# 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 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) pruner = Pruner() 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: self.assertTrue(param.shape == shapes[param.name]) if __name__ == '__main__': unittest.main()