add slim pruning algorithm and ralated unitest test=develop (#119)

* add slim pruning implementation and related unitest test=develop

demo/prune/train.py

@@ -8,6 +8,7 @@ import math
 import time
 import numpy as np
 import paddle.fluid as fluid
+sys.path.append("../../")
 from paddleslim.prune import Pruner, save_model
 from paddleslim.common import get_logger
 from paddleslim.analysis import flops
@@ -37,6 +38,7 @@ add_arg('log_period',       int, 10,                 "Log period in batches.")
 add_arg('test_period',      int, 10,                 "Test period in epoches.")
 add_arg('model_path',       str, "./models",         "The path to save model.")
 add_arg('pruned_ratio',     float, None,         "The ratios to be pruned.")
+add_arg('criterion',        str, "l1_norm",         "The prune criterion to be used, support l1_norm and batch_norm_scale.")
 # yapf: enable
 
 model_list = models.__all__
@@ -207,7 +209,7 @@ def compress(args):
     params = get_pruned_params(args, fluid.default_main_program())
     _logger.info("FLOPs before pruning: {}".format(
         flops(fluid.default_main_program())))
-    pruner = Pruner()
+    pruner = Pruner(args.criterion)
     pruned_val_program, _, _ = pruner.prune(
         val_program,
         fluid.global_scope(),

paddleslim/prune/pruner.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import logging
+import sys
 import numpy as np
 import paddle.fluid as fluid
 import copy
@@ -79,8 +80,8 @@ class Pruner():
                 pruned_num = int(round(param_v.shape()[0] * ratio))
                 pruned_idx = [0] * pruned_num
             else:
-                param_t = np.array(scope.find_var(param).get_tensor())
-                pruned_idx = self._cal_pruned_idx(param_t, ratio, axis=0)
+                pruned_idx = self._cal_pruned_idx(
+                    graph, scope, param, ratio, axis=0)
             param = graph.var(param)
             conv_op = param.outputs()[0]
             walker = conv2d_walker(
@@ -130,7 +131,7 @@ class Pruner():
         graph.infer_shape()
         return graph.program, param_backup, param_shape_backup
 
-    def _cal_pruned_idx(self, param, ratio, axis):
+    def _cal_pruned_idx(self, graph, scope, param, ratio, axis):
         """
         Calculate the index to be pruned on axis by given pruning ratio.
 
@@ -145,11 +146,26 @@ class Pruner():
         Returns:
             list<int>: The indexes to be pruned on axis.
         """
-        prune_num = int(round(param.shape[axis] * ratio))
-        reduce_dims = [i for i in range(len(param.shape)) if i != axis]
         if self.criterion == 'l1_norm':
-            criterions = np.sum(np.abs(param), axis=tuple(reduce_dims))
-        pruned_idx = criterions.argsort()[:prune_num]
+            param_t = np.array(scope.find_var(param).get_tensor())
+            prune_num = int(round(param_t.shape[axis] * ratio))
+            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 == "batch_norm_scale":
+            param_var = graph.var(param)
+            conv_op = param_var.outputs()[0]
+            conv_output = conv_op.outputs("Output")[0]
+            bn_op = conv_output.outputs()[0]
+            if bn_op is not None:
+                bn_scale_param = bn_op.inputs("Scale")[0].name()
+                bn_scale_np = np.array(
+                    scope.find_var(bn_scale_param).get_tensor())
+                prune_num = int(round(bn_scale_np.shape[axis] * ratio))
+                pruned_idx = np.abs(bn_scale_np).argsort()[:prune_num]
+            else:
+                raise SystemExit(
+                    "Can't find BatchNorm op after Conv op in Network.")
         return pruned_idx
 
     def _prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):

tests/test_slim_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 = 'batch_norm_scale'
+        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()