Implementation of Inception V4 (#18)

slim/BUILD

@@ -164,25 +164,50 @@ py_library(
         ":inception_v1",
         ":inception_v2",
         ":inception_v3",
+        ":inception_v4",
     ],
 )
 
+py_library(
+    name = "inception_utils",
+    srcs = ["nets/inception_utils.py"],
+    srcs_version = "PY2AND3",
+)
+
 py_library(
     name = "inception_v1",
     srcs = ["nets/inception_v1.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+    ],
 )
 
 py_library(
     name = "inception_v2",
     srcs = ["nets/inception_v2.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+    ],
 )
 
 py_library(
     name = "inception_v3",
     srcs = ["nets/inception_v3.py"],
     srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+    ],
+)
+
+py_library(
+    name = "inception_v4",
+    srcs = ["nets/inception_v4.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_utils",
+    ],
 )
 
 py_library(
@@ -218,6 +243,15 @@ py_test(
     deps = [":inception"],
 )
 
+py_test(
+    name = "inception_v4_test",
+    size = "large",
+    srcs = ["nets/inception_v4_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [":inception"],
+)
+
 py_test(
     name = "inception_resnet_v2_test",
     size = "large",

slim/README.md

@@ -197,9 +197,10 @@ crops at multiple scales.
 
 Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
 :----:|:------------:|:----------:|:-------:|:--------:|
-[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/inception_v1.py)|[inception_v1.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
-[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/inception_v2.py)|[inception_v2.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
-[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/inception_v3.py)|[inception_v3.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
+[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
+[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
+[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
+[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
 [Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
 [ResNet 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py)|[resnet_v1_50.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
 [ResNet 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py)|[resnet_v1_101.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|

slim/nets/inception.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Brings inception_v1, inception_v2 and inception_v3 under one namespace."""
+"""Brings all inception models under one namespace."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -30,4 +30,7 @@
 from nets.inception_v3 import inception_v3
 from nets.inception_v3 import inception_v3_arg_scope
 from nets.inception_v3 import inception_v3_base
+from nets.inception_v4 import inception_v4
+from nets.inception_v4 import inception_v4_arg_scope
+from nets.inception_v4 import inception_v4_base
 # pylint: enable=unused-import

slim/nets/inception_utils.py

@@ -0,0 +1,71 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains common code shared by all inception models.
+
+Usage of arg scope:
+  with slim.arg_scope(inception_arg_scope()):
+    logits, end_points = inception.inception_v3(images, num_classes,
+                                                is_training=is_training)
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def inception_arg_scope(weight_decay=0.00004,
+                        use_batch_norm=True,
+                        batch_norm_decay=0.9997,
+                        batch_norm_epsilon=0.001):
+  """Defines the default arg scope for inception models.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+
+  Returns:
+    An `arg_scope` to use for the inception models.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      # collection containing update_ops.
+      'updates_collections': tf.GraphKeys.UPDATE_OPS,
+  }
+  if use_batch_norm:
+    normalizer_fn = slim.batch_norm
+    normalizer_params = batch_norm_params
+  else:
+    normalizer_fn = None
+    normalizer_params = {}
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope(
+        [slim.conv2d],
+        weights_initializer=slim.variance_scaling_initializer(),
+        activation_fn=tf.nn.relu,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params) as sc:
+      return sc

slim/nets/inception_v1.py

@@ -20,6 +20,8 @@
 
 import tensorflow as tf
 
+from nets import inception_utils
+
 slim = tf.contrib.slim
 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
 
@@ -300,41 +302,4 @@ def inception_v1(inputs,
   return logits, end_points
 inception_v1.default_image_size = 224
 
