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129 lines
3.7 KiB
129 lines
3.7 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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import logging
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import paddle.fluid.core as core
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import unittest
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import numpy as np
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from paddle.fluid.op import Operator, CondOp
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class PySimpleCond(object):
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'''
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A simple implementation of dynamic if-else based on numpy
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'''
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def __init__(self):
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array = [1] * 10
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for i in range(1, 10, 2):
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array[i] = 0
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self.cond = np.array(array)
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self.x = np.ones(shape=(10, 1)).astype("float32")
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def forward(self):
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self.index_t = np.where(self.cond == 1)
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self.index_f = np.where(self.cond == 0)
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y_t = self.x[self.index_t]
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y_f = self.x[self.index_f]
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y_t = y_t * 2.
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y_f = y_f * (-2.)
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output = np.zeros(shape=(10, 1))
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output[self.index_t] = y_t
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output[self.index_f] = y_f
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return output
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class PySimpleCondTest(unittest.TestCase):
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def setUp(self):
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self.condnn = PySimpleCond()
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def test_forward(self):
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output = self.condnn.forward()
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def create_tensor(scope, name, shape, np_data):
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tensor = scope.var(name).get_tensor()
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tensor.set_dims(shape)
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tensor.set(np_data, core.CPUPlace())
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return tensor
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class TestCondOp(unittest.TestCase):
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'''
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Test CondOp
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equation:
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cond = [True, False, True, False, ...]
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y[index_t] = x[index_t] * 2.
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y[index_f] = x[index_f] * -2.
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outputs:
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y
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'''
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def setUp(self):
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self.py_cond = PySimpleCond()
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def forward(self):
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self.scope = core.Scope()
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self.create_global_variables()
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self.create_cond_op()
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self.create_sub_net()
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self.condop.run(self.scope, core.CPUPlace())
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return np.array(self.scope.find_var("Out").get_tensor())
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def create_global_variables(self):
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x_np_data = self.py_cond.x
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create_tensor(self.scope, "X", [10, 1], x_np_data)
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cond_np_data = self.py_cond.cond.astype("int32")
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create_tensor(self.scope, "cond", [10, 1], cond_np_data)
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self.scope.var("SubScopes")
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self.scope.var("IndexTensors")
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self.scope.var("Out")
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def create_cond_op(self):
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self.condop = CondOp(
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Cond="cond",
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Xs=["X"],
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Outs=["Out"],
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SubScopes="SubScopes",
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IndexTensors="IndexTensors")
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def create_sub_net(self):
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truenet = core.Net.create()
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scale_op_t = Operator("scale", X='X', Out='Out', scale=2.)
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truenet.append_op(scale_op_t)
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truenet.complete_add_op(True)
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self.condop.set_truenet(truenet)
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falsenet = core.Net.create()
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scale_op_t = Operator("scale", X='X', Out='Out', scale=-2.)
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falsenet.append_op(scale_op_t)
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falsenet.complete_add_op(True)
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self.condop.set_falsenet(falsenet)
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def test_forward(self):
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print 'test cond op forward'
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pd_output = self.forward()
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py_output = self.py_cond.forward()
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print 'pd_output', pd_output
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print
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print 'py_output', py_output
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self.assertEqual(pd_output.shape, py_output.shape)
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print 'test passed'
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return 0
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if __name__ == "__main__":
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unittest.main()
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