Beispiel #1
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def res2net101_26w_4s(pretrained=False, strides=(2, 2, 2, 1, 1), inter_features=False):
    model = Res2Net(Bottle2neck, [3, 4, 23, 3], strides=strides, inter_features=inter_features, baseWidth=26, scale=4)
    if pretrained:
        pretrained_state = network_utils.flex_load(model.state_dict(),
                                                   model_zoo.load_url(model_urls['res2net101_26w_4s']), verb=False)
        model.load_state_dict(pretrained_state, strict=False)
    return model
Beispiel #2
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def wide_resnet101_2(pretrained=False, strides=(2, 2, 2, 1, 1), inter_features=False):
    model = ResNet(Bottleneck, [3, 4, 23, 3], strides=strides, inter_features=inter_features, width_per_group=64*2)
    if pretrained:
        pretrained_state = network_utils.flex_load(model.state_dict(),
                                                   model_zoo.load_url(model_urls['wide_resnet101_2']), verb=False)
        model.load_state_dict(pretrained_state, strict=False)
    return model
Beispiel #3
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def resnet152(pretrained=True, strides=(2, 2, 2, 1, 1), inter_features=False):
    model = ResNet(Bottleneck, [3, 8, 36, 3], strides=strides, inter_features=inter_features)
    if pretrained:
        pretrained_state = network_utils.flex_load(model.state_dict(), model_zoo.load_url(model_urls['resnet152']),
                                                   verb=False)
        model.load_state_dict(pretrained_state, strict=False)
    return model
Beispiel #4
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def resnext50_32x4d(pretrained=False, strides=(2, 2, 2, 1, 1), inter_features=False):
    model = ResNet(Bottleneck, [3, 4, 6, 3], strides=strides, inter_features=inter_features, groups=32,
                   width_per_group=4)
    if pretrained:
        pretrained_state = network_utils.flex_load(model.state_dict(),
                                                   model_zoo.load_url(model_urls['resnext50_32x4d']), verb=False)
        model.load_state_dict(pretrained_state, strict=False)
    return model
Beispiel #5
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def resnet34(pretrained=True, strides=(2, 2, 2, 1, 1), inter_features=False):
    model = ResNet(BasicBlock, [3, 4, 6, 3],
                   strides=strides,
                   inter_features=inter_features)
    if pretrained:
        pretrained_state = network_utils.flex_load(
            model.state_dict(), model_zoo.load_url(model_urls['resnet34']))
        model.load_state_dict(pretrained_state, strict=False)
    return model