Merge branch 'pruner' into 'develop'

Add flops function in analysis module

See merge request !10

.gitignore

 *.egg-info
 build/
 ./dist/
+*.pyc
+dist/

paddleslim/analysis/flops.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__ = ["flops"]
+
+
+def flops(program):
+    """
+    Get FLOPS of target graph.
+    Args:
+        program(Program): The program used to calculate FLOPS.
+    """
+    graph = GraphWrapper(program)
+    return _graph_flops(graph)
+
+
+def _graph_flops(graph, only_conv=False):
+    assert isinstance(graph, GraphWrapper)
+    flops = 0
+    for op in graph.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

tests/test_flops.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 paddleslim.analysis import flops
+from layers import conv_bn_layer
+
+
+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(1597440 == flops(main_program))
+
+
+if __name__ == '__main__':
+    unittest.main()