206 lines
7.9 KiB
206 lines
7.9 KiB
# Copyright (c) 2018 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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import paddle.fluid.framework as framework
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from paddle.fluid.executor import Executor
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from paddle.fluid.framework import Program, program_guard
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class TestApiWhileLoop(unittest.TestCase):
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def test_var_tuple(self):
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def cond(i):
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return layers.less_than(i, ten)
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def body(i):
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return layers.elementwise_add(x=i, y=one)
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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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i = layers.fill_constant(shape=[1], dtype='int64', value=0)
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one = layers.fill_constant(shape=[1], dtype='int64', value=1)
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ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
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out = layers.while_loop(cond, body, (i, ))
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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res = exe.run(main_program, fetch_list=out)
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self.assertTrue(
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np.allclose(np.asarray(res[0]), np.full((1), 10, np.int64)))
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def test_var_list(self):
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def cond(i, mem):
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return layers.less_than(i, ten)
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def body(i, mem):
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mem = layers.elementwise_add(x=mem, y=one)
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i = layers.increment(i)
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return [i, mem]
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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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i = layers.zeros(shape=[1], dtype='int64')
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ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
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mem = layers.data(name="mem", shape=[10], dtype='float32')
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one = layers.fill_constant(shape=[10], dtype='float32', value=1)
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out = layers.while_loop(cond, body, [i, mem])
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data = np.random.rand(10).astype('float32')
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data_one = np.ones(10).astype('float32')
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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res = exe.run(main_program, feed={'mem': data}, fetch_list=out)
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for i in range(10):
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data = np.add(data, data_one)
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self.assertTrue(np.allclose(np.asarray(res[1]), data))
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class TestApiWhileLoop_Nested(unittest.TestCase):
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def test_nested_net(self):
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def external_cond(i, j, init, sums):
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return layers.less_than(i, loop_len1)
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def external_body(i, j, init, sums):
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def internal_cond(j, init, sums):
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return layers.less_than(j, loop_len2)
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def internal_body(j, init, sums):
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init = layers.elementwise_add(x=init, y=ones)
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sums = layers.elementwise_add(x=init, y=sums)
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j = layers.increment(j)
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return [j, init, sums]
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result = layers.while_loop(internal_cond, internal_body,
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[j, init, sums])
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j = result[0]
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init = result[1]
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sums = result[2]
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sums = layers.elementwise_add(x=init, y=sums)
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i = layers.increment(i)
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return [i, j, init, sums]
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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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i = layers.zeros(shape=[1], dtype='int64')
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j = layers.zeros(shape=[1], dtype='int64')
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init = layers.data(name="init", shape=[3, 3], dtype='float32')
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sums = layers.data(name="sums", shape=[3, 3], dtype='float32')
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loop_len1 = layers.fill_constant(shape=[1], dtype='int64', value=2)
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loop_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
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ones = layers.fill_constant(shape=[3, 3], dtype='float32', value=1)
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res = layers.while_loop(external_cond, external_body,
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[i, j, init, sums])
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data = np.random.rand(3, 3).astype('float32')
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data_sums = np.zeros([3, 3]).astype('float32')
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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ret = exe.run(main_program,
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feed={'init': data,
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'sums': data_sums},
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fetch_list=res)
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for i in range(3):
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data = np.add(data, 1)
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data_sums = np.add(data, data_sums)
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for j in range(2):
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data_sums = np.add(data, data_sums)
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self.assertTrue(np.allclose(np.asarray(ret[3]), data_sums))
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class TestApiWhileLoop_Error(unittest.TestCase):
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def test_error(self):
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def cond_returns_constant(i):
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return 1
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def cond_returns_not_bool_tensor(i):
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return layers.increment(i)
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def cond_returns_bool_tensor(i):
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return layers.less_than(i, ten)
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def cond_returns_2d_tensor(i):
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return layers.less_than(i, ten_2d)
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def body(i):
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return layers.increment(i)
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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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data = layers.fill_constant(shape=[1], dtype='int64', value=1)
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data_1d = layers.fill_constant(shape=[1], dtype='int64', value=1)
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data_2d = layers.fill_constant(shape=[2, 2], dtype='int64', value=1)
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ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
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ten_2d = layers.fill_constant(shape=[2, 2], dtype='int64', value=10)
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# The type of `cond` in Op(while_loop) must be callable
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def type_error_cond():
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out = layers.while_loop(data, body, [data_1d])
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self.assertRaises(TypeError, type_error_cond)
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# The type of `body` in Op(while_loop) must be callable
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def type_error_body():
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out = layers.while_loop(cond_returns_bool_tensor, data,
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[data_1d])
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self.assertRaises(TypeError, type_error_body)
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# The type of `loop_vars` in Op(while_loop) must be list or tuple
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def type_error_loop_vars():
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out = layers.while_loop(cond_returns_bool_tensor, body, data_1d)
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self.assertRaises(TypeError, type_error_loop_vars)
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# The value of `loop_vars` is empty
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def value_error_loop_vars():
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out = layers.while_loop(cond_returns_bool_tensor, body, [])
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self.assertRaises(ValueError, value_error_loop_vars)
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# The type of `cond` returns in Op(while_loop) must be Variable
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def type_error_cond_returns_not_variable():
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out = layers.while_loop(cond_returns_constant, body, [data_1d])
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self.assertRaises(TypeError, type_error_cond_returns_not_variable)
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# The type of `cond` returns in Op(while_loop) must be a bollean variable
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def type_error_cond_returns_not_boolean():
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out = layers.while_loop(cond_returns_not_bool_tensor, body,
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[data_1d])
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self.assertRaises(TypeError, type_error_cond_returns_not_boolean)
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# The shape of `cond` returns in Op(while_loop) must be 1
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def type_error_shape_cond_returns_2d():
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out = layers.while_loop(cond_returns_2d_tensor, body, [data_2d])
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self.assertRaises(TypeError, type_error_shape_cond_returns_2d)
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if __name__ == '__main__':
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unittest.main()
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