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keras_retinanet/models/__init__.py

@@ -49,7 +49,9 @@ class Backbone(object):
 def backbone(backbone_name):
     """ Returns a backbone object for the given backbone.
     """
-    if 'resnet' in backbone_name:
+    if 'seresnext' in backbone_name or 'seresnet' in backbone_name or 'senet' in backbone_name:
+        from .senet import SeBackbone as b
+    elif 'resnet' in backbone_name:
         from .resnet import ResNetBackbone as b
     elif 'mobilenet' in backbone_name:
         from .mobilenet import MobileNetBackbone as b

keras_retinanet/models/senet.py

+"""
+Copyright 2017-2018 Fizyr (https://fizyr.com)
+
+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.
+"""
+
+import keras
+from keras.utils import get_file
+import keras_resnet
+import keras_resnet.models
+
+from . import retinanet
+from . import Backbone
+from classification_models.keras import Classifiers
+
+
+class SeBackbone(Backbone):
+    """ Describes backbone information and provides utility functions.
+    """
+
+    def __init__(self, backbone):
+        super(SeBackbone, self).__init__(backbone)
+        self.custom_objects.update(keras_resnet.custom_objects)
+        self.preprocess_image_func = None
+
+    def retinanet(self, *args, **kwargs):
+        """ Returns a retinanet model using the correct backbone.
+        """
+        _, self.preprocess_image_func = Classifiers.get(self.backbone)
+        return senet_retinanet(*args, backbone=self.backbone, **kwargs)
+
+    def download_imagenet(self):
+        """ Downloads ImageNet weights and returns path to weights file.
+        """
+        return
+
+    def validate(self):
+        """ Checks whether the backbone string is correct.
+        """
+        allowed_backbones = ['seresnet18', 'seresnet34', 'seresnet50', 'seresnet101', 'seresnet152',
+                             'seresnext50', 'seresnext101', 'senet154']
+        backbone = self.backbone.split('_')[0]
+
+        if backbone not in allowed_backbones:
+            raise ValueError('Backbone (\'{}\') not in allowed backbones ({}).'.format(backbone, allowed_backbones))
+
+    def preprocess_image(self, inputs):
+        """ Takes as input an image and prepares it for being passed through the network.
+        """
+        return self.preprocess_image_func(inputs)
+
+
+def senet_retinanet(num_classes, backbone='seresnext50', inputs=None, modifier=None, **kwargs):
+    """ Constructs a retinanet model using a resnet backbone.
+
+    Args
+        num_classes: Number of classes to predict.
+        backbone: Which backbone to use (one of ('resnet50', 'resnet101', 'resnet152')).
+        inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
+        modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).
+
+    Returns
+        RetinaNet model with a ResNet backbone.
+    """
+    # choose default input
+    if inputs is None:
+        if keras.backend.image_data_format() == 'channels_first':
+            inputs = keras.layers.Input(shape=(3, None, None))
+        else:
+            # inputs = keras.layers.Input(shape=(224, 224, 3))
+            inputs = keras.layers.Input(shape=(None, None, 3))
+
+    classifier, _ = Classifiers.get(backbone)
+    model = classifier(input_tensor=inputs, include_top=False, weights='imagenet')
+
+    # get last conv layer from the end of each block [28x28, 14x14, 7x7]
+    if backbone == 'seresnet18' or backbone == 'seresnet34':
+        layer_outputs = ['stage3_unit1_relu1', 'stage4_unit1_relu1', 'relu1']
+    elif backbone == 'seresnet50':
+        layer_outputs = ['activation_36', 'activation_66', 'activation_81']
+    elif backbone == 'seresnet101':
+        layer_outputs = ['activation_36', 'activation_151', 'activation_166']
+    elif backbone == 'seresnet152':
+        layer_outputs = ['activation_56', 'activation_236', 'activation_251']
+    elif backbone == 'seresnext50':
+        layer_outputs = ['activation_37', 'activation_67', 'activation_81']
+    elif backbone == 'seresnext101':
+        layer_outputs = ['activation_37', 'activation_152', 'activation_166']
+    elif backbone == 'senet154':
+        layer_outputs = ['activation_59', 'activation_239', 'activation_253']
+    else:
+        raise ValueError('Backbone (\'{}\') is invalid.'.format(backbone))
+
+    layer_outputs = [
+        model.get_layer(name=layer_outputs[0]).output,  # 28x28
+        model.get_layer(name=layer_outputs[1]).output,  # 14x14
+        model.get_layer(name=layer_outputs[2]).output,  # 7x7
+    ]
+    # create the densenet backbone
+    model = keras.models.Model(inputs=inputs, outputs=layer_outputs, name=model.name)
+
+    # invoke modifier if given
+    if modifier:
+        model = modifier(model)
+
+    # create the full model
+    return retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=model.outputs, **kwargs)
+
+
+def seresnext50_retinanet(num_classes, inputs=None, **kwargs):
+    return senet_retinanet(num_classes=num_classes, backbone='seresnext50', inputs=inputs, **kwargs)