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250 lines
7.6 KiB
250 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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import paddle.fluid as fluid
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from op_test import OpTest
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# Correct: General.
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class TestUnsqueezeOp(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = "unsqueeze2"
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self.inputs = {"X": np.random.random(self.ori_shape).astype("float64")}
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self.init_attrs()
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.new_shape),
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"XShape": np.random.random(self.ori_shape).astype("float64")
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}
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def test_check_output(self):
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self.check_output(no_check_set=["XShape"])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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def init_test_case(self):
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self.ori_shape = (3, 40)
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self.axes = (1, 2)
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self.new_shape = (3, 1, 1, 40)
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def init_attrs(self):
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self.attrs = {"axes": self.axes}
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# Correct: Single input index.
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class TestUnsqueezeOp1(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (-1, )
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self.new_shape = (20, 5, 1)
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# Correct: Mixed input axis.
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class TestUnsqueezeOp2(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (0, -1)
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self.new_shape = (1, 20, 5, 1)
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# Correct: There is duplicated axis.
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class TestUnsqueezeOp3(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (0, 3, 3)
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self.new_shape = (1, 10, 2, 1, 1, 5)
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# Correct: Reversed axes.
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class TestUnsqueezeOp4(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (3, 1, 1)
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self.new_shape = (10, 1, 1, 2, 5, 1)
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# axes is a list(with tensor)
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class TestUnsqueezeOp_AxesTensorList(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = "unsqueeze2"
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axes_tensor_list = []
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for index, ele in enumerate(self.axes):
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axes_tensor_list.append(("axes" + 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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"AxesTensorList": axes_tensor_list
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}
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self.init_attrs()
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.new_shape),
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"XShape": np.random.random(self.ori_shape).astype("float64")
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}
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def test_check_output(self):
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self.check_output(no_check_set=["XShape"])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (1, 2)
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self.new_shape = (20, 1, 1, 5)
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def init_attrs(self):
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self.attrs = {}
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class TestUnsqueezeOp1_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (-1, )
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self.new_shape = (20, 5, 1)
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class TestUnsqueezeOp2_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (0, -1)
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self.new_shape = (1, 20, 5, 1)
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class TestUnsqueezeOp3_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (0, 3, 3)
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self.new_shape = (1, 10, 2, 1, 1, 5)
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class TestUnsqueezeOp4_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (3, 1, 1)
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self.new_shape = (10, 1, 1, 2, 5, 1)
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# axes is a Tensor
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class TestUnsqueezeOp_AxesTensor(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = "unsqueeze2"
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self.inputs = {
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"X": np.random.random(self.ori_shape).astype("float64"),
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"AxesTensor": np.array(self.axes).astype("int32")
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}
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self.init_attrs()
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.new_shape),
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"XShape": np.random.random(self.ori_shape).astype("float64")
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}
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def test_check_output(self):
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self.check_output(no_check_set=["XShape"])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (1, 2)
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self.new_shape = (20, 1, 1, 5)
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def init_attrs(self):
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self.attrs = {}
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class TestUnsqueezeOp1_AxesTensor(TestUnsqueezeOp_AxesTensor):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (-1, )
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self.new_shape = (20, 5, 1)
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class TestUnsqueezeOp2_AxesTensor(TestUnsqueezeOp_AxesTensor):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (0, -1)
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self.new_shape = (1, 20, 5, 1)
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class TestUnsqueezeOp3_AxesTensor(TestUnsqueezeOp_AxesTensor):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (0, 3, 3)
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self.new_shape = (1, 10, 2, 1, 1, 5)
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class TestUnsqueezeOp4_AxesTensor(TestUnsqueezeOp_AxesTensor):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (3, 1, 1)
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self.new_shape = (10, 1, 1, 2, 5, 1)
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# test api
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class TestUnsqueezeAPI(unittest.TestCase):
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def test_api(self):
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input = np.random.random([3, 2, 5]).astype("float64")
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x = fluid.data(name='x', shape=[3, 2, 5], dtype="float64")
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positive_3_int32 = fluid.layers.fill_constant([1], "int32", 3)
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positive_1_int64 = fluid.layers.fill_constant([1], "int64", 1)
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axes_tensor_int32 = fluid.data(
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name='axes_tensor_int32', shape=[3], dtype="int32")
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axes_tensor_int64 = fluid.data(
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name='axes_tensor_int64', shape=[3], dtype="int64")
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out_1 = fluid.layers.unsqueeze(x, axes=[3, 1, 1])
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out_2 = fluid.layers.unsqueeze(
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x, axes=[positive_3_int32, positive_1_int64, 1])
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out_3 = fluid.layers.unsqueeze(x, axes=axes_tensor_int32)
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out_4 = fluid.layers.unsqueeze(x, axes=3)
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out_5 = fluid.layers.unsqueeze(x, axes=axes_tensor_int64)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3, res_4, res_5 = exe.run(
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fluid.default_main_program(),
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feed={
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"x": input,
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"axes_tensor_int32": np.array([3, 1, 1]).astype("int32"),
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"axes_tensor_int64": np.array([3, 1, 1]).astype("int64")
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},
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fetch_list=[out_1, out_2, out_3, out_4, out_5])
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assert np.array_equal(res_1, input.reshape([3, 1, 1, 2, 5, 1]))
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assert np.array_equal(res_2, input.reshape([3, 1, 1, 2, 5, 1]))
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assert np.array_equal(res_3, input.reshape([3, 1, 1, 2, 5, 1]))
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assert np.array_equal(res_4, input.reshape([3, 2, 5, 1]))
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assert np.array_equal(res_5, input.reshape([3, 1, 1, 2, 5, 1]))
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def test_error(self):
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def test_axes_type():
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x2 = fluid.data(name="x2", shape=[2, 25], dtype="int32")
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fluid.layers.unsqueeze(x2, axes=2.1)
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self.assertRaises(TypeError, test_axes_type)
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if __name__ == "__main__":
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unittest.main()
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