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118 lines
3.5 KiB
118 lines
3.5 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 six
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import paddle.fluid.core as core
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class TestInferShape(unittest.TestCase):
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def test_sum_op(self):
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prog = core.ProgramDesc()
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self.assertIsNotNone(prog)
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block = prog.block(0)
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self.assertIsNotNone(block)
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shape = [10, 20]
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# prepare input/output
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x1 = block.var(six.b("x1"))
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x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
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x1.set_shape(shape)
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x2 = block.var(six.b("x2"))
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x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
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x2.set_shape(shape)
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out = block.var(six.b("out"))
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out.set_type(core.VarDesc.VarType.LOD_TENSOR)
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# prepare the operator
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sum_op_desc = block.append_op()
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sum_op_desc.set_type("sum")
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sum_op_desc.set_input("X", ["x1", "x2"])
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sum_op_desc.set_output("Out", ["out"])
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sum_op_desc.check_attrs()
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sum_op_desc.infer_shape(block)
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self.assertEqual(out.shape(), shape)
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def test_mul_op(self):
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prog = core.ProgramDesc()
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self.assertIsNotNone(prog)
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block = prog.block(0)
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self.assertIsNotNone(block)
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x_shape = [10, 20]
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y_shape = [20, 30]
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# prepare input/output
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x1 = block.var(six.b("x"))
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x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
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x1.set_shape(x_shape)
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x2 = block.var(six.b("y"))
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x2.set_type(core.VarDesc.VarType.LOD_TENSOR)
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x2.set_shape(y_shape)
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out = block.var(six.b("out"))
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out.set_type(core.VarDesc.VarType.LOD_TENSOR)
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# prepare the operator
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mul_op_desc = block.append_op()
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mul_op_desc.set_type("mul")
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mul_op_desc.set_input("X", ["x"])
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mul_op_desc.set_input("Y", ["y"])
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mul_op_desc.set_output("Out", ["out"])
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mul_op_desc._set_attr("x_num_col_dims", 1)
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mul_op_desc._set_attr("y_num_col_dims", 1)
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mul_op_desc.check_attrs()
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mul_op_desc.infer_shape(block)
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self.assertEqual(out.shape(), [x_shape[0], y_shape[1]])
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def test_expand_op(self):
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prog = core.ProgramDesc()
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self.assertIsNotNone(prog)
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block = prog.block(0)
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self.assertIsNotNone(block)
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shape = [-1, 20]
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expand_times = [3, 1]
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# prepare input/output
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x1 = block.var(six.b("x"))
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x1.set_type(core.VarDesc.VarType.LOD_TENSOR)
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x1.set_shape(shape)
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out = block.var(six.b("out"))
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out.set_type(core.VarDesc.VarType.LOD_TENSOR)
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# prepare the operator
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sum_op_desc = block.append_op()
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sum_op_desc.set_type("expand")
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sum_op_desc.set_input("X", ["x"])
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sum_op_desc.set_input('expand_times_tensor', [])
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sum_op_desc.set_output("Out", ["out"])
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sum_op_desc._set_attr('expand_times', expand_times)
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sum_op_desc.check_attrs()
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sum_op_desc.infer_shape(block)
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self.assertEqual(out.shape(), shape)
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if __name__ == '__main__':
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unittest.main()
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