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252 lines
7.6 KiB
252 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
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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: repeat_times is a list (without tensor)
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class TestTileOpRank1(OpTest):
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def setUp(self):
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self.op_type = "tile"
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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 = {'repeat_times': self.repeat_times}
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output = np.tile(self.inputs['X'], self.repeat_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.repeat_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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# with dimension expanding
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class TestTileOpRank2Expanding(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = [120]
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self.repeat_times = [2, 2]
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class TestTileOpRank2(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.repeat_times = [2, 3]
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class TestTileOpRank3_Corner(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = (2, 10, 5)
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self.repeat_times = (1, 1, 1)
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class TestTileOpRank3_Corner2(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = (2, 10, 5)
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self.repeat_times = (2, 2)
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class TestTileOpRank3(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = (2, 4, 15)
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self.repeat_times = (2, 1, 4)
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class TestTileOpRank4(TestTileOpRank1):
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def init_data(self):
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self.ori_shape = (2, 4, 5, 7)
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self.repeat_times = (3, 2, 1, 2)
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# Situation 2: repeat_times is a list (with tensor)
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class TestTileOpRank1_tensor_attr(OpTest):
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def setUp(self):
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self.op_type = "tile"
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self.init_data()
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repeat_times_tensor = []
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for index, ele in enumerate(self.repeat_times):
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repeat_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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'repeat_times_tensor': repeat_times_tensor,
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}
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self.attrs = {"repeat_times": self.infer_repeat_times}
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output = np.tile(self.inputs['X'], self.repeat_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.repeat_times = [2]
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self.infer_repeat_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 TestTileOpRank2_Corner_tensor_attr(TestTileOpRank1_tensor_attr):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.repeat_times = [1, 1]
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self.infer_repeat_times = [1, -1]
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class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.repeat_times = [2, 3]
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self.infer_repeat_times = [-1, 3]
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# Situation 3: repeat_times is a tensor
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class TestTileOpRank1_tensor(OpTest):
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def setUp(self):
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self.op_type = "tile"
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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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'RepeatTimes': np.array(self.repeat_times).astype("int32"),
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}
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self.attrs = {}
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output = np.tile(self.inputs['X'], self.repeat_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.repeat_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 TestTileOpRank2_tensor(TestTileOpRank1_tensor):
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def init_data(self):
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self.ori_shape = [12, 14]
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self.repeat_times = [2, 3]
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# Situation 4: input x is Integer
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class TestTileOpInteger(OpTest):
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def setUp(self):
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self.op_type = "tile"
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self.inputs = {
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'X': np.random.randint(
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10, size=(4, 4, 5)).astype("int32")
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}
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self.attrs = {'repeat_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 TestTileOpBoolean(OpTest):
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def setUp(self):
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self.op_type = "tile"
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self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
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self.attrs = {'repeat_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 TestTileOpInt64_t(OpTest):
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def setUp(self):
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self.op_type = "tile"
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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 = {'repeat_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 TestTileError(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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repeat_times = [2, 2]
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self.assertRaises(TypeError, paddle.tile, x1, repeat_times)
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
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self.assertRaises(TypeError, paddle.tile, x2, repeat_times)
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
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x3.stop_gradient = False
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self.assertRaises(ValueError, paddle.tile, x3, repeat_times)
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class TestTileAPIStatic(unittest.TestCase):
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def test_api(self):
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with program_guard(Program(), Program()):
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repeat_times = [2, 2]
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x1 = fluid.layers.data(name='x1', shape=[4], dtype="int32")
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out = paddle.tile(x1, repeat_times)
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positive_2 = fluid.layers.fill_constant([1], dtype="int32", value=2)
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out2 = paddle.tile(x1, repeat_times=[positive_2, 2])
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# Test python API
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class TestTileAPI(unittest.TestCase):
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def test_api(self):
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with fluid.dygraph.guard():
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np_x = np.random.random([12, 14]).astype("float32")
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x = paddle.to_variable(np_x)
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positive_2 = np.array([2]).astype("int32")
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positive_2 = paddle.to_variable(positive_2)
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repeat_times = np.array([2, 3]).astype("int32")
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repeat_times = paddle.to_variable(repeat_times)
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out_1 = paddle.tile(x, repeat_times=[2, 3])
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out_2 = paddle.tile(x, repeat_times=[positive_2, 3])
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out_3 = paddle.tile(x, repeat_times=repeat_times)
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assert np.array_equal(out_1.numpy(), np.tile(np_x, (2, 3)))
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assert np.array_equal(out_2.numpy(), np.tile(np_x, (2, 3)))
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assert np.array_equal(out_3.numpy(), np.tile(np_x, (2, 3)))
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if __name__ == "__main__":
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unittest.main()
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