67 lines
2.2 KiB
Python
67 lines
2.2 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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self.w2 = nn.Parameter(torch.rand(6, 12, 4, 4))
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self.b2 = nn.Parameter(torch.rand(12))
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self.w3 = nn.Parameter(torch.rand(12, 2, 3, 3))
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def forward(self, x, w0, w1, b1, y):
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x = F.conv_transpose2d(x, w0, None, stride=(2,2), padding=(1,1), output_padding=(1,1))
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x = F.conv_transpose2d(x, w1, b1, stride=(1,2), padding=(2,1), dilation=(2,1), groups=2)
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y = F.conv_transpose2d(y, self.w2, self.b2, stride=(2,2), padding=(1,1), output_padding=(1,1))
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y = F.conv_transpose2d(y, self.w3, None, stride=(1,2), padding=(2,1), dilation=(2,1), groups=3)
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return x, y
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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, 12, 22, 32)
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w0 = torch.rand(12, 16, 3, 3)
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w1 = torch.rand(16, 8, 5, 5)
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b1 = torch.rand(16)
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y = torch.rand(1, 6, 5, 6)
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a0, a1 = net(x, w0, w1, b1, y)
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# export torchscript
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mod = torch.jit.trace(net, (x, w0, w1, b1, y))
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mod.save("test_F_conv_transpose2d.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_F_conv_transpose2d.pt inputshape=[1,12,22,32],[12,16,3,3],[16,8,5,5],[16],[1,6,5,6]")
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# pnnx inference
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import test_F_conv_transpose2d_pnnx
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b0, b1 = test_F_conv_transpose2d_pnnx.test_inference()
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return torch.equal(a0, b0) and torch.equal(a1, b1)
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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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