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79 lines
2.7 KiB
79 lines
2.7 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.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestAccuracyOp(OpTest):
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def setUp(self):
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self.op_type = "accuracy"
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self.dtype = np.float32
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self.init_dtype()
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n = 8192
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infer = np.random.random((n, 1)).astype(self.dtype)
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indices = np.random.randint(0, 2, (n, 1)).astype('int64')
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label = np.random.randint(0, 2, (n, 1)).astype('int64')
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self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
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num_correct = 0
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for rowid in range(n):
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for ele in indices[rowid]:
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if ele == label[rowid]:
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num_correct += 1
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break
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self.outputs = {
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'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
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'Correct': np.array([num_correct]).astype("int32"),
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'Total': np.array([n]).astype("int32")
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}
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def init_dtype(self):
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pass
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def test_check_output(self):
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self.check_output()
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class TestAccuracyOpFp16(TestAccuracyOp):
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(atol=1e-3)
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class TestAccuracyOpError(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 accuracy_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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label = fluid.layers.data(
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name='label', shape=[-1, 1], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.accuracy, x1, label)
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# The input dtype of accuracy_op must be float32 or float64.
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.accuracy, x2, label)
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x3 = fluid.layers.data(name='input', shape=[-1, 2], dtype="float16")
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fluid.layers.accuracy(input=x3, label=label)
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if __name__ == '__main__':
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unittest.main()
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