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- # 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
- from packaging import version
-
- class Model(nn.Module):
- def __init__(self):
- super(Model, self).__init__()
-
- def forward(self, x, y, z):
- x = torch.repeat_interleave(x, 2)
- y = torch.repeat_interleave(y, 3, dim=1)
- if version.parse(torch.__version__) >= version.parse('1.10'):
- z = torch.repeat_interleave(z, torch.tensor([2, 1, 3]), dim=0, output_size=6)
- else:
- z = torch.repeat_interleave(z, torch.tensor([2, 1, 3]), dim=0)
- return x, y, z
-
- def test():
- net = Model()
- net.eval()
-
- torch.manual_seed(0)
- x = torch.rand(3)
- y = torch.rand(4, 5)
- z = torch.rand(3, 7, 8)
-
- a = net(x, y, z)
-
- # export torchscript
- mod = torch.jit.trace(net, (x, y, z))
- mod.save("test_torch_repeat_interleave.pt")
-
- # torchscript to pnnx
- import os
- os.system("../src/pnnx test_torch_repeat_interleave.pt inputshape=[3],[4,5],[3,7,8]")
-
- # pnnx inference
- import test_torch_repeat_interleave_pnnx
- b = test_torch_repeat_interleave_pnnx.test_inference()
-
- for a0, b0 in zip(a, b):
- if not torch.equal(a0, b0):
- return False
- return True
-
- if __name__ == "__main__":
- if test():
- exit(0)
- else:
- exit(1)
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