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697 lines
24 KiB
697 lines
24 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, skip_check_grad_ci
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import paddle
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import paddle.fluid.core as core
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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 paddle.fluid.framework import convert_np_dtype_to_dtype_
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class TestSumOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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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 TestSumOp5D(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {
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'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
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}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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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 TestSumOp6D(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {
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'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float64")
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}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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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 TestMeanOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
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self.attrs = {'dim': [1]}
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self.outputs = {
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'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
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}
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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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@skip_check_grad_ci(
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reason="reduce_max is discontinuous non-derivable function,"
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" its gradient check is not supported by unittest framework.")
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class TestMaxOp(OpTest):
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"""Remove Max with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_max"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [-1]}
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self.outputs = {
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'Out': self.inputs['X'].max(axis=tuple(self.attrs['dim']))
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}
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def test_check_output(self):
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self.check_output()
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@skip_check_grad_ci(
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reason="reduce_min is discontinuous non-derivable function,"
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" its gradient check is not supported by unittest framework.")
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class TestMinOp(OpTest):
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"""Remove Min with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_min"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [2]}
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self.outputs = {
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'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
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}
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def test_check_output(self):
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self.check_output()
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class TestProdOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_prod"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].prod(axis=0)}
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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 TestAllOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_all"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.outputs = {'Out': self.inputs['X'].all()}
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self.attrs = {'reduce_all': True}
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def test_check_output(self):
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self.check_output()
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class TestAllOpWithDim(OpTest):
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def setUp(self):
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self.op_type = "reduce_all"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.attrs = {'dim': [1]}
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self.outputs = {'Out': self.inputs['X'].all(axis=1)}
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def test_check_output(self):
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self.check_output()
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class TestAllOpWithKeepDim(OpTest):
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def setUp(self):
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self.op_type = "reduce_all"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.attrs = {'dim': [1], 'keep_dim': True}
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self.outputs = {
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'Out': np.expand_dims(
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self.inputs['X'].all(axis=1), axis=1)
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}
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def test_check_output(self):
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self.check_output()
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class TestAllOpError(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 reduce_all_op must be Variable.
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input1 = 12
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self.assertRaises(TypeError, fluid.layers.reduce_all, input1)
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# The input dtype of reduce_all_op must be bool.
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input2 = fluid.layers.data(
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name='input2', shape=[12, 10], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.reduce_all, input2)
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class TestAnyOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_any"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.outputs = {'Out': self.inputs['X'].any()}
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self.attrs = {'reduce_all': True}
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def test_check_output(self):
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self.check_output()
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class TestAnyOpWithDim(OpTest):
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def setUp(self):
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self.op_type = "reduce_any"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.attrs = {'dim': [1]}
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self.outputs = {'Out': self.inputs['X'].any(axis=1)}
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def test_check_output(self):
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self.check_output()
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class TestAnyOpWithKeepDim(OpTest):
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def setUp(self):
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self.op_type = "reduce_any"
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self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
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self.attrs = {'dim': [1], 'keep_dim': True}
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self.outputs = {
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'Out': np.expand_dims(
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self.inputs['X'].any(axis=1), axis=1)
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}
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def test_check_output(self):
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self.check_output()
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class TestAnyOpError(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 reduce_any_op must be Variable.
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input1 = 12
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self.assertRaises(TypeError, fluid.layers.reduce_any, input1)
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# The input dtype of reduce_any_op must be bool.
