mirror of https://github.com/Jittor/Jittor
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# ***************************************************************
|
|
# Copyright (c) 2021 Jittor. All Rights Reserved.
|
|
# Maintainers:
|
|
# Wenyang Zhou <576825820@qq.com>
|
|
# Dun Liang <randonlang@gmail.com>.
|
|
#
|
|
# This file is subject to the terms and conditions defined in
|
|
# file 'LICENSE.txt', which is part of this source code package.
|
|
# ***************************************************************
|
|
# This model is generated by pytorch converter.
|
|
import jittor as jt
|
|
import jittor.nn as nn
|
|
|
|
__all__ = ['AlexNet', 'alexnet']
|
|
|
|
class AlexNet(nn.Module):
|
|
""" AlexNet model architecture.
|
|
|
|
Args:
|
|
|
|
* num_classes: Number of classes. Default: 1000.
|
|
|
|
Example::
|
|
|
|
model = jittor.models.AlexNet(500)
|
|
x = jittor.random([10,224,224,3])
|
|
y = model(x) # [10, 500]
|
|
|
|
"""
|
|
|
|
def __init__(self, num_classes=1000):
|
|
super(AlexNet, self).__init__()
|
|
self.features = nn.Sequential(
|
|
nn.Conv(3, 64, kernel_size=11, stride=4, padding=2),
|
|
nn.Relu(),
|
|
nn.Pool(kernel_size=3, stride=2, op='maximum'),
|
|
nn.Conv(64, 192, kernel_size=5, padding=2),
|
|
nn.Relu(), nn.Pool(kernel_size=3, stride=2, op='maximum'),
|
|
nn.Conv(192, 384, kernel_size=3, padding=1),
|
|
nn.Relu(),
|
|
nn.Conv(384, 256, kernel_size=3, padding=1),
|
|
nn.Relu(),
|
|
nn.Conv(256, 256, kernel_size=3, padding=1),
|
|
nn.Relu(),
|
|
nn.Pool(kernel_size=3, stride=2, op='maximum')
|
|
)
|
|
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
|
|
self.classifier = nn.Sequential(
|
|
nn.Dropout(),
|
|
nn.Linear(((256 * 6) * 6), 4096),
|
|
nn.Relu(),
|
|
nn.Dropout(),
|
|
nn.Linear(4096, 4096),
|
|
nn.Relu(),
|
|
nn.Linear(4096, num_classes)
|
|
)
|
|
|
|
def execute(self, x):
|
|
x = self.features(x)
|
|
x = self.avgpool(x)
|
|
x = jt.reshape(x, (x.shape[0], (- 1)))
|
|
x = self.classifier(x)
|
|
return x
|
|
|
|
def alexnet(pretrained=False, **kwargs):
|
|
model = AlexNet(**kwargs)
|
|
if pretrained: model.load("jittorhub://alexnet.pkl")
|
|
return model
|