1. Rename common to core package.

2. Add copyright.
3. Rmove FLOPS from graph wrapper.

paddleslim/common/graph_wrapper.py → paddleslim/core/graph_wrapper.py

@@ -329,52 +329,6 @@ class GraphWrapper(object):
             ret += np.product(param.shape())
         return ret
 
-    def flops(self, only_conv=False):
-        """
-        Get the flops of current graph.
-        Args:
-            only_conv: Only calculating the conv layers. default: False.
-        Returns:
-            int: The flops of current graph.
-        """
-        flops = 0
-        for op in self.ops():
-            if op.type() in ['conv2d', 'depthwise_conv2d']:
-                filter_shape = op.inputs("Filter")[0].shape()
-                input_shape = op.inputs("Input")[0].shape()
-                output_shape = op.outputs("Output")[0].shape()
-                c_out, c_in, k_h, k_w = filter_shape
-                _, _, h_out, w_out = output_shape
-                groups = op.attr("groups")
-                kernel_ops = k_h * k_w * (c_in / groups)
-                if len(op.inputs("Bias")) > 0:
-                    with_bias = 1
-                else:
-                    with_bias = 0
-                flops += 2 * h_out * w_out * c_out * (kernel_ops + with_bias)
-            elif op.type() == 'pool2d' and not only_conv:
-                input_shape = op.inputs("X")[0].shape()
-                output_shape = op.outputs("Out")[0].shape()
-                _, c_out, h_out, w_out = output_shape
-                k_size = op.attr("ksize")
-                flops += h_out * w_out * c_out * (k_size[0]**2)
-
-            elif op.type() == 'mul' and not only_conv:
-                x_shape = list(op.inputs("X")[0].shape())
-                y_shape = op.inputs("Y")[0].shape()
-                if x_shape[0] == -1:
-                    x_shape[0] = 1
-                flops += 2 * x_shape[0] * x_shape[1] * y_shape[1]
-
-            elif op.type() in ['relu', 'sigmoid', 'batch_norm'
-                               ] and not only_conv:
-                input_shape = list(op.inputs("X")[0].shape())
-                if input_shape[0] == -1:
-                    input_shape[0] = 1
-                flops += np.product(input_shape)
-
-        return flops
-
     def update_param_shape(self, scope):
         """
         Update the shape of parameters in the graph according to tensors in scope.

paddleslim/prune/pruner.py

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

tests/layers.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 paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 

tests/test_prune.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