remove deprecated impl in BN. catch more exception in saver

tensorpack/callbacks/saver.py

@@ -73,8 +73,9 @@ class ModelSaver(Callback):
                 global_step=tf.train.get_global_step(),
                 write_meta_graph=False)
             logger.info("Model saved to %s." % tf.train.get_checkpoint_state(self.checkpoint_dir).model_checkpoint_path)
-        except (OSError, IOError):   # disk error sometimes.. just ignore it
-            logger.exception("Exception in ModelSaver.trigger_epoch!")
+        except (OSError, IOError, tf.errors.PermissionDeniedError,
+                tf.errors.ResourceExhaustedError):   # disk error sometimes.. just ignore it
+            logger.exception("Exception in ModelSaver!")
 
 
 class MinSaver(Callback):

tensorpack/models/batch_norm.py

@@ -17,85 +17,6 @@ __all__ = ['BatchNorm', 'BatchRenorm']
 # eps: torch: 1e-5. Lasagne: 1e-4
 
 
-# XXX This is deprecated. Only kept for future reference.
-@layer_register(log_shape=False)
-def BatchNormV1(x, use_local_stat=None, decay=0.9, epsilon=1e-5):
-    shape = x.get_shape().as_list()
-    assert len(shape) in [2, 4]
-
-    n_out = shape[-1]  # channel
-    assert n_out is not None
-    beta = tf.get_variable('beta', [n_out],
-                           initializer=tf.constant_initializer())
-    gamma = tf.get_variable('gamma', [n_out],
-                            initializer=tf.constant_initializer(1.0))
-
-    if len(shape) == 2:
-        batch_mean, batch_var = tf.nn.moments(x, [0], keep_dims=False)
-    else:
-        batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], keep_dims=False)
-    # just to make a clear name.
-    batch_mean = tf.identity(batch_mean, 'mean')
-    batch_var = tf.identity(batch_var, 'variance')
-
-    emaname = 'EMA'
-    ctx = get_current_tower_context()
-    if use_local_stat is None:
-        use_local_stat = ctx.is_training
-    if use_local_stat != ctx.is_training:
-        logger.warn("[BatchNorm] use_local_stat != is_training")
-
-    if use_local_stat:
-        # training tower
-        if ctx.is_training:
-            # reuse = tf.get_variable_scope().reuse
-            with tf.variable_scope(tf.get_variable_scope(), reuse=False):
-                # BatchNorm in reuse scope can be tricky! Moving mean/variance are not reused
-                with tf.name_scope(None):  # https://github.com/tensorflow/tensorflow/issues/2740
-                    # if reuse=True, try to find and use the existing statistics
-                    # how to use multiple tensors to update one EMA? seems impossbile
-                    ema = tf.train.ExponentialMovingAverage(decay=decay, name=emaname)
-                    ema_apply_op = ema.apply([batch_mean, batch_var])
-                    ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)
-                    if ctx.is_main_training_tower:
-                        # inside main training tower
-                        add_model_variable(ema_mean)
-                        add_model_variable(ema_var)
-    else:
-        # no apply() is called here, no magic vars will get created,
-        # no reuse issue will happen
-        assert not ctx.is_training
-        with tf.name_scope(None):
-            ema = tf.train.ExponentialMovingAverage(decay=decay, name=emaname)
-            mean_var_name = ema.average_name(batch_mean)
-            var_var_name = ema.average_name(batch_var)
-            if ctx.is_main_tower:
-                # main tower, but needs to use global stat. global stat must be from outside
-                # when reuse=True, the desired variable name could
-                # actually be different, because a different var is created
-                # for different reuse tower
-                ema_mean = tf.get_variable('mean/' + emaname, [n_out])
-                ema_var = tf.get_variable('variance/' + emaname, [n_out])
-            else:
-                # use statistics in another tower
-                G = tf.get_default_graph()
-                ema_mean = ctx.find_tensor_in_main_tower(G, mean_var_name + ':0')
-                ema_var = ctx.find_tensor_in_main_tower(G, var_var_name + ':0')
-
-    if use_local_stat:
-        batch = tf.cast(tf.shape(x)[0], tf.float32)
-        mul = tf.where(tf.equal(batch, 1.0), 1.0, batch / (batch - 1))
-        batch_var = batch_var * mul  # use unbiased variance estimator in training
-
-        with tf.control_dependencies([ema_apply_op] if ctx.is_training else []):
-            # only apply EMA op if is_training
-            return tf.nn.batch_normalization(
-                x, batch_mean, batch_var, beta, gamma, epsilon, 'output')
-    else:
-        return tf.nn.batch_normalization(
-            x, ema_mean, ema_var, beta, gamma, epsilon, 'output')
-
-
 def get_bn_variables(n_out, use_scale, use_bias, gamma_init):
     if use_bias:
         beta = tf.get_variable('beta', [n_out], initializer=tf.constant_initializer())
@@ -310,7 +231,7 @@ def BatchRenorm(x, rmax, dmax, decay=0.9, epsilon=1e-5,
             xn = tf.nn.batch_normalization(
                 x, moving_mean, moving_var, beta, gamma, epsilon)
 
-    if ctx.is_main_training_tower:
+    if ctx.is_main_training_tower or ctx.has_own_variables:
         ret = update_bn_ema(xn, batch_mean, batch_var, moving_mean, moving_var, decay)
     else:
         ret = tf.identity(xn, name='output')