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144 lines
4.7 KiB
144 lines
4.7 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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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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class TestHashOp(OpTest):
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def setUp(self):
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self.op_type = "hash"
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self.init_test_case()
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self.inputs = {'X': (self.in_seq, self.lod)}
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self.attrs = {'num_hash': 2, 'mod_by': 10000}
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self.outputs = {'Out': (self.out_seq, self.lod)}
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def init_test_case(self):
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np.random.seed(1)
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self.in_seq = np.random.randint(0, 10, (8, 1)).astype("int32")
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self.lod = [[2, 6]]
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self.out_seq = [[[3481], [7475]], [[1719], [5986]], [[8473], [694]],
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[[3481], [7475]], [[4372], [9456]], [[4372], [9456]],
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[[6897], [3218]], [[9038], [7951]]]
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self.out_seq = np.array(self.out_seq)
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def test_check_output(self):
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self.check_output()
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class TestHashNotLoDOp(TestHashOp):
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def setUp(self):
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self.op_type = "hash"
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self.init_test_case()
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self.inputs = {'X': self.in_seq}
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self.attrs = {'num_hash': 2, 'mod_by': 10000}
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self.outputs = {'Out': self.out_seq}
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def init_test_case(self):
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np.random.seed(1)
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self.in_seq = np.random.randint(0, 10, (8, 1)).astype("int32")
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self.out_seq = [[[3481], [7475]], [[1719], [5986]], [[8473], [694]],
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[[3481], [7475]], [[4372], [9456]], [[4372], [9456]],
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[[6897], [3218]], [[9038], [7951]]]
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self.out_seq = np.array(self.out_seq)
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def test_check_output(self):
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self.check_output()
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class TestHashOp2(TestHashOp):
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"""
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Case:
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int64 type input
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"""
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def setUp(self):
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self.op_type = "hash"
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self.init_test_case()
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self.inputs = {'X': self.in_seq}
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self.attrs = {'num_hash': 2, 'mod_by': 10000}
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self.outputs = {'Out': self.out_seq}
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def init_test_case(self):
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self.in_seq = np.array([1, 2**32 + 1]).reshape((2, 1)).astype("int64")
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self.out_seq = np.array([1269, 9609, 3868, 7268]).reshape((2, 2, 1))
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def test_check_output(self):
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self.check_output()
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class TestHashOp3(TestHashOp):
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"""
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Case:
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int64 type input
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int64 type mod_by attr
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"""
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def setUp(self):
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self.op_type = "hash"
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self.init_test_case()
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self.inputs = {'X': self.in_seq}
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self.attrs = {'num_hash': 2, 'mod_by': 2**32}
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self.outputs = {'Out': self.out_seq}
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def init_test_case(self):
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self.in_seq = np.array([10, 5]).reshape((2, 1)).astype("int64")
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self.out_seq = np.array(
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[1204014882, 393011615, 3586283837, 2814821595]).reshape((2, 2, 1))
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def test_check_output(self):
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self.check_output()
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class TestHashOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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input_data = np.random.randint(0, 10, (8, 1)).astype("int32")
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def test_Variable():
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# the input type must be Variable
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fluid.layers.hash(input=input_data, hash_size=2**32)
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self.assertRaises(TypeError, test_Variable)
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def test_type():
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# dtype must be int32, int64.
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x2 = fluid.layers.data(
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name='x2', shape=[1], dtype="float32", lod_level=1)
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fluid.layers.hash(input=x2, hash_size=2**32)
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self.assertRaises(TypeError, test_type)
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def test_hash_size_type():
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# hash_size dtype must be int32, int64.
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x3 = fluid.layers.data(
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name='x3', shape=[1], dtype="int32", lod_level=1)
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fluid.layers.hash(input=x3, hash_size=1024.5)
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self.assertRaises(TypeError, test_hash_size_type)
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def test_num_hash_type():
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# num_hash dtype must be int32, int64.
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x4 = fluid.layers.data(
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name='x4', shape=[1], dtype="int32", lod_level=1)
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fluid.layers.hash(input=x4, hash_size=2**32, num_hash=2.5)
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self.assertRaises(TypeError, test_num_hash_type)
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if __name__ == "__main__":
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unittest.main()
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