74 lines
2.9 KiB
Python
74 lines
2.9 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__()
|
||
|
|
|
||
|
|
def forward(self, x, y):
|
||
|
|
x = F.max_pool1d(x, kernel_size=3)
|
||
|
|
x = F.max_pool1d(x, kernel_size=4, stride=2, padding=2, dilation=1)
|
||
|
|
x = F.max_pool1d(x, kernel_size=3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
|
||
|
|
x = F.max_pool1d(x, kernel_size=5, stride=2, padding=2, dilation=1, return_indices=False, ceil_mode=True)
|
||
|
|
x = F.max_pool1d(x, kernel_size=3, stride=1, padding=1, dilation=2, return_indices=False, ceil_mode=False)
|
||
|
|
x = F.max_pool1d(x, kernel_size=2, stride=1, padding=0, dilation=1, return_indices=False, ceil_mode=True)
|
||
|
|
x, indices1 = F.max_pool1d(x, kernel_size=2, padding=1, dilation=1, return_indices=True, ceil_mode=False)
|
||
|
|
x, indices2 = F.max_pool1d(x, kernel_size=5, stride=1, padding=2, dilation=1, return_indices=True, ceil_mode=True)
|
||
|
|
|
||
|
|
y = F.max_pool1d(y, kernel_size=3)
|
||
|
|
y = F.max_pool1d(y, kernel_size=4, stride=2, padding=2, dilation=1)
|
||
|
|
y = F.max_pool1d(y, kernel_size=3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
|
||
|
|
y = F.max_pool1d(y, kernel_size=5, stride=2, padding=2, dilation=1, return_indices=False, ceil_mode=True)
|
||
|
|
y = F.max_pool1d(y, kernel_size=3, stride=1, padding=1, dilation=2, return_indices=False, ceil_mode=False)
|
||
|
|
y = F.max_pool1d(y, kernel_size=2, stride=1, padding=0, dilation=1, return_indices=False, ceil_mode=True)
|
||
|
|
|
||
|
|
return x, indices1, indices2, y
|
||
|
|
|
||
|
|
def test():
|
||
|
|
net = Model()
|
||
|
|
net.eval()
|
||
|
|
|
||
|
|
torch.manual_seed(0)
|
||
|
|
x = torch.rand(1, 12, 128)
|
||
|
|
y = torch.rand(12, 128)
|
||
|
|
|
||
|
|
a = net(x, y)
|
||
|
|
|
||
|
|
# export torchscript
|
||
|
|
mod = torch.jit.trace(net, (x, y))
|
||
|
|
mod.save("test_F_max_pool1d.pt")
|
||
|
|
|
||
|
|
# torchscript to pnnx
|
||
|
|
import os
|
||
|
|
os.system("../src/pnnx test_F_max_pool1d.pt inputshape=[1,12,128],[12,128]")
|
||
|
|
|
||
|
|
# pnnx inference
|
||
|
|
import test_F_max_pool1d_pnnx
|
||
|
|
b = test_F_max_pool1d_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)
|