Update slim/nets.

- Set spatial squeeze to true for resnets
- Make activations configurable for resnets
- Make batch norm configurable for resnets
- Fix some bad comment wrapping in vgg.py.

slim/nets/resnet_utils.py

@@ -199,7 +199,9 @@ def stack_blocks_dense(net, blocks, output_stride=None,
 def resnet_arg_scope(weight_decay=0.0001,
                      batch_norm_decay=0.997,
                      batch_norm_epsilon=1e-5,
-                     batch_norm_scale=True):
+                     batch_norm_scale=True,
+                     activation_fn=tf.nn.relu,
+                     use_batch_norm=True):
   """Defines the default ResNet arg scope.
 
   TODO(gpapan): The batch-normalization related default values above are
@@ -215,6 +217,8 @@ def resnet_arg_scope(weight_decay=0.0001,
       normalizing activations by their variance in batch normalization.
     batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
       activations in the batch normalization layer.
+    activation_fn: The activation function which is used in ResNet.
+    use_batch_norm: Whether or not to use batch normalization.
 
   Returns:
     An `arg_scope` to use for the resnet models.
@@ -230,8 +234,8 @@ def resnet_arg_scope(weight_decay=0.0001,
       [slim.conv2d],
       weights_regularizer=slim.l2_regularizer(weight_decay),
       weights_initializer=slim.variance_scaling_initializer(),
-      activation_fn=tf.nn.relu,
-      normalizer_fn=slim.batch_norm,
+      activation_fn=activation_fn,
+      normalizer_fn=slim.batch_norm if use_batch_norm else None,
       normalizer_params=batch_norm_params):
     with slim.arg_scope([slim.batch_norm], **batch_norm_params):
       # The following implies padding='SAME' for pool1, which makes feature

slim/nets/resnet_v1.py

@@ -66,8 +66,14 @@ slim = tf.contrib.slim
 
 
 @slim.add_arg_scope
-def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
-               outputs_collections=None, scope=None):
+def bottleneck(inputs,
+               depth,
+               depth_bottleneck,
+               stride,
+               rate=1,
+               outputs_collections=None,
+               scope=None,
+               use_bounded_activations=False):
   """Bottleneck residual unit variant with BN after convolutions.
 
   This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
@@ -86,6 +92,8 @@ def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
     rate: An integer, rate for atrous convolution.
     outputs_collections: Collection to add the ResNet unit output.
     scope: Optional variable_scope.
+    use_bounded_activations: Whether or not to use bounded activations. Bounded
+      activations better lend themselves to quantized inference.
 
   Returns:
     The ResNet unit's output.
@@ -95,8 +103,12 @@ def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
     if depth == depth_in:
       shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
     else:
-      shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
-                             activation_fn=None, scope='shortcut')
+      shortcut = slim.conv2d(
+          inputs,
+          depth, [1, 1],
+          stride=stride,
+          activation_fn=tf.nn.relu6 if use_bounded_activations else None,
+          scope='shortcut')
 
     residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                            scope='conv1')
@@ -105,7 +117,12 @@ def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
     residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                            activation_fn=None, scope='conv3')
 
-    output = tf.nn.relu(shortcut + residual)
+    if use_bounded_activations:
+      # Use clip_by_value to simulate bandpass activation.
+      residual = tf.clip_by_value(residual, -6.0, 6.0)
+      output = tf.nn.relu6(shortcut + residual)
+    else:
+      output = tf.nn.relu(shortcut + residual)
 
     return slim.utils.collect_named_outputs(outputs_collections,
                                             sc.original_name_scope,
@@ -119,7 +136,7 @@ def resnet_v1(inputs,
               global_pool=True,
               output_stride=None,
               include_root_block=True,
-              spatial_squeeze=False,
+              spatial_squeeze=True,
               reuse=None,
               scope=None):
   """Generator for v1 ResNet models.

slim/nets/resnet_v1_test.py

@@ -251,6 +251,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                     global_pool=True,
                     output_stride=None,
                     include_root_block=True,
+                    spatial_squeeze=True,
                     reuse=None,
                     scope='resnet_v1_small'):
     """A shallow and thin ResNet v1 for faster tests."""
@@ -266,6 +267,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                                global_pool=global_pool,
                                output_stride=output_stride,
                                include_root_block=include_root_block,
+                               spatial_squeeze=spatial_squeeze,
                                reuse=reuse,
                                scope=scope)
 
