wehub-resource-sync
245 lines
9.3 KiB
Python
245 lines
9.3 KiB
Python
# Copyright (c) 2021 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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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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np.random.seed(42)
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def calc_hinge_embedding_loss(input, label, margin=1.0, reduction='mean'):
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result = np.where(
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label == -1.0, np.maximum(0.0, margin - input), 0.0
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) + np.where(label == 1.0, input, 0.0)
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if reduction == 'none':
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return result
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elif reduction == 'sum':
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return np.sum(result)
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elif reduction == 'mean':
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return np.mean(result)
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class TestFunctionalHingeEmbeddingLoss(unittest.TestCase):
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def setUp(self):
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self.margin = 1.0
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self.shape = (10, 10, 5)
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self.input_np = np.random.random(size=self.shape).astype(np.float64)
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# get label elem in {1., -1.}
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self.label_np = 2 * np.random.randint(0, 2, size=self.shape) - 1.0
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def run_dynamic_check(self, place=paddle.CPUPlace()):
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paddle.disable_static(place=place)
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input = paddle.to_tensor(self.input_np)
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label = paddle.to_tensor(self.label_np, dtype="float64")
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dy_result = paddle.nn.functional.hinge_embedding_loss(input, label)
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expected = calc_hinge_embedding_loss(self.input_np, self.label_np)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, [])
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dy_result = paddle.nn.functional.hinge_embedding_loss(
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input, label, reduction='sum'
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)
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction='sum'
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)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, [])
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dy_result = paddle.nn.functional.hinge_embedding_loss(
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input, label, reduction='none'
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)
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction='none'
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)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, list(self.shape))
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def run_static_check(self, place=paddle.CPUPlace):
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paddle.enable_static()
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for reduction in ['none', 'mean', 'sum']:
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction=reduction
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)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input = paddle.static.data(
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name="input", shape=self.shape, dtype="float64"
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)
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label = paddle.static.data(
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name="label", shape=self.shape, dtype="float64"
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)
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st_result = paddle.nn.functional.hinge_embedding_loss(
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input, label, reduction=reduction
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)
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exe = paddle.static.Executor(place)
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(result_numpy,) = exe.run(
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feed={"input": self.input_np, "label": self.label_np},
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fetch_list=[st_result],
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)
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np.testing.assert_allclose(result_numpy, expected, rtol=1e-05)
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def test_cpu(self):
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self.run_dynamic_check(place=paddle.CPUPlace())
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self.run_static_check(place=paddle.CPUPlace())
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def test_gpu(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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self.run_dynamic_check(place=get_device_place())
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self.run_static_check(place=get_device_place())
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# test case the raise message
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def test_reduce_errors(self):
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def test_value_error():
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loss = paddle.nn.functional.hinge_embedding_loss(
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self.input_np, self.label_np, reduction='reduce_mean'
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)
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self.assertRaises(ValueError, test_value_error)
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class TestClassHingeEmbeddingLoss(unittest.TestCase):
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def setUp(self):
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self.margin = 1.0
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self.shape = (10, 10, 5)
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self.input_np = np.random.random(size=self.shape).astype(np.float64)
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# get label elem in {1., -1.}
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self.label_np = 2 * np.random.randint(0, 2, size=self.shape) - 1.0
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def run_dynamic_check(self, place=paddle.CPUPlace()):
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paddle.disable_static(place=place)
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input = paddle.to_tensor(self.input_np)
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label = paddle.to_tensor(self.label_np, dtype="float64")
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hinge_embedding_loss = paddle.nn.loss.HingeEmbeddingLoss()
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dy_result = hinge_embedding_loss(input, label)
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expected = calc_hinge_embedding_loss(self.input_np, self.label_np)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, [])
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hinge_embedding_loss = paddle.nn.loss.HingeEmbeddingLoss(
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reduction='sum'
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)
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dy_result = hinge_embedding_loss(input, label)
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction='sum'
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)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, [])
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hinge_embedding_loss = paddle.nn.loss.HingeEmbeddingLoss(
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reduction='none'
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)
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dy_result = hinge_embedding_loss(input, label)
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction='none'
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)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertTrue(dy_result.shape, list(self.shape))
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def run_static_check(self, place=paddle.CPUPlace):
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paddle.enable_static()
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for reduction in ['none', 'mean', 'sum']:
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction=reduction
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)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input = paddle.static.data(
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name="input", shape=self.shape, dtype="float64"
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)
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label = paddle.static.data(
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name="label", shape=self.shape, dtype="float64"
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)
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hinge_embedding_loss = paddle.nn.loss.HingeEmbeddingLoss(
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reduction=reduction
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)
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st_result = hinge_embedding_loss(input, label)
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exe = paddle.static.Executor(place)
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(result_numpy,) = exe.run(
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feed={"input": self.input_np, "label": self.label_np},
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fetch_list=[st_result],
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)
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np.testing.assert_allclose(result_numpy, expected, rtol=1e-05)
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def test_cpu(self):
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self.run_dynamic_check(place=paddle.CPUPlace())
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self.run_static_check(place=paddle.CPUPlace())
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def test_gpu(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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self.run_dynamic_check(place=get_device_place())
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self.run_static_check(place=get_device_place())
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# test case the raise message
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def test_reduce_errors(self):
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def test_value_error():
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hinge_embedding_loss = paddle.nn.loss.HingeEmbeddingLoss(
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reduction='reduce_mean'
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)
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loss = hinge_embedding_loss(self.input_np, self.label_np)
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self.assertRaises(ValueError, test_value_error)
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class TestFunctionalHingeEmbeddingLoss_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.margin = 1.0
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self.shape = (0, 10, 5) # zero size
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self.input_np = np.random.random(size=self.shape).astype(np.float64)
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self.label_np = 2 * np.random.randint(0, 2, size=self.shape) - 1.0
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def run_dynamic_check(self, place=paddle.CPUPlace()):
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paddle.disable_static(place=place)
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input = paddle.to_tensor(self.input_np)
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input.stop_gradient = False
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label = paddle.to_tensor(self.label_np, dtype="float64")
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dy_result = paddle.nn.functional.hinge_embedding_loss(input, label)
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expected = calc_hinge_embedding_loss(self.input_np, self.label_np)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, [])
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dy_result = paddle.nn.functional.hinge_embedding_loss(
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input, label, reduction='none'
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)
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expected = calc_hinge_embedding_loss(
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self.input_np, self.label_np, reduction='none'
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)
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np.testing.assert_allclose(dy_result.numpy(), expected, rtol=1e-05)
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loss = paddle.sum(dy_result)
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loss.backward()
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self.assertEqual(input.grad.shape, input.shape)
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def test_cpu(self):
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self.run_dynamic_check(place=paddle.CPUPlace())
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def test_gpu(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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self.run_dynamic_check(place=get_device_place())
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if __name__ == "__main__":
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unittest.main()
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