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244 lines
9.7 KiB
244 lines
9.7 KiB
# Copyright (c) 2020 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 paddle
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import paddle.fluid as fluid
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import numpy as np
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import unittest
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from op_test import OpTest
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def test_static_layer(place,
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input_np,
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label_np,
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reduction='mean',
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weight_np=None):
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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.fluid.data(name='input', shape=input_np.shape, dtype='float64')
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label = paddle.fluid.data(name='label', shape=label_np.shape, dtype='float64')
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if weight_np is not None:
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weight = paddle.fluid.data(
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name='weight', shape=weight_np.shape, dtype='float64')
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bce_loss = paddle.nn.loss.BCELoss(
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weight=weight, reduction=reduction)
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else:
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bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
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res = bce_loss(input, label)
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exe = paddle.static.Executor(place)
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static_result = exe.run(prog,
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feed={"input": input_np,
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"label": label_np}
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if weight_np is None else {
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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return static_result
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def test_static_functional(place,
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input_np,
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label_np,
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reduction='mean',
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weight_np=None):
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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.fluid.data(name='input', shape=input_np.shape, dtype='float64')
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label = paddle.fluid.data(name='label', shape=label_np.shape, dtype='float64')
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if weight_np is not None:
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weight = paddle.fluid.data(
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name='weight', shape=weight_np.shape, dtype='float64')
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res = paddle.nn.functional.binary_cross_entropy(
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input, label, weight=weight, reduction=reduction)
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else:
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res = paddle.nn.functional.binary_cross_entropy(
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input, label, reduction=reduction)
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exe = paddle.static.Executor(place)
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static_result = exe.run(prog,
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feed={"input": input_np,
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"label": label_np}
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if weight_np is None else {
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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return static_result
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def test_dygraph_layer(place,
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input_np,
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label_np,
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reduction='mean',
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weight_np=None):
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paddle.disable_static()
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if weight_np is not None:
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weight = paddle.to_tensor(weight_np)
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bce_loss = paddle.nn.loss.BCELoss(weight=weight, reduction=reduction)
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else:
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bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
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dy_res = bce_loss(paddle.to_tensor(input_np), paddle.to_tensor(label_np))
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dy_result = dy_res.numpy()
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paddle.enable_static()
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return dy_result
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def test_dygraph_functional(place,
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input_np,
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label_np,
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reduction='mean',
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weight_np=None):
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paddle.disable_static()
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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_np is not None:
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weight = paddle.to_tensor(weight_np)
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dy_res = paddle.nn.functional.binary_cross_entropy(
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input, label, weight=weight, reduction=reduction)
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else:
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dy_res = paddle.nn.functional.binary_cross_entropy(
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input, label, reduction=reduction)
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dy_result = dy_res.numpy()
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paddle.enable_static()
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return dy_result
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def calc_bceloss(input_np, label_np, reduction='mean', weight_np=None):
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if weight_np is None:
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expected = -1 * (label_np * np.log(input_np) +
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(1. - label_np) * np.log(1. - input_np))
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else:
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expected = -1 * weight_np * (label_np * np.log(input_np) +
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(1. - label_np) * np.log(1. - input_np))
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if reduction == 'mean':
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expected = np.mean(expected)
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elif reduction == 'sum':
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expected = np.sum(expected)
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else:
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expected = expected
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return expected
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class TestBCELoss(unittest.TestCase):
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def test_BCELoss(self):
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input_np = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
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label_np = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)
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places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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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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static_result = test_static_layer(place, input_np, label_np,
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reduction)
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dy_result = test_dygraph_layer(place, input_np, label_np,
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reduction)
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expected = calc_bceloss(input_np, label_np, reduction)
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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static_functional = test_static_functional(place, input_np,
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label_np, reduction)
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dy_functional = test_dygraph_functional(place, input_np,
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label_np, reduction)
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self.assertTrue(np.allclose(static_functional, expected))
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self.assertTrue(np.allclose(static_functional, dy_functional))
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self.assertTrue(np.allclose(dy_functional, expected))
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def test_BCELoss_weight(self):
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input_np = np.random.uniform(
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0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
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label_np = np.random.randint(
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0, 2, size=(2, 3, 4, 10)).astype(np.float64)
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weight_np = np.random.random(size=(3, 4, 10)).astype(np.float64)
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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for reduction in ['sum', 'mean', 'none']:
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static_result = test_static_layer(
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place, input_np, label_np, reduction, weight_np=weight_np)
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dy_result = test_dygraph_layer(
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place, input_np, label_np, reduction, weight_np=weight_np)
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expected = calc_bceloss(
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input_np, label_np, reduction, weight_np=weight_np)
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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static_functional = test_static_functional(
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place, input_np, label_np, reduction, weight_np=weight_np)
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dy_functional = test_dygraph_functional(
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place, input_np, label_np, reduction, weight_np=weight_np)
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self.assertTrue(np.allclose(static_functional, expected))
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self.assertTrue(np.allclose(static_functional, dy_functional))
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self.assertTrue(np.allclose(dy_functional, expected))
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def test_BCELoss_error(self):
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paddle.disable_static()
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self.assertRaises(
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ValueError, paddle.nn.loss.BCELoss, reduction="unsupport reduction")
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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.binary_cross_entropy,
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input=input,
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label=label,
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reduction="unsupport reduction")
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paddle.enable_static()
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def bce_loss(input, label):
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return -1 * (label * np.log(input) + (1. - label) * np.log(1. - input))
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class TestBceLossOp(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = "bce_loss"
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input_np = np.random.uniform(0.1, 0.8, self.shape).astype("float64")
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label_np = np.random.randint(0, 2, self.shape).astype("float64")
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output_np = bce_loss(input_np, label_np)
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self.inputs = {'X': input_np, 'Label': label_np}
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self.outputs = {'Out': output_np}
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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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def init_test_case(self):
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self.shape = [10, 10]
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class TestBceLossOpCase1(OpTest):
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def init_test_cast(self):
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self.shape = [2, 3, 4, 5]
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class TestBceLossOpCase2(OpTest):
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def init_test_cast(self):
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self.shape = [2, 3, 20]
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if __name__ == "__main__":
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unittest.main()
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