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input2 = fluid.layers.data(
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name='input2', shape=[12, 10], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.reduce_any, input2)
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class Test1DReduce(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random(120).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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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 Test2DReduce0(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [0]}
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self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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class Test2DReduce1(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [1]}
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self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce0(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [1]}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce1(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [2]}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce2(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [-2]}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
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}
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class Test3DReduce3(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.attrs = {'dim': [1, 2]}
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self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
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}
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class TestKeepDimReduce(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [1], 'keep_dim': True}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
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keepdims=self.attrs['keep_dim'])
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}
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class TestReduceAll(Test1DReduce):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
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self.attrs = {'reduce_all': True}
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self.outputs = {'Out': self.inputs['X'].sum()}
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## reduction in multi dims
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class TestReduceMeanOpMultiAxises(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
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self.attrs = {'dim': [1, 2]}
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self.outputs = {'Out': self.inputs['X'].mean(axis=(1, 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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@skip_check_grad_ci(
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reason="reduce_max is discontinuous non-derivable function,"
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" its gradient check is not supported by unittest framework.")
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class TestReduceMaxOpMultiAxises(OpTest):
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"""Remove Max with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_max"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [-2, -1]}
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self.outputs = {
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'Out': self.inputs['X'].max(axis=tuple(self.attrs['dim']))
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}
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def test_check_output(self):
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self.check_output()
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@skip_check_grad_ci(
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reason="reduce_min is discontinuous non-derivable function,"
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" its gradient check is not supported by unittest framework.")
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class TestReduceMinOpMultiAxises(OpTest):
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"""Remove Min with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_min"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [1, 2]}
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self.outputs = {
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'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
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}
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def test_check_output(self):
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self.check_output()
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class TestKeepDimReduceSumMultiAxises(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.attrs = {'dim': [-2, -1], 'keep_dim': True}
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self.outputs = {
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'Out':
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self.inputs['X'].sum(axis=tuple(self.attrs['dim']), keepdims=True)
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}
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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 TestReduceSumWithDimOne(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
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self.attrs = {'dim': [1, 2], 'keep_dim': True}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
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keepdims=True)
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}
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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 TestReduceSumWithNumelOne(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((100, 1)).astype("float64")}
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self.attrs = {'dim': [1], 'keep_dim': False}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
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keepdims=False)
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}
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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 TestReduceMeanWithDimOne(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
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self.attrs = {'dim': [1], 'keep_dim': False}
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self.outputs = {
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'Out': self.inputs['X'].mean(
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axis=tuple(self.attrs['dim']), keepdims=False)
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}
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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 TestReduceMeanWithNumelOne(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((100, 1)).astype("float64")}
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self.attrs = {'dim': [1], 'keep_dim': True}
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self.outputs = {
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'Out': self.inputs['X'].mean(
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axis=tuple(self.attrs['dim']), keepdims=True)
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}
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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 TestReduceAll(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
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self.attrs = {'reduce_all': True, 'keep_dim': False}
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self.outputs = {'Out': self.inputs['X'].sum()}
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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 Test1DReduceWithAxes1(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random(100).astype("float64")}
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self.attrs = {'dim': [0], 'keep_dim': False}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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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 TestReduceWithDtype(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].sum().astype('float64')}
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self.attrs = {'reduce_all': True}
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self.attrs.update({
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'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
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'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
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})
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def test_check_output(self):
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self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
|
|
class TestReduceWithDtype1(TestReduceWithDtype):
|
|
def setUp(self):
|
|
self.op_type = "reduce_sum"
|
|
self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
|
|
self.outputs = {'Out': self.inputs['X'].sum(axis=1)}
|
|
self.attrs = {'dim': [1]}
|
|
self.attrs.update({
|
|
'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
|
|
'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
|
|
})
|
|
|
|
|
|
class TestReduceWithDtype2(TestReduceWithDtype):
|
|
def setUp(self):
|
|
self.op_type = "reduce_sum"
|
|
self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
|
|
self.outputs = {'Out': self.inputs['X'].sum(axis=1, keepdims=True)}
|
|
self.attrs = {'dim': [1], 'keep_dim': True}
|
|
self.attrs.update({
|
|
'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
|
|
'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
|
|
})
|
|
|
|
|
|
class TestReduceSumOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# The input type of reduce_sum_op must be Variable.
