lip_densenet.py

import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict

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

model_urls = {
    'densenet121': './lip_densenet_121.pth',
}

BOTTLENECK_WIDTH = 128
COEFF = 12.0


def lip2d(x, logit, kernel=3, stride=2, padding=1):
    weight = logit.exp()
    return F.avg_pool2d(x*weight, kernel, stride, padding)/F.avg_pool2d(weight, kernel, stride, padding)


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class SoftGate(nn.Module):
    def forward(self, x):
        return torch.sigmoid(x).mul(COEFF)


class BottleneckLIP(nn.Module):
    def __init__(self, channels):
        super(BottleneckLIP, self).__init__()

        rp = BOTTLENECK_WIDTH

        self.logit = nn.Sequential(
            OrderedDict((
                ('conv1', conv1x1(channels, rp)),
                ('bn1', nn.InstanceNorm2d(rp, affine=True)),
                ('relu1', nn.ReLU(inplace=True)),
                ('conv2', conv3x3(rp, rp)),
                ('bn2', nn.InstanceNorm2d(rp, affine=True)),
                ('relu2', nn.ReLU(inplace=True)),
                ('conv3', conv1x1(rp, channels)),
                ('bn3', nn.InstanceNorm2d(channels, affine=True)),
                ('gate', SoftGate()),
            ))
        )

    def init_layer(self):
        self.logit[6].weight.data.fill_(0)

    def forward(self, x):
        frac = lip2d(x, self.logit(x), kernel=2, stride=2, padding=0)
        return frac


class SimplifiedLIP(nn.Module):
    def __init__(self, channels):
        super(SimplifiedLIP, self).__init__()

        self.logit = nn.Sequential(
            OrderedDict((
                ('conv1', conv3x3(channels, channels)),
                ('bn1', nn.InstanceNorm2d(channels, affine=True)),
                ('gate', SoftGate()),
            ))
        )

    def init_layer(self):
        self.logit[0].weight.data.fill_(0)

    def forward(self, x):
        frac = lip2d(x, self.logit(x), kernel=3, stride=2, padding=1)
        return frac


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(inplace=True)),
        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(inplace=True)),
        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 > 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(inplace=True))
        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))
        self.add_module('pool', BottleneckLIP(num_output_features))


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

    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(inplace=True)),
            #('pool0', nn.MaxPool2d(3, 2, 1)),
            ('pool0', SimplifiedLIP(num_init_features)),
        ]))

        # 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))

        # 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)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
        out = self.classifier(out)
        return out


def densenet121(pretrained=False, **kwargs):
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                     **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(
            model_urls['densenet121'], map_location='cpu'))
    return model