77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
|
# Tencent is pleased to support the open source community by making ncnn available.
|
||
|
|
#
|
||
|
|
# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
|
||
|
|
# in compliance with the License. You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# https://opensource.org/licenses/BSD-3-Clause
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software distributed
|
||
|
|
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
|
||
|
|
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||
|
|
# specific language governing permissions and limitations under the License.
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
class Model(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super(Model, self).__init__()
|
||
|
|
|
||
|
|
self.gru_0_0 = nn.GRU(input_size=32, hidden_size=16)
|
||
|
|
self.gru_0_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False)
|
||
|
|
self.gru_0_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
|
||
|
|
self.gru_0_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
|
||
|
|
|
||
|
|
self.gru_1_0 = nn.GRU(input_size=25, hidden_size=16, batch_first=True)
|
||
|
|
self.gru_1_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
|
||
|
|
self.gru_1_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
|
||
|
|
self.gru_1_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
|
||
|
|
|
||
|
|
def forward(self, x, y):
|
||
|
|
x0, h0 = self.gru_0_0(x)
|
||
|
|
x1, h1 = self.gru_0_1(x0)
|
||
|
|
x2, h2 = self.gru_0_2(x1)
|
||
|
|
x3, h3 = self.gru_0_3(x1, h2)
|
||
|
|
|
||
|
|
y0, h4 = self.gru_1_0(y)
|
||
|
|
y1, h5 = self.gru_1_1(y0)
|
||
|
|
y2, h6 = self.gru_1_2(y1)
|
||
|
|
y3, h7 = self.gru_1_3(y1, h6)
|
||
|
|
return x2, x3, h0, h1, h2, h3, y2, y3, h4, h5, h6, h7
|
||
|
|
|
||
|
|
def test():
|
||
|
|
net = Model()
|
||
|
|
net.eval()
|
||
|
|
|
||
|
|
torch.manual_seed(0)
|
||
|
|
x = torch.rand(10, 1, 32)
|
||
|
|
y = torch.rand(1, 12, 25)
|
||
|
|
|
||
|
|
a = net(x, y)
|
||
|
|
|
||
|
|
# export torchscript
|
||
|
|
mod = torch.jit.trace(net, (x, y))
|
||
|
|
mod.save("test_nn_GRU.pt")
|
||
|
|
|
||
|
|
# torchscript to pnnx
|
||
|
|
import os
|
||
|
|
os.system("../src/pnnx test_nn_GRU.pt inputshape=[10,1,32],[1,12,25]")
|
||
|
|
|
||
|
|
# pnnx inference
|
||
|
|
import test_nn_GRU_pnnx
|
||
|
|
b = test_nn_GRU_pnnx.test_inference()
|
||
|
|
|
||
|
|
for a0, b0 in zip(a, b):
|
||
|
|
if not torch.equal(a0, b0):
|
||
|
|
return False
|
||
|
|
return True
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
if test():
|
||
|
|
exit(0)
|
||
|
|
else:
|
||
|
|
exit(1)
|