|
|
x1 = fluid.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], fluid.CPUPlace())
|
|
self.assertRaises(TypeError, fluid.layers.reduce_sum, x1)
|
|
# The input dtype of reduce_sum_op must be float32 or float64 or int32 or int64.
|
|
x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
|
|
self.assertRaises(TypeError, fluid.layers.reduce_sum, x2)
|
|
|
|
|
|
class TestReduceMeanOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# The input type of reduce_mean_op must be Variable.
|
|
x1 = fluid.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], fluid.CPUPlace())
|
|
self.assertRaises(TypeError, fluid.layers.reduce_mean, x1)
|
|
# The input dtype of reduce_mean_op must be float32 or float64 or int32 or int64.
|
|
x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
|
|
self.assertRaises(TypeError, fluid.layers.reduce_mean, x2)
|
|
|
|
|
|
class API_TestSumOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
def test_dtype1():
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data(name="data", shape=[10], dtype="float32")
|
|
paddle.sum(data, dtype="int32")
|
|
|
|
self.assertRaises(ValueError, test_dtype1)
|
|
|
|
def test_dtype2():
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data(name="data", shape=[10], dtype="float32")
|
|
paddle.sum(data, dtype="float32")
|
|
|
|
self.assertRaises(ValueError, test_dtype2)
|
|
|
|
def test_dtype3():
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data(name="data", shape=[10], dtype="int32")
|
|
paddle.sum(data, dtype="bool")
|
|
|
|
self.assertRaises(ValueError, test_dtype3)
|
|
|
|
def test_dtype4():
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data(name="data", shape=[10], dtype="int32")
|
|
paddle.sum(data, dtype="int32")
|
|
|
|
self.assertRaises(ValueError, test_dtype3)
|
|
|
|
|
|
class API_TestSumOp(unittest.TestCase):
|
|
def test_1(self):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="float32")
|
|
result_sum = paddle.sum(input=data, dim=1, dtype="float64")
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.rand(10, 10).astype(np.float32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_sum])
|
|
self.assertEqual(
|
|
(res == np.sum(input_data.astype(np.float64), axis=1)).all(), True)
|
|
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="int32")
|
|
result_sum = paddle.sum(input=data, dim=1, dtype="int64")
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_sum])
|
|
self.assertEqual(
|
|
(res == np.sum(input_data.astype(np.int64), axis=1)).all(), True)
|
|
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="int32")
|
|
result_sum = paddle.sum(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_sum])
|
|
self.assertEqual((res == np.sum(input_data, axis=1)).all(), True)
|
|
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="int32")
|
|
result_sum = paddle.sum(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_sum])
|
|
self.assertEqual((res == np.sum(input_data, axis=1)).all(), True)
|
|
|
|
with fluid.dygraph.guard():
|
|
np_x = np.array([10, 10]).astype('float64')
|
|
x = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.sum(x, dim=0)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.sum(np_x, axis=0))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
class API_TestMaxOp(unittest.TestCase):
|
|
def test_1(self):
|
|
# type: float
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="float32")
|
|
result_max = paddle.max(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.rand(10, 10).astype(np.float32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
|
|
self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
|
|
|
|
# type: int
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="int64")
|
|
result_max = paddle.max(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
|
|
self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
|
|
|
|
# dygraph
|
|
with fluid.dygraph.guard():
|
|
np_x = np.array([10, 10]).astype('float64')
|
|
x = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.max(x, dim=0)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.max(np_x, axis=0))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
class API_TestMinOp(unittest.TestCase):
|
|
def test_1(self):
|
|
# type: float
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="float32")
|
|
result_min = paddle.min(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.rand(10, 10).astype(np.float32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
|
|
self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
|
|
|
|
# type: int
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data = fluid.data("data", shape=[10, 10], dtype="int64")
|
|
result_min = paddle.min(input=data, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
|
|
self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
|
|
|
|
# dygraph
|
|
with fluid.dygraph.guard():
|
|
np_x = np.array([10, 10]).astype('float64')
|
|
x = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.min(x, dim=0)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.min(np_x, axis=0))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|