File size: 3,125 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # 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.m3 = torch.rand(12)
self.v3 = torch.rand(12)
self.w3 = nn.Parameter(torch.rand(12))
self.b3 = nn.Parameter(torch.rand(12))
self.m4 = torch.rand(3)
self.v4 = torch.rand(3)
self.w4 = nn.Parameter(torch.rand(3))
self.b4 = nn.Parameter(torch.rand(3))
self.m5 = torch.rand(10)
self.v5 = torch.rand(10)
self.w5 = nn.Parameter(torch.rand(10))
self.b5 = nn.Parameter(torch.rand(10))
def forward(self, x, y, z, m0, v0, w0, b0, m1, v1, w1, b1, m2, v2, w2, b2):
x = F.instance_norm(x, m0, v0, w0, b0)
x = F.instance_norm(x, m0, v0, None, None)
x = F.instance_norm(x, self.m3, self.v3, self.w3, self.b3)
y = F.instance_norm(y, m1, v1, w1, b1, eps=1e-3)
y = F.instance_norm(y, m1, v1, None, None)
y = F.instance_norm(y, self.m4, self.v4, self.w4, self.b4)
z = F.instance_norm(z, m2, v2, w2, b2)
z = F.instance_norm(z, m2, v2, None, None, eps=1e-2)
z = F.instance_norm(z, self.m5, self.v5, self.w5, self.b5)
return x, y, z
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 12, 24)
y = torch.rand(2, 3, 12, 16)
z = torch.rand(1, 10, 12, 16, 24)
m0 = torch.rand(12)
v0 = torch.rand(12)
w0 = torch.rand(12)
b0 = torch.rand(12)
m1 = torch.rand(3)
v1 = torch.rand(3)
w1 = torch.rand(3)
b1 = torch.rand(3)
m2 = torch.rand(10)
v2 = torch.rand(10)
w2 = torch.rand(10)
b2 = torch.rand(10)
a0, a1, a2 = net(x, y, z, m0, v0, w0, b0, m1, v1, w1, b1, m2, v2, w2, b2)
# export torchscript
mod = torch.jit.trace(net, (x, y, z, m0, v0, w0, b0, m1, v1, w1, b1, m2, v2, w2, b2))
mod.save("test_F_instance_norm.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_F_instance_norm.pt inputshape=[1,12,24],[2,3,12,16],[1,10,12,16,24],[12],[12],[12],[12],[3],[3],[3],[3],[10],[10],[10],[10]")
# pnnx inference
import test_F_instance_norm_pnnx
b0, b1, b2 = test_F_instance_norm_pnnx.test_inference()
return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2)
if __name__ == "__main__":
if test():
exit(0)
else:
exit(1)
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