69 lines
2.0 KiB
Python
69 lines
2.0 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) 2022 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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self.c0 = nn.Parameter(torch.rand(12))
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self.c2 = nn.Parameter(torch.rand(48, 12))
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def forward(self, a0, a1, a2, b0, b1, b2, c1):
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a = torch.addmm(a0, a1, a2)
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b = torch.addmm(b0, b1, b2, beta=1.4, alpha=0.7)
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c = torch.addmm(self.c0, c1, self.c2, beta=1, alpha=1)
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return a, b, c
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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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a0 = torch.rand(13, 1)
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a1 = torch.rand(13, 16)
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a2 = torch.rand(16, 23)
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b0 = torch.rand(7, 33)
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b1 = torch.rand(7, 26)
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b2 = torch.rand(26, 33)
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c1 = torch.rand(16, 48)
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a = net(a0, a1, a2, b0, b1, b2, c1)
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# export torchscript
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mod = torch.jit.trace(net, (a0, a1, a2, b0, b1, b2, c1))
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mod.save("test_torch_addmm.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_torch_addmm.pt inputshape=[13,1],[13,16],[16,23],[7,33],[7,26],[26,33],[16,48]")
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# pnnx inference
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import test_torch_addmm_pnnx
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b = test_torch_addmm_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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