Print warning when failed to enter the expected name scope (#1257)

examples/FasterRCNN/NOTES.md

@@ -19,7 +19,7 @@ This is a minimal implementation that simply contains these files:
 Data:
 
 1. It's easy to train on your own data, by calling `DatasetRegistry.register(name, lambda: YourDatasetSplit())`,
-	 and modify `cfg.DATA.*` accordingly.
+	 and modify `cfg.DATA.*` accordingly. Afterwards, "name" can be used in `cfg.DATA.TRAIN`.
 
 	`YourDatasetSplit` can be:
 

examples/FasterRCNN/modeling/generalized_rcnn.py

@@ -82,6 +82,16 @@ class GeneralizedRCNN(ModelDesc):
                 rpn_losses + head_losses + [wd_cost], 'total_cost')
             add_moving_summary(total_cost, wd_cost)
             return total_cost
+        else:
+            # Check that the model defines the tensors it declares for inference
+            # For existing models, they are defined in "fastrcnn_predictions(name_scope='output')"
+            G = tf.get_default_graph()
+            ns = G.get_name_scope()
+            for name in self.get_inference_tensor_names()[1]:
+                try:
+                    G.get_tensor_by_name('/'.join([ns, name + ':0']))
+                except KeyError:
+                    raise KeyError("Your model does not define the tensor '{}' in inference context.".format(name))
 
 
 class ResNetC4Model(GeneralizedRCNN):

tensorpack/tfutils/scope_utils.py

@@ -7,6 +7,7 @@ from contextlib import contextmanager
 
 from ..compat import tfv1 as tf
 from ..utils.argtools import graph_memoized
+from ..utils import logger
 from .common import get_tf_version_tuple
 
 __all__ = ['auto_reuse_variable_scope', 'cached_name_scope', 'under_name_scope']
@@ -66,6 +67,9 @@ def under_name_scope(name_scope=None):
         2. The 'name_scope' argument of the decorator.
         3. (default) The name of the decorated function itself.
 
+        If the name is taken and cannot be used, a warning will be
+        printed in the first case.
+
     Example:
 
     .. code-block:: python
@@ -86,10 +90,22 @@ def under_name_scope(name_scope=None):
     def _impl(func):
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
+            warn_incorrect_scope = 'name_scope' in kwargs
             scopename = kwargs.pop('name_scope', name_scope)
             if scopename is None:
                 scopename = func.__name__
 
+            if warn_incorrect_scope:
+                # cached_name_scope will try to reenter the existing scope
+                with cached_name_scope(scopename, top_level=False) as scope:
+                    scope = scope.strip('/')
+                    # but it can still conflict with an existing tensor
+                    if not scope.endswith(scopename):
+                        logger.warn(""" \
+Calling function {} with name_scope='{}', but actual name scope becomes '{}'.  \
+The name '{}' might be taken.""".format(func.__name__, scopename, scope.split('/')[-1], scopename))
+                    return func(*args, **kwargs)
+            else:
                 with tf.name_scope(scopename):
                     return func(*args, **kwargs)
         return wrapper

tensorpack/tfutils/unit_tests.py

+#-*- coding: utf-8 -*-
+#File:
+
+import unittest
+import tensorflow as tf
+
+from ..utils import logger
+from .scope_utils import under_name_scope
+
+
+class ScopeUtilsTest(unittest.TestCase):
+
+    @under_name_scope(name_scope='s')
+    def _f(self, check=True):
+        if check:
+            assert tf.get_default_graph().get_name_scope().endswith('s')
+        return True
+
+    def test_under_name_scope(self):
+        self.assertTrue(self._f())
+        with self.assertRaises(AssertionError):
+            self._f()  # name conflict
+
+    def test_under_name_scope_warning(self):
+        x = tf.placeholder(tf.float32, [3])
+        tf.nn.relu(x, name='s')
+        with self.assertLogs(logger=logger._logger, level='WARNING'):
+            self._f(check=False, name_scope='s')
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/run-tests.sh

@@ -15,6 +15,7 @@ python -c "import tensorflow as tf; tf.Operation._add_control_input"
 
 # run tests
 python -m tensorpack.callbacks.param_test
+python -m tensorpack.tfutils.unit_tests
 TENSORPACK_SERIALIZE=pyarrow python test_serializer.py
 TENSORPACK_SERIALIZE=msgpack python test_serializer.py