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242 lines
7.6 KiB
242 lines
7.6 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 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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from paddle.fluid import compiler, Program, program_guard
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# Situation 1: expand_times is a list(without tensor)
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class TestExpandOpRank1(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.init_data()
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self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
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self.attrs = {'expand_times': self.expand_times}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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def init_data(self):
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self.ori_shape = [100]
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self.expand_times = [2]
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestExpandOpRank2_Corner(TestExpandOpRank1):
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def init_data(self):
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self.ori_shape = [120]
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self.expand_times = [2]
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class TestExpandOpRank2(TestExpandOpRank1):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.expand_times = [2, 3]
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class TestExpandOpRank3_Corner(TestExpandOpRank1):
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def init_data(self):
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self.ori_shape = (2, 10, 5)
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self.expand_times = (1, 1, 1)
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class TestExpandOpRank3(TestExpandOpRank1):
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def init_data(self):
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self.ori_shape = (2, 4, 15)
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self.expand_times = (2, 1, 4)
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class TestExpandOpRank4(TestExpandOpRank1):
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def init_data(self):
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self.ori_shape = (2, 4, 5, 7)
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self.expand_times = (3, 2, 1, 2)
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# Situation 2: expand_times is a list(with tensor)
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class TestExpandOpRank1_tensor_attr(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.init_data()
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expand_times_tensor = []
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for index, ele in enumerate(self.expand_times):
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expand_times_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {
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'X': np.random.random(self.ori_shape).astype("float64"),
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'expand_times_tensor': expand_times_tensor,
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}
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self.attrs = {"expand_times": self.infer_expand_times}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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def init_data(self):
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self.ori_shape = [100]
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self.expand_times = [2]
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self.infer_expand_times = [-1]
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestExpandOpRank2_Corner_tensor_attr(TestExpandOpRank1_tensor_attr):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.expand_times = [1, 1]
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self.infer_expand_times = [1, -1]
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class TestExpandOpRank2_attr_tensor(TestExpandOpRank1_tensor_attr):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.expand_times = [2, 3]
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self.infer_expand_times = [-1, 3]
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# Situation 3: expand_times is a tensor
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class TestExpandOpRank1_tensor(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.init_data()
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self.inputs = {
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'X': np.random.random(self.ori_shape).astype("float64"),
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'ExpandTimes': np.array(self.expand_times).astype("int32"),
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}
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self.attrs = {}
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output = np.tile(self.inputs['X'], self.expand_times)
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self.outputs = {'Out': output}
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def init_data(self):
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self.ori_shape = [100]
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self.expand_times = [2]
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestExpandOpRank2_tensor(TestExpandOpRank1_tensor):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.expand_times = [2, 3]
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# Situation 4: input x is Integer
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class TestExpandOpInteger(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.inputs = {
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'X': np.random.randint(
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10, size=(2, 4, 5)).astype("int32")
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}
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self.attrs = {'expand_times': [2, 1, 4]}
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output = np.tile(self.inputs['X'], (2, 1, 4))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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# Situation 5: input x is Bool
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class TestExpandOpBoolean(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
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self.attrs = {'expand_times': [2, 1, 4]}
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output = np.tile(self.inputs['X'], (2, 1, 4))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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# Situation 56: input x is Integer
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class TestExpandOpInt64_t(OpTest):
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def setUp(self):
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self.op_type = "expand"
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self.inputs = {
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'X': np.random.randint(
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10, size=(2, 4, 5)).astype("int64")
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}
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self.attrs = {'expand_times': [2, 1, 4]}
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output = np.tile(self.inputs['X'], (2, 1, 4))
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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class TestExpandError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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expand_times = [2, 2]
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self.assertRaises(TypeError, fluid.layers.expand, x1, expand_times)
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
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self.assertRaises(TypeError, fluid.layers.expand, x2, expand_times)
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
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x3.stop_gradient = True
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self.assertRaises(ValueError, fluid.layers.expand, x3, expand_times)
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# Test python API
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class TestExpandAPI(unittest.TestCase):
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def test_api(self):
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input = np.random.random([12, 14]).astype("float32")
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x = fluid.layers.data(
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name='x', shape=[12, 14], append_batch_size=False, dtype="float32")
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positive_2 = fluid.layers.fill_constant([1], "int32", 2)
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expand_times = fluid.layers.data(
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name="expand_times", shape=[2], append_batch_size=False)
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out_1 = fluid.layers.expand(x, expand_times=[2, 3])
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out_2 = fluid.layers.expand(x, expand_times=[positive_2, 3])
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out_3 = fluid.layers.expand(x, expand_times=expand_times)
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g0 = fluid.backward.calc_gradient(out_2, x)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
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feed={
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"x": input,
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"expand_times":
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np.array([1, 3]).astype("int32")
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},
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fetch_list=[out_1, out_2, out_3])
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assert np.array_equal(res_1, np.tile(input, (2, 3)))
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assert np.array_equal(res_2, np.tile(input, (2, 3)))
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assert np.array_equal(res_3, np.tile(input, (1, 3)))
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if __name__ == "__main__":
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unittest.main()
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