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* add some loss api * unify LossMatcher * fix mseloss * fix unfold bug * fix Tuple2ListMatcher * fix some comments
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| # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| import textwrap | ||
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| from apibase import APIBase | ||
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| obj = APIBase("torch.nn.BCELoss") | ||
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| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,size_average=True) | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,size_average=False) | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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|
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| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,reduction='none') | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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|
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| def test_case_4(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,reduction='mean') | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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||
|
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| def test_case_5(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,reduction='sum') | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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||
|
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| def test_case_6(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,reduce=True) | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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||
|
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| def test_case_7(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| weight = torch.tensor([0.5,0.2,0.3]) | ||
| loss = torch.nn.BCELoss(weight=weight,reduce=False) | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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|
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| def test_case_8(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[0.2837, 0.0297, 0.0355], | ||
| [ 0.9112, 0.7526, 0.4061]]) | ||
| target = torch.tensor([[1.,0.,1.],[0.,1.,0.]]) | ||
| loss = torch.nn.BCELoss() | ||
| result = loss(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) |
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