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90 lines
2.8 KiB
90 lines
2.8 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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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 paddle.fluid import compiler, Program, program_guard
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from op_test import OpTest
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# Correct: General.
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class TestSqueezeOp(OpTest):
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def setUp(self):
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self.op_type = "squeeze"
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self.init_test_case()
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self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
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self.init_attrs()
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self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape), }
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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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def init_test_case(self):
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self.ori_shape = (1, 3, 1, 40)
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self.axes = (0, 2)
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self.new_shape = (3, 40)
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def init_attrs(self):
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self.attrs = {"axes": self.axes}
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# Correct: There is mins axis.
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class TestSqueezeOp1(TestSqueezeOp):
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def init_test_case(self):
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self.ori_shape = (1, 3, 1, 40)
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self.axes = (0, -2)
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self.new_shape = (3, 40)
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# Correct: No axes input.
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class TestSqueezeOp2(TestSqueezeOp):
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def init_test_case(self):
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self.ori_shape = (1, 3, 1, 5)
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self.axes = ()
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self.new_shape = (3, 5)
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# Correct: Just part of axes be squeezed.
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class TestSqueezeOp3(TestSqueezeOp):
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def init_test_case(self):
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self.ori_shape = (3, 1, 5, 1, 4, 1)
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self.axes = (1, -1)
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self.new_shape = (3, 5, 1, 4)
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class TestSqueezeOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of softmax_op must be Variable.
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, fluid.layers.squeeze, x1)
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# The input axes of squeeze must be list.
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.squeeze, x2, axes=0)
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# The input dtype of squeeze not support float16.
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
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self.assertRaises(TypeError, fluid.layers.squeeze, x3, axes=0)
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if __name__ == "__main__":
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unittest.main()
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