added inception model

doc/python/api/models/imagenet.rst

@@ -65,6 +65,7 @@ Available models are summarized in the following table. Error rates are calculat
     "VGG-16", "VGG", 29.03, 10.07, Neural Network Console
     "NIN", "NIN", 42.91, 20.66, Neural Network Console
     "DenseNet-161", "DenseNet", 23.82, 7.02, Neural Network Console
+    "InceptionV3", "InceptionV3", 21.82, 5.88, Neural Network Console
 
 
 Common interfaces
@@ -103,3 +104,6 @@ List of models
 
 .. autoclass:: DenseNet
     :members:
+
+.. autoclass:: InceptionV3
+    :members:

python/src/nnabla/models/imagenet/__init__.py

@@ -19,3 +19,4 @@
 from .vgg import VGG
 from .nin import NIN
 from .densenet import DenseNet
+from .inception import InceptionV3

python/src/nnabla/models/imagenet/inception.py

@@ -0,0 +1,93 @@
+# Copyright (c) 2017 Sony Corporation. 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.
+
+from __future__ import absolute_import
+import nnabla as nn
+from nnabla.utils.nnp_graph import NnpNetworkPass
+
+from nnabla import logger
+
+from .base import ImageNetBase
+
+
+class InceptionV3(ImageNetBase):
+    '''
+    InceptionV3 architecture.
+    The following is a list of string that can be specified to ``use_up_to`` option in ``__call__`` method;
+    * ``'classifier'`` (default): The output of the final affine layer for classification.
+    * ``'pool'``: The output of the final global average pooling.
+    * ``'prepool'``: The input of the final global average pooling, i.e. the output of the final inception block.
+    References:
+        * `Szegedy et al., Rethinking the Inception Architecture for Computer Vision.
+          <https://arxiv.org/abs/1512.00567>`_
+    '''
+
+    _KEY_VARIABLE = {
+        'classifier': 'Affine',
+        'pool': 'AveragePooling_2',
+        'prepool': 'Inception_11/Concatenate',
+        '_aux_classifier': 'Affine_2',
+        '_include_no_aux': 'Conv_6/Convolution'
+        }
+
+    def __init__(self):
+        self._load_nnp('Inception-v3.nnp', 'Inception-v3/Inception-v3.nnp')
+
+    def _input_shape(self):
+        return (3, 299, 299)
+
+    def __call__(self, input_var=None, use_from=None, use_up_to='classifier', training=False, force_global_pooling=False, check_global_pooling=True, returns_net=False, verbose=0, with_aux_tower=False):
+        if not training:
+            assert not with_aux_tower, "Aux Tower should be disabled when inference process."
+
+        input_var = self.get_input_var(input_var)
+
+        callback = NnpNetworkPass(verbose)
+        callback.remove_and_rewire('ImageAugmentation')
+        callback.set_variable('Iv3TrainInput', input_var)
+        self.configure_global_average_pooling(
+            callback, force_global_pooling, check_global_pooling, 'AveragePooling_2')
+        callback.set_batch_normalization_batch_stat_all(training)
+        if with_aux_tower:
+            self.use_up_to('_aux_classifier', callback)
+            funcs_to_drop = ("Affine_2",
+                             "SoftmaxCrossEntropy_2",
+                             "MulScalar_2")
+        else:
+            self.use_up_to('_include_no_aux', callback)
+            funcs_to_drop = ("Conv_6/Convolution",
+                             "Conv_6/BatchNormalization",
+                             "Conv_6/ReLU",
+                             "AveragePooling",
+                             "Conv_7/Convolution",
+                             "Conv_7/BatchNormalization",
+                             "Conv_7/ReLU",
+                             "Affine_2",
+                             "SoftmaxCrossEntropy_2",
+                             "MulScalar_2")
+
+        callback.drop_function(*funcs_to_drop)
+        if not training:
+            callback.remove_and_rewire('Dropout')
+            callback.fix_parameters()
+        self.use_up_to(use_up_to, callback)
+        batch_size = input_var.shape[0]
+        net = self.nnp.get_network(
+            'Train', batch_size=batch_size, callback=callback)
+        if returns_net:
+            return net
+        elif with_aux_tower:
+            return list(net.outputs.values())
+        else:
+            return list(net.outputs.values())[0]

python/test/models/test_imagenet.py

@@ -88,6 +88,7 @@ def _execute():
     ('SENet', ['classifier', 'pool', 'lastconv', 'lastconv+relu']),
     ('SqueezeNet', ['classifier', 'pool', 'lastconv', 'lastconv+relu']),
     ('DenseNet', ['classifier', 'pool', 'lastconv', 'lastconv+relu']),
+    ('InceptionV3', ['classifier', 'pool', 'prepool']),
     ])
 @pytest.mark.parametrize('image_size_factor', [1, 2])
 @pytest.mark.parametrize('batch_size', [1, 5])