68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
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# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, x, y, z):
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x = x[:,:12,1:14:2]
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x = x[...,1:]
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x = x[:,:,:x.size(2)-1]
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y = y[0:,1:,5:,3:]
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y = y[:,:,1:13:2,:14]
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y = y[:1,:y.size(1):,:,:]
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z = z[4:]
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z = z[:2,:,:,:,2:-2:3]
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z = z[:,:,:,z.size(3)-3:,:]
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return x, y, z
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def test():
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net = Model()
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net.eval()
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torch.manual_seed(0)
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x = torch.rand(1, 13, 26)
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y = torch.rand(1, 15, 19, 21)
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z = torch.rand(14, 18, 15, 19, 20)
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a = net(x, y, z)
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# export torchscript
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mod = torch.jit.trace(net, (x, y, z))
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mod.save("test_Tensor_slice.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_Tensor_slice.pt inputshape=[1,13,26],[1,15,19,21],[14,18,15,19,20]")
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# pnnx inference
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import test_Tensor_slice_pnnx
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b = test_Tensor_slice_pnnx.test_inference()
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for a0, b0 in zip(a, b):
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if not torch.equal(a0, b0):
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return False
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return True
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if __name__ == "__main__":
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if test():
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exit(0)
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else:
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exit(1)
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