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276 lines
11 KiB
276 lines
11 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 numpy as np
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.fluid.layers as layers
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from paddle.fluid.framework import Program, program_guard
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from functools import partial
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import paddle.fluid.optimizer as optimizer
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class TestAPICase(unittest.TestCase):
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def test_return_single_var(self):
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def fn_1():
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return layers.fill_constant(shape=[4, 2], dtype='int32', value=1)
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def fn_2():
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return layers.fill_constant(shape=[4, 2], dtype='int32', value=2)
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def fn_3():
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return layers.fill_constant(shape=[4, 3], dtype='int32', value=3)
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main_program = Program()
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startup_program = Program()
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with program_guard(main_program, startup_program):
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x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
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y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
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z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
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pred_2 = layers.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = layers.less_than(z, x) # true: 0.2 < 0.3
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# call fn_1
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out_0 = layers.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3)
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# call fn_2
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out_1 = layers.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3)
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# call default fn_3
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out_2 = layers.case(
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pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3)
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# no default, call fn_2
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out_3 = layers.case(pred_fn_pairs=[(pred_1, fn_2)])
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# no default, call fn_2. but pred_2 is false
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out_4 = layers.case(pred_fn_pairs=[(pred_2, fn_2)])
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place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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res = exe.run(main_program,
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fetch_list=[out_0, out_1, out_2, out_3, out_4])
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self.assertTrue(np.allclose(res[0], 1))
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self.assertTrue(np.allclose(res[1], 2))
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self.assertTrue(np.allclose(res[2], 3))
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self.assertTrue(np.allclose(res[3], 2))
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self.assertTrue(np.allclose(res[4], 2))
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def test_return_var_tuple(self):
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def fn_1():
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return layers.fill_constant(
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shape=[1, 2], dtype='int32', value=1), layers.fill_constant(
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shape=[2, 3], dtype='float32', value=2)
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def fn_2():
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return layers.fill_constant(
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shape=[3, 4], dtype='int32', value=3), layers.fill_constant(
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shape=[4, 5], dtype='float32', value=4)
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def fn_3():
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return layers.fill_constant(
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shape=[5], dtype='int32', value=5), layers.fill_constant(
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shape=[5, 6], dtype='float32', value=6)
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main_program = Program()
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startup_program = Program()
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with program_guard(main_program, startup_program):
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x = layers.fill_constant(shape=[1], dtype='float32', value=1)
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y = layers.fill_constant(shape=[1], dtype='float32', value=1)
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z = layers.fill_constant(shape=[1], dtype='float32', value=3)
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pred_1 = layers.equal(x, y) # true
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pred_2 = layers.equal(x, z) # false
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out = layers.case(((pred_1, fn_1), (pred_2, fn_2)), fn_3)
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place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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ret = exe.run(main_program, fetch_list=out)
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self.assertTrue(
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np.allclose(np.asarray(ret[0]), np.full((1, 2), 1, np.int32)))
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self.assertTrue(
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np.allclose(
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np.asarray(ret[1]), np.full((2, 3), 2, np.float32)))
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class TestAPICase_Nested(unittest.TestCase):
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def test_nested_case(self):
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def fn_1(x=1):
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var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
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var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
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out = layers.case(pred_fn_pairs=[(var_5 < var_6, partial(
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layers.fill_constant, shape=[1], dtype='int32', value=x)),
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(var_5 == var_6, partial(
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layers.fill_constant,
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shape=[2],
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dtype='int32',
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value=x))])
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return out
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def fn_2(x=2):
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var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
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var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
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out = layers.case(pred_fn_pairs=[(var_5 < var_6, partial(
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fn_1, x=x)), (var_5 == var_6, partial(
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layers.fill_constant, shape=[2], dtype='int32', value=x))])
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return out
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def fn_3():
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var_5 = layers.fill_constant(shape=[1], dtype='int32', value=5)
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var_6 = layers.fill_constant(shape=[1], dtype='int32', value=6)
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out = layers.case(pred_fn_pairs=[(var_5 < var_6, partial(
