Merge branch 'develop' into 'pruner'

# Conflicts:
#   paddleslim/analysis/__init__.py

paddleslim/analysis/__init__.py

@@ -13,5 +13,8 @@
 # limitations under the License.
 import flops as flops_module
 from flops import *
+import model_size as model_size_module
+from model_size import *
 __all__ = []
 __all__ += flops_module.__all__
+__all__ += model_size_module.__all__

paddleslim/analysis/model_size.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.
+
+import numpy as np
+from ..core import GraphWrapper
+
+__all__ = ["model_size"]
+
+
+def model_size(program):
+    """
+    Get total value numbers of all parameters.
+    Args:
+        program(Program): The program used to calculate model size.
+    """
+    size = 0
+    for block in program.blocks:
+        for param in block.all_parameters():
+            size += np.product(param.shape)
+    return size

paddleslim/nas/searchspace/mobilenetv2_space.py

@@ -16,13 +16,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import sys
-sys.path.append('..')
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
-from searchspacebase import SearchSpaceBase
-from .base_layer import conv_bn_layer
+from ..search_space_base import SearchSpaceBase
+from .layer import conv_bn_layer
 from .registry import SEARCHSPACE
 
 @SEARCHSPACE.register_module

paddleslim/nas/searchspace/registry.py

-import sys
-sys.path.append('..')
-from utils.registry import Registry
+from ..utils.registry import Registry
 
 SEARCHSPACE = Registry('searchspace')

paddleslim/prune/pruner.py

@@ -14,7 +14,7 @@
 
 import numpy as np
 import paddle.fluid as fluid
-from ..core import VarWrapper, OpWrapper, GraphWrapper
+from core import VarWrapper, OpWrapper, GraphWrapper
 
 __all__ = ["prune"]
 

paddleslim/tests/test_nas_search_space.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.
+
+import sys
+sys.path.append('..')
+import unittest
+import paddle.fluid as fluid
+from nas.search_space_factory import SearchSpaceFactory
+
+class TestSearchSpace(unittest.TestCase):
+    def test_searchspace(self):
+        # if output_size is 1, the model will add fc layer in the end.
+        config = {'input_size': 224, 'output_size': 7, 'block_num': 5}
+        space = SearchSpaceFactory()
+        
+        my_space = space.get_search_space('MobileNetV2Space', config)
+        model_arch = my_space.token2arch()
+        
+        train_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        with fluid.program_guard(train_prog, startup_prog):
+            input_size= config['input_size']
+            model_input = fluid.layers.data(name='model_in', shape=[1, 3, input_size, input_size], dtype='float32', append_batch_size=False)
+            predict = model_arch(model_input)
+            self.assertTrue(predict.shape[2] == config['output_size'])
+            
+
+        #for op in train_prog.global_block().ops:
+        #    print(op.type)
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_prune.py → paddleslim/tests/test_prune.py

@@ -15,7 +15,7 @@ import sys
 sys.path.append("../")
 import unittest
 import paddle.fluid as fluid
-from paddleslim.prune import Pruner
+from prune import Pruner
 from layers import conv_bn_layer
 
 

paddleslim/nas/test_searchspace.py → tests/test_model_size.py

@@ -11,26 +11,30 @@
 # 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 sys
+sys.path.append("../")
+import unittest
 import paddle.fluid as fluid
-from searchspacefactory import SearchSpaceFactory
-if __name__ == '__main__':
-    # if output_size is 1, the model will add fc layer in the end.
-    config = {'input_size': 224, 'output_size': 7, 'block_num': 5}
-    space = SearchSpaceFactory()
-    
-    my_space = space.get_search_space('MobileNetV2Space', config)
-    model_arch = my_space.token2arch()
-    
-    train_prog = fluid.Program()
-    startup_prog = fluid.Program()
-    with fluid.program_guard(train_prog, startup_prog):
-        input_size= config['input_size']
-        model_input = fluid.layers.data(name='model_in', 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)
-        
+from paddleslim.analysis import model_size
+from layers import conv_bn_layer
+
 
-    #for op in train_prog.global_block().ops:
-    #    print(op.type)
+class TestPrune(unittest.TestCase):
+    def test_prune(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+            input = fluid.data(name="image", shape=[None, 3, 16, 16])
+            conv1 = conv_bn_layer(input, 8, 3, "conv1")
+            conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
+            sum1 = conv1 + conv2
+            conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
+            conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
+            sum2 = conv4 + sum1
+            conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
+            conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
+        self.assertTrue(3288 == model_size(main_program))
+
+
+if __name__ == '__main__':
+    unittest.main()