.iteritems():
+    for k, v in cudaconfig.items():
         if not os.path.exists(v):
             raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
 
@@ -81,7 +81,7 @@ def customize_compiler_for_nvcc(self):
     # object but distutils doesn't have the ability to change compilers
     # based on source extension: we add it.
     def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
-        print extra_postargs
+        print(extra_postargs)
         if os.path.splitext(src)[1] == '.cu':
             # use the cuda for .cu files
             self.set_executable('compiler_so', CUDA['nvcc'])

train/train.py

@@ -170,11 +170,11 @@ def train():
                              FLAGS.im_batch,
                              is_training=True)
 
-    data_queue = tf.RandomShuffleQueue(capacity=32, min_after_dequeue=16,
-            dtypes=(
-                image.dtype, ih.dtype, iw.dtype, 
-                gt_boxes.dtype, gt_masks.dtype, 
-                num_instances.dtype, img_id.dtype)) 
+    data_queue = tf.RandomShuffleQueue(capacity=32, min_after_dequeue=16, dtypes=(
+                                                    image.dtype, ih.dtype, iw.dtype,
+                                                    gt_boxes.dtype, gt_masks.dtype,
+                                                    num_instances.dtype, img_id.dtype))
+
     enqueue_op = data_queue.enqueue((image, ih, iw, gt_boxes, gt_masks, num_instances, img_id))
     data_queue_runner = tf.train.QueueRunner(data_queue, [enqueue_op] * 4)
     tf.add_to_collection(tf.GraphKeys.QUEUE_RUNNERS, data_queue_runner)