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101 lines
4.1 KiB
101 lines
4.1 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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import paddle.compat as cpt
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class TestPrune(unittest.TestCase):
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def net(self):
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x = fluid.layers.data(name='x', shape=[2], dtype='float32')
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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y = fluid.layers.fc(input=[x], size=2, act="softmax")
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loss = fluid.layers.cross_entropy(input=y, label=label)
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loss = fluid.layers.mean(x=loss)
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return x, y, label, loss
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def test_prune_with_input(self):
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program = framework.Program()
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startup_program = framework.Program()
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block = program.global_block()
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with fluid.program_guard(program, startup_program):
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(x, y, label, loss) = self.net()
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self.assertEqual(len(block.ops), 5)
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self.assertEqual([op.type for op in block.ops], [
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"mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
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])
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pruned_program = program._prune_with_input(
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feeded_var_names=[y.name, label.name], targets=[loss])
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self.assertEqual(len(pruned_program.global_block().ops), 2)
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self.assertEqual([op.type for op in pruned_program.global_block().ops],
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["cross_entropy2", "mean"])
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def test_prune(self):
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program = framework.Program()
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startup_program = framework.Program()
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block = program.global_block()
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with fluid.program_guard(program, startup_program):
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(x, y, label, loss) = self.net()
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self.assertEqual(len(block.ops), 5)
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self.assertEqual([op.type for op in block.ops], [
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"mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
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])
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pruned_program = program._prune(targets=[loss])
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self.assertEqual(len(pruned_program.global_block().ops), 5)
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self.assertEqual(
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[op.type for op in pruned_program.global_block().ops],
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["mul", "elementwise_add", "softmax", "cross_entropy2", "mean"])
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def test_prune_target_not_list(self):
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program = framework.Program()
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startup_program = framework.Program()
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block = program.global_block()
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with fluid.program_guard(program, startup_program):
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(x, y, label, loss) = self.net()
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self.assertEqual(len(block.ops), 5)
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self.assertEqual([op.type for op in block.ops], [
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"mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
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])
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pruned_program = program._prune(targets=loss)
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self.assertEqual(len(pruned_program.global_block().ops), 5)
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self.assertEqual(
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[op.type for op in pruned_program.global_block().ops],
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["mul", "elementwise_add", "softmax", "cross_entropy2", "mean"])
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def test_prune_target_none(self):
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program = framework.Program()
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startup_program = framework.Program()
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block = program.global_block()
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with fluid.program_guard(program, startup_program):
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(x, y, label, loss) = self.net()
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self.assertEqual(len(block.ops), 5)
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self.assertEqual([op.type for op in block.ops], [
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"mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
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])
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try:
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pruned_program = program._prune(targets=None)
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except ValueError as e:
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self.assertEqual(
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"All targets of prune() can only be Variable or Operator.",
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cpt.get_exception_message(e))
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if __name__ == '__main__':
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unittest.main()
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