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[API Compatibility No.154] add target alias for binary_cross_entropy -part #863

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[API Compatibility No.154] add target alias for binary_cross_entropy -part #863

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5 changes: 1 addition & 4 deletions paconvert/api_mapping.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8388,7 +8388,7 @@
}
},
"torch.nn.functional.binary_cross_entropy": {
"Matcher": "SizeAverageMatcher",
"Matcher": "ChangePrefixMatcher",
"paddle_api": "paddle.nn.functional.binary_cross_entropy",
"args_list": [
"input",
Expand All @@ -8398,9 +8398,6 @@
"reduce",
"reduction"
],
"kwargs_change": {
"target": "label"
},
"min_input_args": 2
},
"torch.nn.functional.binary_cross_entropy_with_logits": {
Expand Down
155 changes: 140 additions & 15 deletions tests/test_nn_functional_binary_cross_entropy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,19 @@ def test_case_1():
result = torch.nn.functional.binary_cross_entropy(input,target,weight=weight,size_average=True)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input, target, weight=weight, size_average=True
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


def test_case_2():
Expand All @@ -43,11 +55,22 @@ def test_case_2():
[ 0.9112, 0.7526, 0.4061]])
target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
weight = torch.tensor([0.5,0.2,0.3])
weight = torch.tensor([0.5,0.2,0.3])
result = torch.nn.functional.binary_cross_entropy(input,target,weight=weight,size_average=False)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input, target, weight=weight, size_average=False
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


def test_case_3():
Expand Down Expand Up @@ -107,7 +130,19 @@ def test_case_6():
result = torch.nn.functional.binary_cross_entropy(input,target,weight=weight,reduce=True)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input, target, weight=weight, reduce=True
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


def test_case_7():
Expand All @@ -122,7 +157,19 @@ def test_case_7():
result = torch.nn.functional.binary_cross_entropy(input,target,weight=weight,reduce=False)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input, target, weight=weight, reduce=False
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


def test_case_8():
Expand Down Expand Up @@ -151,10 +198,21 @@ def test_case_9():
result = torch.nn.functional.binary_cross_entropy(input,target,weight=weight, size_average=None, reduce=False, reduction='mean')
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input, target, weight=weight, size_average=None, reduce=False, reduction="mean"
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


# generated by validate_unittest autofix, based on test_case_9
def test_case_10():
pytorch_code = textwrap.dedent(
"""
Expand All @@ -163,14 +221,21 @@ def test_case_10():
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])
result = torch.nn.functional.binary_cross_entropy(input, target, weight, None, False, 'mean')
result = torch.nn.functional.binary_cross_entropy(input=input, target=target)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
result = paddle.nn.functional.binary_cross_entropy(input=input, target=target)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


# generated by validate_unittest autofix, based on test_case_9
def test_case_11():
pytorch_code = textwrap.dedent(
"""
Expand All @@ -180,14 +245,57 @@ def test_case_11():
[ 0.9112, 0.7526, 0.4061]])
target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
weight = torch.tensor([0.5,0.2,0.3])
result = torch.nn.functional.binary_cross_entropy(input=input, target=target, weight=weight, size_average=None, reduce=False, reduction='mean')
result = torch.nn.functional.binary_cross_entropy(target=target, input=input, reduction='sum', weight=weight)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
target=target, input=input, reduction="sum", weight=weight
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


# generated by validate_unittest autofix, based on test_case_9
def test_case_12():
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])
result = torch.nn.functional.binary_cross_entropy(input=input, target=target, weight=weight, size_average=None, reduce=False, reduction='mean')
"""
)
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
input=input,
target=target,
weight=weight,
size_average=None,
reduce=False,
reduction="mean",
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)


def test_case_13():
pytorch_code = textwrap.dedent(
"""
import torch
Expand All @@ -199,4 +307,21 @@ def test_case_12():
result = torch.nn.functional.binary_cross_entropy(reduction='mean', reduce=False, size_average=None, weight=weight, target=target, input=input)
"""
)
obj.run(pytorch_code, ["result"])
expect_paddle_code = textwrap.dedent(
"""
import paddle

input = paddle.tensor([[0.2837, 0.0297, 0.0355], [0.9112, 0.7526, 0.4061]])
target = paddle.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]])
weight = paddle.tensor([0.5, 0.2, 0.3])
result = paddle.nn.functional.binary_cross_entropy(
reduction="mean",
reduce=False,
size_average=None,
weight=weight,
target=target,
input=input,
)
"""
)
obj.run(pytorch_code, expect_paddle_code=expect_paddle_code)