@@ -276,6 +278,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       logits, end_points = self._resnet_small(inputs, num_classes,
                                               global_pool=global_pool,
+                                              spatial_squeeze=False,
                                               scope='resnet')
     self.assertTrue(logits.op.name.startswith('resnet/logits'))
     self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
@@ -307,6 +310,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       _, end_points = self._resnet_small(inputs, num_classes,
                                          global_pool=global_pool,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 41, 41, 4],
@@ -325,6 +329,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
       _, end_points = self._resnet_small(inputs, num_classes,
                                          global_pool=global_pool,
                                          include_root_block=False,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 64, 64, 4],
@@ -345,6 +350,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                                          num_classes,
                                          global_pool=global_pool,
                                          output_stride=output_stride,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 41, 41, 4],
@@ -391,6 +397,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       logits, _ = self._resnet_small(inputs, num_classes,
                                      global_pool=global_pool,
+                                     spatial_squeeze=False,
                                      scope='resnet')
     self.assertTrue(logits.op.name.startswith('resnet/logits'))
     self.assertListEqual(logits.get_shape().as_list(),

slim/nets/resnet_v2.py

@@ -115,7 +115,7 @@ def resnet_v2(inputs,
               global_pool=True,
               output_stride=None,
               include_root_block=True,
-              spatial_squeeze=False,
+              spatial_squeeze=True,
               reuse=None,
               scope=None):
   """Generator for v2 (preactivation) ResNet models.
@@ -251,7 +251,7 @@ def resnet_v2_50(inputs,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
-                 spatial_squeeze=False,
+                 spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v2_50'):
   """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
@@ -273,7 +273,7 @@ def resnet_v2_101(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
-                  spatial_squeeze=False,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_101'):
   """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
@@ -295,7 +295,7 @@ def resnet_v2_152(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
-                  spatial_squeeze=False,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_152'):
   """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""
@@ -317,7 +317,7 @@ def resnet_v2_200(inputs,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
-                  spatial_squeeze=False,
+                  spatial_squeeze=True,
                   reuse=None,
                   scope='resnet_v2_200'):
   """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""

slim/nets/resnet_v2_test.py

@@ -251,6 +251,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                     global_pool=True,
                     output_stride=None,
                     include_root_block=True,
+                    spatial_squeeze=True,
                     reuse=None,
                     scope='resnet_v2_small'):
     """A shallow and thin ResNet v2 for faster tests."""
@@ -266,6 +267,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                                global_pool=global_pool,
                                output_stride=output_stride,
                                include_root_block=include_root_block,
+                               spatial_squeeze=spatial_squeeze,
                                reuse=reuse,
                                scope=scope)
 
@@ -276,6 +278,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       logits, end_points = self._resnet_small(inputs, num_classes,
                                               global_pool=global_pool,
+                                              spatial_squeeze=False,
                                               scope='resnet')
     self.assertTrue(logits.op.name.startswith('resnet/logits'))
     self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
@@ -307,6 +310,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       _, end_points = self._resnet_small(inputs, num_classes,
                                          global_pool=global_pool,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 41, 41, 4],
@@ -325,6 +329,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
       _, end_points = self._resnet_small(inputs, num_classes,
                                          global_pool=global_pool,
                                          include_root_block=False,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 64, 64, 4],
@@ -345,6 +350,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
                                          num_classes,
                                          global_pool=global_pool,
                                          output_stride=output_stride,
+                                         spatial_squeeze=False,
                                          scope='resnet')
       endpoint_to_shape = {
           'resnet/block1': [2, 41, 41, 4],
@@ -393,6 +399,7 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     with slim.arg_scope(resnet_utils.resnet_arg_scope()):
       logits, _ = self._resnet_small(inputs, num_classes,
                                      global_pool=global_pool,
+                                     spatial_squeeze=False,
                                      scope='resnet')
     self.assertTrue(logits.op.name.startswith('resnet/logits'))
     self.assertListEqual(logits.get_shape().as_list(),

slim/nets/vgg.py

@@ -87,8 +87,9 @@ def vgg_a(inputs,
     fc_conv_padding: the type of padding to use for the fully connected layer
       that is implemented as a convolutional layer. Use 'SAME' padding if you
       are applying the network in a fully convolutional manner and want to
-      get a prediction map downsampled by a factor of 32 as an output. Otherwise,
-      the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
 
   Returns:
     the last op containing the log predictions and end_points dict.
@@ -152,8 +153,9 @@ def vgg_16(inputs,
     fc_conv_padding: the type of padding to use for the fully connected layer
       that is implemented as a convolutional layer. Use 'SAME' padding if you
       are applying the network in a fully convolutional manner and want to
-      get a prediction map downsampled by a factor of 32 as an output. Otherwise,
-      the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
 
   Returns:
     the last op containing the log predictions and end_points dict.
@@ -217,8 +219,10 @@ def vgg_19(inputs,
     fc_conv_padding: the type of padding to use for the fully connected layer
       that is implemented as a convolutional layer. Use 'SAME' padding if you
       are applying the network in a fully convolutional manner and want to
-      get a prediction map downsampled by a factor of 32 as an output. Otherwise,
-      the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+      get a prediction map downsampled by a factor of 32 as an output.
+      Otherwise, the output prediction map will be (input / 32) - 6 in case of
+      'VALID' padding.
+
 
   Returns:
     the last op containing the log predictions and end_points dict.