wehub-resource-sync
275 lines
8.5 KiB
Python
275 lines
8.5 KiB
Python
# Copyright (c) 2022 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_devices
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import paddle
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def call_MultiLabelSoftMarginLoss_layer(
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input,
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label,
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weight=None,
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reduction='mean',
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):
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multi_label_margin_loss = paddle.nn.MultiLabelSoftMarginLoss(
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weight=weight, reduction=reduction
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)
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res = multi_label_margin_loss(
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input=input,
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label=label,
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)
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return res
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def call_MultiLabelSoftMarginLoss_functional(
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input,
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label,
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weight=None,
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reduction='mean',
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):
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res = paddle.nn.functional.multi_label_soft_margin_loss(
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input,
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label,
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reduction=reduction,
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weight=weight,
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)
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return res
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def test_static(
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place,
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input_np,
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label_np,
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weight_np=None,
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reduction='mean',
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functional=False,
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):
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paddle.enable_static()
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=input_np.shape, dtype='float64'
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)
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label = paddle.static.data(
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name='label', shape=label_np.shape, dtype='float64'
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)
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feed_dict = {
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"input": input_np,
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"label": label_np,
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}
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weight = None
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if weight_np is not None:
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weight = paddle.static.data(
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name='weight', shape=weight_np.shape, dtype='float64'
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)
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feed_dict['weight'] = weight_np
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if functional:
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res = call_MultiLabelSoftMarginLoss_functional(
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input=input, label=label, weight=weight, reduction=reduction
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)
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else:
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res = call_MultiLabelSoftMarginLoss_layer(
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input=input, label=label, weight=weight, reduction=reduction
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)
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exe = paddle.static.Executor(place)
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(static_result,) = exe.run(prog, feed=feed_dict, fetch_list=[res])
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return static_result
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def test_dygraph(
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place, input_np, label_np, weight=None, reduction='mean', functional=False
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):
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with paddle.base.dygraph.base.guard():
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input = paddle.to_tensor(input_np)
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label = paddle.to_tensor(label_np)
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if weight is not None:
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weight = paddle.to_tensor(weight)
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if functional:
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dy_res = call_MultiLabelSoftMarginLoss_functional(
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input=input, label=label, weight=weight, reduction=reduction
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)
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else:
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dy_res = call_MultiLabelSoftMarginLoss_layer(
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input=input, label=label, weight=weight, reduction=reduction
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)
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dy_result = dy_res.numpy()
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return dy_result
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def calc_multi_label_margin_loss(
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input,
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label,
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weight=None,
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reduction="mean",
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):
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def LogSigmoid(x):
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return np.log(1 / (1 + np.exp(-x)))
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loss = -(label * LogSigmoid(input) + (1 - label) * LogSigmoid(-input))
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if weight is not None:
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loss = loss * weight
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loss = loss.mean(axis=-1) # only return N loss values
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if reduction == "none":
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return loss
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elif reduction == "mean":
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return np.mean(loss)
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elif reduction == "sum":
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return np.sum(loss)
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class TestMultiLabelMarginLoss(unittest.TestCase):
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def test_MultiLabelSoftMarginLoss(self):
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input = np.random.uniform(0.1, 0.8, size=(5, 5)).astype(np.float64)
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label = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
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places = get_devices()
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reductions = ['sum', 'mean', 'none']
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for place in places:
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for reduction in reductions:
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expected = calc_multi_label_margin_loss(
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input=input, label=label, reduction=reduction
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)
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dy_result = test_dygraph(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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)
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static_result = test_static(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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static_functional = test_static(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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functional=True,
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)
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dy_functional = test_dygraph(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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functional=True,
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)
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np.testing.assert_allclose(
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static_functional, expected, rtol=1e-05
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)
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np.testing.assert_allclose(
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static_functional, dy_functional, rtol=1e-05
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)
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np.testing.assert_allclose(dy_functional, expected, rtol=1e-05)
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def test_MultiLabelSoftMarginLoss_error(self):
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paddle.disable_static()
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self.assertRaises(
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ValueError,
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paddle.nn.MultiLabelSoftMarginLoss,
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reduction="unsupported reduction",
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)
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input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
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label = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
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self.assertRaises(
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ValueError,
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paddle.nn.functional.multi_label_soft_margin_loss,
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input=input,
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label=label,
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reduction="unsupported reduction",
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)
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paddle.enable_static()
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def test_MultiLabelSoftMarginLoss_weights(self):
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input = np.random.uniform(0.1, 0.8, size=(5, 5)).astype(np.float64)
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label = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
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weight = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
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place = 'cpu'
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reduction = 'mean'
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expected = calc_multi_label_margin_loss(
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input=input, label=label, weight=weight, reduction=reduction
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)
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dy_result = test_dygraph(
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place=place,
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input_np=input,
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label_np=label,
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weight=weight,
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reduction=reduction,
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)
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static_result = test_static(
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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reduction=reduction,
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)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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static_functional = test_static(
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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reduction=reduction,
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functional=True,
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)
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dy_functional = test_dygraph(
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place=place,
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input_np=input,
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label_np=label,
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weight=weight,
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reduction=reduction,
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functional=True,
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)
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np.testing.assert_allclose(static_functional, expected, rtol=1e-05)
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np.testing.assert_allclose(static_functional, dy_functional, rtol=1e-05)
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np.testing.assert_allclose(dy_functional, expected, rtol=1e-05)
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def test_MultiLabelSoftMarginLoss_dimension(self):
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paddle.disable_static()
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input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
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label = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
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self.assertRaises(
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ValueError,
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paddle.nn.functional.multi_label_soft_margin_loss,
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input=input,
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label=label,
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)
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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