add FPGM pruning algorithm's implementation test=develop (#172)

demo/imagenet_reader.py

@@ -156,8 +156,7 @@ def _reader_creator(file_list,
                 for line in lines:
                     if mode == 'train' or mode == 'val':
                         img_path, label = line.split()
-                        img_path = os.path.join(
-                            os.path.join(data_dir, mode), img_path)
+                        img_path = os.path.join(data_dir, img_path)
                         yield img_path, int(label)
                     elif mode == 'test':
                         img_path = os.path.join(data_dir, line)

paddleslim/prune/pruner.py

@@ -15,6 +15,7 @@
 import logging
 import sys
 import numpy as np
+from functools import reduce
 import paddle.fluid as fluid
 import copy
 from ..core import VarWrapper, OpWrapper, GraphWrapper
@@ -152,6 +153,27 @@ class Pruner():
             reduce_dims = [i for i in range(len(param_t.shape)) if i != axis]
             criterions = np.sum(np.abs(param_t), axis=tuple(reduce_dims))
             pruned_idx = criterions.argsort()[:prune_num]
+        elif self.criterion == 'geometry_median':
+            param_t = np.array(scope.find_var(param).get_tensor())
+            prune_num = int(round(param_t.shape[axis] * ratio))
+
+            def get_distance_sum(param, out_idx):
+                w = param.view()
+                reduce_dims = reduce(lambda x, y: x * y, param.shape[1:])
+                w.shape = param.shape[0], reduce_dims
+                selected_filter = np.tile(w[out_idx], (w.shape[0], 1))
+                x = w - selected_filter
+                x = np.sqrt(np.sum(x * x, -1))
+                return x.sum()
+
+            dist_sum_list = []
+            for out_i in range(param_t.shape[0]):
+                dist_sum = get_distance_sum(param_t, out_i)
+                dist_sum_list.append((dist_sum, out_i))
+            min_gm_filters = sorted(
+                dist_sum_list, key=lambda x: x[0])[:prune_num]
+            pruned_idx = [x[1] for x in min_gm_filters]
+
         elif self.criterion == "batch_norm_scale":
             param_var = graph.var(param)
             conv_op = param_var.outputs()[0]

tests/test_fpgm_prune.py

+# Copyright (c) 2020  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.prune import Pruner
+from layers import conv_bn_layer
+
+
+class TestPrune(unittest.TestCase):
+    def test_prune(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        #   X       X              O       X              O
+        # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
+        #     |            ^ |                    ^
+        #     |____________| |____________________|
+        #
+        # X: prune output channels
+        # O: prune input channels
+        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")
+
+        shapes = {}
+        for param in main_program.global_block().all_parameters():
+            shapes[param.name] = param.shape
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        scope = fluid.Scope()
+        exe.run(startup_program, scope=scope)
+        criterion = 'geometry_median'
+        pruner = Pruner(criterion)
+        main_program, _, _ = pruner.prune(
+            main_program,
+            scope,
+            params=["conv4_weights"],
+            ratios=[0.5],
+            place=place,
+            lazy=False,
+            only_graph=False,
+            param_backup=None,
+            param_shape_backup=None)
+
+        shapes = {
+            "conv1_weights": (4L, 3L, 3L, 3L),
+            "conv2_weights": (4L, 4L, 3L, 3L),
+            "conv3_weights": (8L, 4L, 3L, 3L),
+            "conv4_weights": (4L, 8L, 3L, 3L),
+            "conv5_weights": (8L, 4L, 3L, 3L),
+            "conv6_weights": (8L, 8L, 3L, 3L)
+        }
+
+        for param in main_program.global_block().all_parameters():
+            if "weights" in param.name:
+                print("param: {}; param shape: {}".format(param.name,
+                                                          param.shape))
+                self.assertTrue(param.shape == shapes[param.name])
+
+
+if __name__ == '__main__':
+    unittest.main()