77 lines
2.7 KiB
Python
77 lines
2.7 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.rnn_0_0 = nn.RNN(input_size=32, hidden_size=16)
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self.rnn_0_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False)
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self.rnn_0_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='relu', bias=True, bidirectional=True)
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self.rnn_0_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True, bidirectional=True)
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self.rnn_1_0 = nn.RNN(input_size=25, hidden_size=16, batch_first=True)
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self.rnn_1_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False, batch_first=True)
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self.rnn_1_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='relu', bias=True, batch_first=True, bidirectional=True)
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self.rnn_1_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True, batch_first=True, bidirectional=True)
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def forward(self, x, y):
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x0, h0 = self.rnn_0_0(x)
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x1, h1 = self.rnn_0_1(x0)
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x2, h2 = self.rnn_0_2(x1)
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x3, h3 = self.rnn_0_3(x1, h2)
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y0, h4 = self.rnn_1_0(y)
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y1, h5 = self.rnn_1_1(y0)
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y2, h6 = self.rnn_1_2(y1)
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y3, h7 = self.rnn_1_3(y1, h6)
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return x2, x3, h0, h1, h2, h3, y2, y3, h4, h5, h6, h7
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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(10, 1, 32)
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y = torch.rand(1, 12, 25)
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a = net(x, y)
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# export torchscript
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mod = torch.jit.trace(net, (x, y))
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mod.save("test_nn_RNN.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_nn_RNN.pt inputshape=[10,1,32],[1,12,25]")
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# pnnx inference
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import test_nn_RNN_pnnx
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b = test_nn_RNN_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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