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[API Compatibility No.155] Keep input/target alias for bce_with_logits-part #846

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[API Compatibility No.155] Keep input/target alias for bce_with_logits-part #846

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2 changes: 1 addition & 1 deletion Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ lint:
test: unit-test

unit-test:
PYTHONPATH=. pytest tests
PYTHONPATH=.:tests pytest tests

# # # # # # # # # # # # # # # Coverage Block # # # # # # # # # # # # # # #

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6 changes: 1 addition & 5 deletions paconvert/api_mapping.json
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Original file line number Diff line number Diff line change
Expand Up @@ -8404,7 +8404,7 @@
"min_input_args": 2
},
"torch.nn.functional.binary_cross_entropy_with_logits": {
"Matcher": "SizeAverageMatcher",
"Matcher": "ChangePrefixMatcher",
"paddle_api": "paddle.nn.functional.binary_cross_entropy_with_logits",
"args_list": [
"input",
Expand All @@ -8415,10 +8415,6 @@
"reduction",
"pos_weight"
],
"kwargs_change": {
"input": "logit",
"target": "label"
},
"min_input_args": 2
},
"torch.nn.functional.celu": {
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2 changes: 1 addition & 1 deletion scripts/unittest_check.sh
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Expand Up @@ -47,7 +47,7 @@ python -m pip install -r requirements.txt
echo '************************************************************************************************************'
echo "Checking code unit test by pytest ..."
python -m pip install pytest-timeout
python -m pytest -v -s -p no:warnings ./tests;check_error=$?
PYTHONPATH=.:tests python -m pytest -v -s -p no:warnings ./tests;check_error=$?

echo '************************************************************************************************************'
echo "______ _____ _ "
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4 changes: 2 additions & 2 deletions scripts/unittest_check_gpu.sh
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Expand Up @@ -47,10 +47,10 @@ python -m pip install -r requirements.txt
echo '************************************************************************************************************'
echo "Checking code unit test by pytest ..."
python -m pip install pytest-timeout pytest-xdist pytest-rerunfailures
python -m pytest -v -s -p no:warnings -n 1 --reruns=3 ./tests; check_error=$?
PYTHONPATH=.:tests python -m pytest -v -s -p no:warnings -n 1 --reruns=3 ./tests; check_error=$?
if [ ${check_error} != 0 ];then
echo "Rerun unit test check."
python -m pytest -v -s -p no:warnings -n 1 --lf ./tests; check_error=$?
PYTHONPATH=.:tests python -m pytest -v -s -p no:warnings -n 1 --lf ./tests; check_error=$?
fi

echo '************************************************************************************************************'
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94 changes: 73 additions & 21 deletions tests/test_nn_BCEWithLogitsLoss.py
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Expand Up @@ -25,8 +25,13 @@ def test_case_1():
import torch
import torch.nn as nn
loss = torch.nn.BCEWithLogitsLoss(reduction='none')
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -38,9 +43,17 @@ def test_case_2():
"""
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss(weight=torch.tensor([1.0,0.2, 0.2]), reduction='none')
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
weight = torch.ones(3)
weight[1] = 0.2
weight[2] = 0.2
loss = nn.BCEWithLogitsLoss(weight=weight, reduction='none')
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -52,9 +65,14 @@ def test_case_3():
"""
import torch
import torch.nn as nn
loss= nn.BCEWithLogitsLoss(pos_weight = torch.ones([3]))
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
loss= nn.BCEWithLogitsLoss(pos_weight=torch.ones(3))
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -67,8 +85,13 @@ def test_case_4():
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss(size_average=True)
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -81,8 +104,13 @@ def test_case_5():
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss()
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -94,9 +122,17 @@ def test_case_6():
"""
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss(weight=torch.tensor([1.0,0.2, 0.2]), size_average=None, reduce=None, reduction='none', pos_weight=None)
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
weight = torch.ones(3)
weight[1] = 0.2
weight[2] = 0.2
loss = nn.BCEWithLogitsLoss(weight=weight, size_average=None, reduce=None, reduction='none', pos_weight=None)
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -109,9 +145,17 @@ def test_case_7():
"""
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss(torch.tensor([1.0,0.2, 0.2]), None, None, 'none', None)
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
weight = torch.ones(3)
weight[1] = 0.2
weight[2] = 0.2
loss = nn.BCEWithLogitsLoss(weight, None, None, 'none', None)
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
Expand All @@ -124,9 +168,17 @@ def test_case_8():
"""
import torch
import torch.nn as nn
loss = nn.BCEWithLogitsLoss(pos_weight=None, reduction='none', reduce=None, size_average=None, weight=torch.tensor([1.0,0.2, 0.2]))
input = torch.tensor([1.,0.7,0.2], requires_grad=True)
target = torch.tensor([1.,0., 0.])
weight = torch.ones(3)
weight[1] = 0.2
weight[2] = 0.2
loss = nn.BCEWithLogitsLoss(pos_weight=None, reduction='none', reduce=None, size_average=None, weight=weight)
input = torch.zeros(3)
input[0] = 1.0
input[1] = 0.7
input[2] = 0.2
input.requires_grad = True
target = torch.zeros(3)
target[0] = 1.0
result = loss(input, target)
"""
)
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