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fn_2, x=3)), (var_5 == var_6, partial(
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layers.fill_constant, shape=[2], dtype='int32', value=7))])
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return out
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main_program = Program()
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startup_program = Program()
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with program_guard(main_program, startup_program):
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x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
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y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
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z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
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pred_2 = layers.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = layers.less_than(z, x) # true: 0.2 < 0.3
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out_1 = layers.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3)
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out_2 = layers.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3)
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out_3 = layers.case(
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pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3)
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place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])
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self.assertTrue(np.allclose(res[0], 1))
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self.assertTrue(np.allclose(res[1], 2))
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self.assertTrue(np.allclose(res[2], 3))
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class TestAPICase_Error(unittest.TestCase):
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def test_error(self):
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def fn_1():
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return layers.fill_constant(shape=[4, 2], dtype='int32', value=1)
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main_program = Program()
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startup_program = Program()
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with program_guard(main_program, startup_program):
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x = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
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z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)
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pred_1 = layers.less_than(z, x) # true
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# The type of 'pred_fn_pairs' in case must be list or tuple
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def type_error_pred_fn_pairs():
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layers.case(pred_fn_pairs=1, default=fn_1)
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self.assertRaises(TypeError, type_error_pred_fn_pairs)
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# The elements' type of 'pred_fn_pairs' in Op(case) must be tuple
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def type_error_pred_fn_1():
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layers.case(pred_fn_pairs=[1], default=fn_1)
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self.assertRaises(TypeError, type_error_pred_fn_1)
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# The tuple's size of 'pred_fn_pairs' in Op(case) must be 2
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def type_error_pred_fn_2():
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layers.case(pred_fn_pairs=[(1, 2, 3)], default=fn_1)
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self.assertRaises(TypeError, type_error_pred_fn_2)
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# The pred's type of 'pred_fn_pairs' in Op(case) must be bool Variable
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def type_error_pred():
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layers.case(pred_fn_pairs=[(1, fn_1)], default=fn_1)
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self.assertRaises(TypeError, type_error_pred)
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# The function of pred_fn_pairs in case must be callable
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def type_error_fn():
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layers.case(pred_fn_pairs=[(pred_1, 2)], default=fn_1)
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self.assertRaises(TypeError, type_error_fn)
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# The default in Op(case) must be callable
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def type_error_default():
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layers.case(pred_fn_pairs=[(pred_1, fn_1)], default=fn_1())
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self.assertRaises(TypeError, type_error_default)
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# when optimizer in case
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class TestMutiTask(unittest.TestCase):
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def test_optimizer_in_case(self):
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BATCH_SIZE = 1
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INPUT_SIZE = 784
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EPOCH_NUM = 2
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x = fluid.data(
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name='x', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32')
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y = fluid.data(
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name='y', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32')
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switch_id = fluid.data(name='switch_id', shape=[1], dtype='int32')
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one = layers.fill_constant(shape=[1], dtype='int32', value=1)
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adam = optimizer.Adam(learning_rate=0.001)
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adagrad = optimizer.Adagrad(learning_rate=0.001)
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def fn_1():
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sum = layers.elementwise_mul(x, y)
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loss = layers.mean(sum, name="f_1_loss")
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adam.minimize(loss)
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def fn_2():
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sum = layers.elementwise_mul(x, y)
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loss = layers.mean(sum, name="f_2_loss")
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adagrad.minimize(loss)
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layers.case(pred_fn_pairs=[(switch_id == one, fn_1)], default=fn_2)
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exe = fluid.Executor(fluid.CPUPlace())
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exe.run(fluid.default_startup_program())
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for epoch in range(EPOCH_NUM):
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np.random.seed(epoch)
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feed_image = np.random.random(
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size=[BATCH_SIZE, INPUT_SIZE]).astype('float32')
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main_program = fluid.default_main_program()
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out = exe.run(main_program,
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feed={
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'x': feed_image,
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'y': feed_image,
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'switch_id': np.array([epoch]).astype('int32')
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},
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fetch_list=[])
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if __name__ == '__main__':
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unittest.main()
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