How to add spectral norm to densenet?

Naruto-Sasuke: <p>I want to add SN to densenet (the pretrained-one).<br>
I copy the script from torchvison here,</p>
<pre><code class="lang-python">import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict

__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']


model_urls = {
    'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
}


def densenet121(pretrained=False, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                     **kwargs)
    if pretrained:
        # '.'s are no longer allowed in module names, but pervious _DenseLayer
        # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
        # They are also in the checkpoints in model_urls. This pattern is used
        # to find such keys.
        pattern = re.compile(
            r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
        state_dict = model_zoo.load_url(model_urls['densenet121'])
        for key in list(state_dict.keys()):
            res = pattern.match(key)
            if res:
                new_key = res.group(1) + res.group(2)
                state_dict[new_key] = state_dict[key]
                del state_dict[key]
        model.load_state_dict(state_dict, strict=False)
    return model

class _DenseLayer(nn.Sequential):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU()),
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                        growth_rate, kernel_size=1, stride=1, bias=False)),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU()),
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                        kernel_size=3, stride=1, padding=1, bias=False)),
        self.drop_rate = drop_rate

    def forward(self, x):
        new_features = super(_DenseLayer, self).forward(x)
        if self.drop_rate &gt; 0:
            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
        return torch.cat([x, new_features], 1)


class _DenseBlock(nn.Sequential):
    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
            self.add_module('denselayer%d' % (i + 1), layer)


class _Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU())
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
    """
    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):

        super(DenseNet, self).__init__()

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU()),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        self.conv_last = nn.Conv2d(num_features, 256, kernel_size=3)

        # Linear layer
        # self.classifier = nn.Linear(num_features, num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight.data)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        features = self.features(x)
        features = self.conv_last(features)
        return features

</code></pre>
<p>How to modify this script to add SN to it ? Is pytorch smart enough to load the weights to the layers if I run <code>model = densenet121(pretrained=True)</code> ?</p>

Naruto-Sasuke: <p>Have solved by myself.</p>

FloCF: <p>Hey Naruto-Sasuke,<br>
mind sharing your solution? I am having kind of the same issue…</p>

Naruto-Sasuke: <p><aside class="onebox githubcommit">
  <header class="source">
      <a href="https://github.com/Zhaoyi-Yan/Shift-Net_pytorch/commit/c539e1ad92a5df4858adf4ca6b4b342e5cf4fa2a" target="_blank" rel="nofollow noopener">github.com/Zhaoyi-Yan/Shift-Net_pytorch</a>
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    <img alt="Zhaoyi-Yan" src="https://avatars3.githubusercontent.com/u/29570378?v=4" class="thumbnail onebox-avatar" width="90" height="90">
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<h4>
  <a href="https://github.com/Zhaoyi-Yan/Shift-Net_pytorch/commit/c539e1ad92a5df4858adf4ca6b4b342e5cf4fa2a" target="_blank" rel="nofollow noopener">add SN to densenet (#60)</a>
</h4>


<div class="date">
  by <a href="https://github.com/Zhaoyi-Yan" target="_blank" rel="nofollow noopener">Zhaoyi-Yan</a>
  on <a href="https://github.com/Zhaoyi-Yan/Shift-Net_pytorch/commit/c539e1ad92a5df4858adf4ca6b4b342e5cf4fa2a" target="_blank" rel="nofollow noopener">10:40AM - 22 Feb 19 UTC</a>
</div>

<div class="github-commit-stats">
  changed <strong>3 files</strong>
  with <strong>21 additions</strong>
  and <strong>21 deletions</strong>.
</div>

  </article>
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</aside>
<br>
Here is the commit that adds SN to densenet.</p>