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pnnx convert torch maximum minimum and torch max min as expression (#…
…4944) * reset device check dtype kind int * placeholder for ncnn sign * convert torch maximum minimum * torch.max as expression * torch.min as expression
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| # Tencent is pleased to support the open source community by making ncnn available. | ||
| # | ||
| # Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved. | ||
| # | ||
| # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| # in compliance with the License. You may obtain a copy of the License at | ||
| # | ||
| # https://opensource.org/licenses/BSD-3-Clause | ||
| # | ||
| # 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 torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
|
|
||
| class Model(nn.Module): | ||
| def __init__(self): | ||
| super(Model, self).__init__() | ||
|
|
||
| def forward(self, x, y, z): | ||
| out0 = torch.maximum(x, y) | ||
| out1 = torch.maximum(y, y) | ||
| out2 = torch.maximum(z, torch.ones_like(z) + 0.1) | ||
| return out0, out1, out2 | ||
|
|
||
| def test(): | ||
| net = Model() | ||
| net.eval() | ||
|
|
||
| torch.manual_seed(0) | ||
| x = torch.rand(3, 16) | ||
| y = torch.rand(3, 16) | ||
| z = torch.rand(5, 9, 3) | ||
|
|
||
| a = net(x, y, z) | ||
|
|
||
| # export torchscript | ||
| mod = torch.jit.trace(net, (x, y, z)) | ||
| mod.save("test_torch_maximum.pt") | ||
|
|
||
| # torchscript to pnnx | ||
| import os | ||
| os.system("../../src/pnnx test_torch_maximum.pt inputshape=[3,16],[3,16],[5,9,3]") | ||
|
|
||
| # ncnn inference | ||
| import test_torch_maximum_ncnn | ||
| b = test_torch_maximum_ncnn.test_inference() | ||
|
|
||
| for a0, b0 in zip(a, b): | ||
| if not torch.allclose(a0, b0, 1e-4, 1e-4): | ||
| return False | ||
| return True | ||
|
|
||
| if __name__ == "__main__": | ||
| if test(): | ||
| exit(0) | ||
| else: | ||
| exit(1) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| # Tencent is pleased to support the open source community by making ncnn available. | ||
| # | ||
| # Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved. | ||
| # | ||
| # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| # in compliance with the License. You may obtain a copy of the License at | ||
| # | ||
| # https://opensource.org/licenses/BSD-3-Clause | ||
| # | ||
| # 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 torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
|
|
||
| class Model(nn.Module): | ||
| def __init__(self): | ||
| super(Model, self).__init__() | ||
|
|
||
| def forward(self, x, y, z): | ||
| out0 = torch.minimum(x, y) | ||
| out1 = torch.minimum(y, y) | ||
| out2 = torch.minimum(z, torch.ones_like(z) + 0.1) | ||
| return out0, out1, out2 | ||
|
|
||
| def test(): | ||
| net = Model() | ||
| net.eval() | ||
|
|
||
| torch.manual_seed(0) | ||
| x = torch.rand(3, 16) | ||
| y = torch.rand(3, 16) | ||
| z = torch.rand(5, 9, 3) | ||
|
|
||
| a = net(x, y, z) | ||
|
|
||
| # export torchscript | ||
| mod = torch.jit.trace(net, (x, y, z)) | ||
| mod.save("test_torch_minimum.pt") | ||
|
|
||
| # torchscript to pnnx | ||
| import os | ||
| os.system("../../src/pnnx test_torch_minimum.pt inputshape=[3,16],[3,16],[5,9,3]") | ||
|
|
||
| # ncnn inference | ||
| import test_torch_minimum_ncnn | ||
| b = test_torch_minimum_ncnn.test_inference() | ||
|
|
||
| for a0, b0 in zip(a, b): | ||
| if not torch.allclose(a0, b0, 1e-4, 1e-4): | ||
| return False | ||
| return True | ||
|
|
||
| if __name__ == "__main__": | ||
| if test(): | ||
| exit(0) | ||
| else: | ||
| exit(1) |
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