75 lines
2.3 KiB
Python
75 lines
2.3 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, w):
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x = F.pad(x, (3,4), mode='constant', value=1.3)
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x = F.pad(x, (2,2))
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y = F.pad(y, (5,6), mode='reflect')
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y = F.pad(y, (2,1), mode='replicate')
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y = F.pad(y, (3,4), mode='constant', value=1.3)
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y = F.pad(y, (1,1))
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z = F.pad(z, (3,4,3,4), mode='reflect')
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z = F.pad(z, (2,1,2,0), mode='replicate')
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z = F.pad(z, (1,0,2,0), mode='constant', value=1.3)
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z = F.pad(z, (3,3,3,3))
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#w = F.pad(w, (1,2,3,4,5,6), mode='reflect')
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w = F.pad(w, (5,0,1,2,0,2), mode='replicate')
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w = F.pad(w, (0,2,2,1,3,4), mode='constant', value=1.3)
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w = F.pad(w, (2,2,2,2,2,2))
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return x, y, z, w
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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, 16)
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y = torch.rand(12, 2, 16)
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z = torch.rand(1, 3, 12, 16)
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w = torch.rand(1, 5, 7, 9, 11)
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a0, a1, a2, a3 = net(x, y, z, w)
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# export torchscript
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mod = torch.jit.trace(net, (x, y, z, w))
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mod.save("test_F_pad.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_F_pad.pt inputshape=[1,16],[12,2,16],[1,3,12,16],[1,5,7,9,11]")
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# pnnx inference
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import test_F_pad_pnnx
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b0, b1, b2, b3 = test_F_pad_pnnx.test_inference()
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return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2) and torch.equal(a3, b3)
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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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