【Hackathon 5th No.42】 为Paddle代码转换工具新增API转换规则 (第1组 编号1-20) -part #306
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,91 @@ | ||
| # 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. | ||
| # | ||
| import textwrap | ||
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| from apibase import APIBase | ||
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| obj = APIBase("torch.Tensor.as_strided") | ||
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| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[ 0.0335, 0.1830, -0.1269], | ||
| [ 0.1897, -0.1422, -0.4940], | ||
| [-0.7674, -0.0134, -0.3733]]) | ||
| results = x.as_strided((2, 2), (1, 2)) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["results"]) | ||
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| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[ 0.0335, 0.1830, -0.1269], | ||
| [ 0.1897, -0.1422, -0.4940], | ||
| [-0.7674, -0.0134, -0.3733]]) | ||
| results = x.as_strided((2, 2), (1, 2), 0) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["results"]) | ||
|
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||
|
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| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[ 0.0335, 0.1830, -0.1269], | ||
| [ 0.1897, -0.1422, -0.4940], | ||
| [-0.7674, -0.0134, -0.3733]]) | ||
| size = (2, 2) | ||
| stride = (1, 2) | ||
| storage_offset = 0 | ||
| results = x.as_strided(size, stride, storage_offset) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["results"]) | ||
|
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|
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| def test_case_4(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[ 0.0335, 0.1830, -0.1269], | ||
| [ 0.1897, -0.1422, -0.4940], | ||
| [-0.7674, -0.0134, -0.3733]]) | ||
| size = (2, 2) | ||
| stride = (1, 2) | ||
| results = x.as_strided(size, stride, 0) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["results"]) | ||
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| def test_case_5(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[ 0.0335, 0.1830, -0.1269], | ||
| [ 0.1897, -0.1422, -0.4940], | ||
| [-0.7674, -0.0134, -0.3733]]) | ||
| size = (2, 2) | ||
| stride = (1, 2) | ||
| results = x.as_strided(size = (2,2), stride = (2,2), storage_offset = 0) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["results"]) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| # 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. | ||
|
|
||
| import textwrap | ||
|
|
||
| from apibase import APIBase | ||
|
|
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| obj = APIBase("torch.Tensor.cummax") | ||
|
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|
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| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1.0, 1.0, 1.0], | ||
| [2.0, 2.0, 2.0], | ||
| [3.0, 3.0, 3.0]]) | ||
| result = x.cummax(0) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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|
|
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| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1.0, 1.0, 1.0], | ||
| [2.0, 2.0, 2.0], | ||
| [3.0, 3.0, 3.0]]) | ||
| result = x.cummax(1) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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||
|
|
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| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1.0, 1.0, 1.0], | ||
| [2.0, 2.0, 2.0], | ||
| [3.0, 3.0, 3.0]]) | ||
| result = x.cummax(dim=1) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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| def test_case_4(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1.0, 1.0, 1.0], | ||
| [2.0, 2.0, 2.0], | ||
| [3.0, 3.0, 3.0]]) | ||
| result = x.cummax(dim=0) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) |
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paddle有
paddle.Tensor.view_as