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117 lines
3.8 KiB
117 lines
3.8 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 Program, program_guard
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class TestRankLossOp(OpTest):
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def setUp(self):
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self.op_type = "rank_loss"
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shape = (100, 1)
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# labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
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label_shape, left_shape, right_shape = self.set_shape()
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label = np.random.randint(0, 2, size=shape).astype("float32")
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left = np.random.random(shape).astype("float32")
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right = np.random.random(shape).astype("float32")
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loss = np.log(1.0 + np.exp(left - right)) - label * (left - right)
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loss = np.reshape(loss, label_shape)
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self.inputs = {
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'Label': label.reshape(label_shape),
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'Left': left.reshape(left_shape),
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'Right': right.reshape(right_shape)
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}
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self.outputs = {'Out': loss.reshape(label_shape)}
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def set_shape(self):
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batch_size = 100
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return (batch_size, 1), (batch_size, 1), (batch_size, 1)
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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(["Left", "Right"], "Out")
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def test_check_grad_ignore_left(self):
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self.check_grad(["Right"], "Out", no_grad_set=set('Left'))
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def test_check_grad_ignore_right(self):
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self.check_grad(["Left"], "Out", no_grad_set=set('Right'))
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class TestRankLossOp1(TestRankLossOp):
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def set_shape(self):
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batch_size = 100
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return (batch_size), (batch_size, 1), (batch_size, 1)
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class TestRankLossOp2(TestRankLossOp):
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def set_shape(self):
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batch_size = 100
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return (batch_size, 1), (batch_size), (batch_size, 1)
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class TestRankLossOp3(TestRankLossOp):
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def set_shape(self):
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batch_size = 100
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return (batch_size, 1), (batch_size, 1), (batch_size)
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class TestRankLossOp4(TestRankLossOp):
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def set_shape(self):
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batch_size = 100
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return (batch_size), (batch_size), (batch_size, 1)
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class TestRankLossOp5(TestRankLossOp):
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def set_shape(self):
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batch_size = 100
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return (batch_size), (batch_size), (batch_size)
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class TestRankLossOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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label = fluid.data(name="label", shape=[16, 1], dtype="float32")
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left = fluid.data(name="left", shape=[16, 1], dtype="float32")
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right = fluid.data(name="right", shape=[16, 1], dtype="float32")
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def test_label_Variable():
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label_data = np.random.rand(16, 1).astype("float32")
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out = fluid.layers.rank_loss(label_data, left, right)
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self.assertRaises(TypeError, test_label_Variable)
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def test_left_Variable():
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left_data = np.random.rand(16, 1).astype("float32")
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out = fluid.layers.rank_loss(label, left_data, right)
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self.assertRaises(TypeError, test_left_Variable)
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def test_right_Variable():
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right_data = np.random.rand(16, 1).astype("float32")
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out = fluid.layers.rank_loss(label, left, right_data)
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self.assertRaises(TypeError, test_right_Variable)
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if __name__ == '__main__':
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unittest.main()
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