-
-def inception_v1_arg_scope(weight_decay=0.00004,
-                           use_batch_norm=True):
-  """Defines the default InceptionV1 arg scope.
-
-  Note: Althougth the original paper didn't use batch_norm we found it useful.
-
-  Args:
-    weight_decay: The weight decay to use for regularizing the model.
-    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
-
-  Returns:
-    An `arg_scope` to use for the inception v3 model.
-  """
-  batch_norm_params = {
-      # Decay for the moving averages.
-      'decay': 0.9997,
-      # epsilon to prevent 0s in variance.
-      'epsilon': 0.001,
-      # collection containing update_ops.
-      'updates_collections': tf.GraphKeys.UPDATE_OPS,
-  }
-  if use_batch_norm:
-    normalizer_fn = slim.batch_norm
-    normalizer_params = batch_norm_params
-  else:
-    normalizer_fn = None
-    normalizer_params = {}
-  # Set weight_decay for weights in Conv and FC layers.
-  with slim.arg_scope([slim.conv2d, slim.fully_connected],
-                      weights_regularizer=slim.l2_regularizer(weight_decay)):
-    with slim.arg_scope(
-        [slim.conv2d],
-        weights_initializer=slim.variance_scaling_initializer(),
-        activation_fn=tf.nn.relu,
-        normalizer_fn=normalizer_fn,
-        normalizer_params=normalizer_params) as sc:
-      return sc
+inception_v1_arg_scope = inception_utils.inception_arg_scope

slim/nets/inception_v2.py

@@ -20,6 +20,8 @@
 
 import tensorflow as tf
 
+from nets import inception_utils
+
 slim = tf.contrib.slim
 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
 
@@ -515,31 +517,4 @@ def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
   return kernel_size_out
 
 
-def inception_v2_arg_scope(weight_decay=0.00004):
-  """Defines the default InceptionV2 arg scope.
-
-  Args:
-    weight_decay: The weight decay to use for regularizing the model.
-
-  Returns:
-    An `arg_scope` to use for the inception v3 model.
-  """
-  batch_norm_params = {
-      # Decay for the moving averages.
-      'decay': 0.9997,
-      # epsilon to prevent 0s in variance.
-      'epsilon': 0.001,
-      # collection containing update_ops.
-      'updates_collections': tf.GraphKeys.UPDATE_OPS,
-  }
-
-  # Set weight_decay for weights in Conv and FC layers.
-  with slim.arg_scope([slim.conv2d, slim.fully_connected],
-                      weights_regularizer=slim.l2_regularizer(weight_decay)):
-    with slim.arg_scope(
-        [slim.conv2d],
-        weights_initializer=slim.variance_scaling_initializer(),
-        activation_fn=tf.nn.relu,
-        normalizer_fn=slim.batch_norm,
-        normalizer_params=batch_norm_params) as sc:
-      return sc
+inception_v2_arg_scope = inception_utils.inception_arg_scope

slim/nets/inception_v3.py

@@ -20,6 +20,8 @@
 
 import tensorflow as tf
 
+from nets import inception_utils
+
 slim = tf.contrib.slim
 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
 
@@ -555,33 +557,4 @@ def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
   return kernel_size_out
 
 
-def inception_v3_arg_scope(weight_decay=0.00004,
-                           stddev=0.1):
-  """Defines the default InceptionV3 arg scope.
-
-  Args:
-    weight_decay: The weight decay to use for regularizing the model.
-    stddev: The standard deviation of the trunctated normal weight initializer.
-
-  Returns:
-    An `arg_scope` to use for the inception v3 model.
-  """
-  batch_norm_params = {
-      # Decay for the moving averages.
-      'decay': 0.9997,
-      # epsilon to prevent 0s in variance.
-      'epsilon': 0.001,
-      # collection containing update_ops.
-      'updates_collections': tf.GraphKeys.UPDATE_OPS,
-  }
-
-  # Set weight_decay for weights in Conv and FC layers.
-  with slim.arg_scope([slim.conv2d, slim.fully_connected],
-                      weights_regularizer=slim.l2_regularizer(weight_decay)):
-    with slim.arg_scope(
-        [slim.conv2d],
-        weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
-        activation_fn=tf.nn.relu,
-        normalizer_fn=slim.batch_norm,
-        normalizer_params=batch_norm_params) as sc:
-      return sc
+inception_v3_arg_scope = inception_utils.inception_arg_scope