update multi search space

paddleslim/nas/search_space/__init__.py

@@ -14,6 +14,8 @@
 
 import mobilenetv2
 from .mobilenetv2 import *
+import resnet
+from .resnet import *
 import search_space_registry
 from search_space_registry import *
 import search_space_factory
@@ -26,3 +28,4 @@ __all__ += mobilenetv2.__all__
 __all__ += search_space_registry.__all__
 __all__ += search_space_factory.__all__
 __all__ += search_space_base.__all__
+

paddleslim/nas/search_space/base_layer.py

@@ -59,5 +59,7 @@ def conv_bn_layer(input,
         moving_variance_name=bn_name + '_variance')
     if act == 'relu6':
         return fluid.layers.relu6(bn)
+    elif act == 'sigmoid':
+        return fluid.layers.sigmoid(bn)
     else:
         return bn

paddleslim/nas/search_space/combine_search_space.py

@@ -86,9 +86,5 @@ class CombineSearchSpace(object):
         for space, token in zip(self.spaces, self.token):
             model_archs.append(space.token2arch(token))
 
-        def net_arch(input):
-            for model_arch in model_archs:
-                input = model_arch(input)
-            return input
+        return model_archs
 
-        return net_arch

paddleslim/nas/search_space/resnet.py

+# Copyright (c) 2019  PaddlePaddle 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from .search_space_base import SearchSpaceBase
+from .base_layer import conv_bn_layer
+from .search_space_registry import SEARCHSPACE
+
+__all__ = ["ResNetSpace"]
+
+@SEARCHSPACE.register
+class ResNetSpace(SearchSpaceBase):
+    def __init__(self, input_size, output_size, block_num, scale=1.0, class_dim=1000):
+        super(ResNetSpace, self).__init__(input_size, output_size, block_num)
+        pass
+
+    def init_tokens(self):
+        return [0,0,0,0,0,0]
+
+    def range_table(self):
+        return [3,3,3,3,3,3]
+
+    def token2arch(self,tokens=None):
+        if tokens is None:
+            self.init_tokens()
+
+        def net_arch(input):
+            input = conv_bn_layer(
+                input,
+                num_filters=32,
+                filter_size=3,
+                stride=2,
+                padding='SAME',
+                act='sigmoid',
+                name='resnet_conv1_1')
+
+            return input
+
+        return net_arch
+
+

tests/test_searchspace.py

@@ -24,7 +24,7 @@ class TestSearchSpaceFactory(unittest.TestCase):
         config = {'input_size': 224, 'output_size': 7, 'block_num': 5}
         space = SearchSpaceFactory()
 
-        my_space = space.get_search_space('MobileNetV2Space', config)
+        my_space = space.get_search_space([('MobileNetV2Space', config)])
         model_arch = my_space.token2arch()
 
         train_prog = fluid.Program()
@@ -36,10 +36,26 @@ class TestSearchSpaceFactory(unittest.TestCase):
                 shape=[1, 3, input_size, input_size],
                 dtype='float32',
                 append_batch_size=False)
-            print('input shape', model_input.shape)
-            predict = model_arch(model_input)
-            print('output shape', predict.shape)
+            predict = model_arch[0](model_input)
+            self.assertTrue(predict.shape[2] == config['output_size'])
 
+class TestMultiSearchSpace(unittest.TestCase):
+    space = SearchSpaceFactory()
+    
+    config0 = {'input_size': 224, 'output_size': 7, 'block_num': 5}
+    config1 = {'input_size': 7, 'output_size': 1, 'block_num': 2}
+    my_space = space.get_search_space([('MobileNetV2Space', config0), ('ResNetSpace', config1)])
+    model_archs = my_space.token2arch()
+    
+    train_prog = fluid.Program()
+    startup_prog = fluid.Program()
+    with fluid.program_guard(train_prog, startup_prog):
+        input_size= config0['input_size']
+        model_input = fluid.layers.data(name='model_in', shape=[1, 3, input_size, input_size], dtype='float32', append_batch_size=False)
+        for model_arch in model_archs:
+            predict = model_arch(model_input)
+            model_input = predict
+        print(predict)
 
 if __name__ == '__main__':
     unittest.main()