mirror of https://github.com/Jittor/Jittor
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Guoye Yang <498731903@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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# This model is generated by pytorch converter.
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import jittor as jt
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from jittor import nn
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__all__ = [
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'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
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'vgg19_bn', 'vgg19',
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]
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class VGG(nn.Module):
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def __init__(self, features, num_classes=1000, init_weights=True):
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super(VGG, self).__init__()
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self.features = features
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self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
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self.classifier = nn.Sequential(
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nn.Linear(512 * 7 * 7, 4096),
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nn.ReLU(),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(),
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nn.Dropout(),
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nn.Linear(4096, num_classes),
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)
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def execute(self, x):
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x = self.features(x)
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x = self.avgpool(x)
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x = jt.reshape(x, [x.shape[0],-1])
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x = self.classifier(x)
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return x
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def make_layers(cfg, batch_norm=False):
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layers = []
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in_channels = 3
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for v in cfg:
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if v == 'M':
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layers += [nn.Pool(kernel_size=2, stride=2, op="maximum")]
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else:
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conv2d = nn.Conv(in_channels, v, kernel_size=3, padding=1)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm(v), nn.ReLU()]
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else:
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layers += [conv2d, nn.ReLU()]
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in_channels = v
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return nn.Sequential(*layers)
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cfgs = {
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'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
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'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
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}
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def _vgg(arch, cfg, batch_norm, **kwargs):
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model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
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return model
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def vgg11(pretrained=False, **kwargs):
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model = _vgg('vgg11', 'A', False, **kwargs)
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if pretrained: model.load("jittorhub://vgg11.pkl")
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return model
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def vgg11_bn(pretrained=False, **kwargs):
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model = _vgg('vgg11_bn', 'A', True, **kwargs)
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if pretrained: model.load("jittorhub://vgg11_bn.pkl")
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return model
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def vgg13(pretrained=False, **kwargs):
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model = _vgg('vgg13', 'B', False, **kwargs)
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if pretrained: model.load("jittorhub://vgg13.pkl")
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return model
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def vgg13_bn(pretrained=False, **kwargs):
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model = _vgg('vgg13_bn', 'B', True, **kwargs)
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if pretrained: model.load("jittorhub://vgg13_bn.pkl")
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return model
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def vgg16(pretrained=False, **kwargs):
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model = _vgg('vgg16', 'D', False, **kwargs)
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if pretrained: model.load("jittorhub://vgg16.pkl")
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return model
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def vgg16_bn(pretrained=False, **kwargs):
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model = _vgg('vgg16_bn', 'D', True, **kwargs)
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if pretrained: model.load("jittorhub://vgg16_bn.pkl")
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return model
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def vgg19(pretrained=False, **kwargs):
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model = _vgg('vgg19', 'E', False, **kwargs)
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if pretrained: model.load("jittorhub://vgg19.pkl")
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return model
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def vgg19_bn(pretrained=False, **kwargs):
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model = _vgg('vgg19_bn', 'E', True, **kwargs)
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if pretrained: model.load("jittorhub://vgg19_bn.pkl")
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return model |