本文介绍CondenseNet V2模型的实现，该模型基于密集连接，针对DenseNet和CondenseNet特征复用问题，引入稀疏特征重激活，对冗余特征裁剪与更新，提升复用效率。文中给出基于Paddle的代码实现，包括各组件及预设模型，并测试了模型输出，还列出不同模型在ImageNet-1k上的精度表现。

引入

最近各种 Transformer 的视觉模型层出不穷，偶然看到一些新的 CNN 模型居然有一丝小兴奋这次就来大致实现一下 CPVR 2024 新鲜出炉的新模型 —— CondenseNet V2

相关资料

论文：CondenseNet V2: Sparse Feature Reactivation for Deep Networks最新实现：jianghaojun/CondenseNetV2参考文章：CVPR2024 | 密集连接网络中的稀疏特征重激活

论文概述

本文提出了一种基于密集连接的高效轻量级神经网络。针对 DenseNet 的特征复用冗余，CondenseNet 提出利用可学习分组卷积来裁剪掉冗余连接。然而，DenseNet 的和 CondenseNet 中特征一旦产生将不再发生任何更改，这就导致了部分特征的潜在价值被严重忽略。本文提出：与其直接删掉冗余，不妨给冗余特征一个“翻身”机会。因此我们提出一种可学习的稀疏特征重激活的方法，来有选择地更新冗余特征，从而增强特征的复用效率。CondenseNet V2 在 CondenseNet 的基础上引入了稀疏特征重激活，对冗余特征同时进行了裁剪和更新，有效提升了密集连接网络的特征复用效率，在图像分类和检测任务上取得的出色表现。更多详情请看上面的参考文章的内容

代码实现

最新实现只提供了转换后的标准分组卷积的模型参数，大致的转换流程如下图：

所以为了兼容这些模型参数，代码也是转换后的模型代码，适合于微调使用完整的模型训练代码之后应该会再更新的In [1]

!pip install ppim

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import paddleimport paddle.nn as nnimport paddle.vision.transforms as Tfrom ppim.models.common import kaiming_normal_, zeros_, ones_class SELayer(nn.Layer):    def __init__(self, inplanes, reduction=16):        super(SELayer, self).__init__()        self.avg_pool = nn.AdaptiveAvgPool2D(1)        self.fc = nn.Sequential(            nn.Linear(inplanes, inplanes // reduction, bias_attr=False),            nn.ReLU(),            nn.Linear(inplanes // reduction, inplanes, bias_attr=False),            nn.Sigmoid()        )    def forward(self, x):        b, c, _, _ = x.shape        y = self.avg_pool(x).reshape((b, c))        y = self.fc(y).reshape((b, c, 1, 1))        return x * y.expand_as(x)class HS(nn.Layer):    def __init__(self):        super(HS, self).__init__()        self.relu6 = nn.ReLU6()    def forward(self, inputs):        return inputs * self.relu6(inputs + 3) / 6class Conv(nn.Sequential):    def __init__(self, in_channels, out_channels, kernel_size,                 stride=1, padding=0, groups=1, activation='ReLU', bn_momentum=0.9):        super(Conv, self).__init__()        self.add_sublayer('norm', nn.BatchNorm2D(            in_channels, momentum=bn_momentum))        if activation == 'ReLU':            self.add_sublayer('activation', nn.ReLU())        elif activation == 'HS':            self.add_sublayer('activation', HS())        else:            raise NotImplementedError        self.add_sublayer('conv', nn.Conv2D(in_channels, out_channels,                                            kernel_size=kernel_size,                                            stride=stride,                                            padding=padding, bias_attr=False,                                            groups=groups))def ShuffleLayer(x, groups):    batchsize, num_channels, height, width = x.shape    channels_per_group = num_channels // groups    # reshape    x = x.reshape((batchsize, groups, channels_per_group, height, width))    # transpose    x = x.transpose((0, 2, 1, 3, 4))    # reshape    x = x.reshape((batchsize, -1, height, width))    return xdef ShuffleLayerTrans(x, groups):    batchsize, num_channels, height, width = x.shape    channels_per_group = num_channels // groups    # reshape    x = x.reshape((batchsize, channels_per_group, groups, height, width))    # transpose    x = x.transpose((0, 2, 1, 3, 4))    # reshape    x = x.reshape((batchsize, -1, height, width))    return xclass CondenseLGC(nn.Layer):    def __init__(self, in_channels, out_channels, kernel_size,                 stride=1, padding=0, groups=1, activation='ReLU'):        super(CondenseLGC, self).__init__()        self.in_channels = in_channels        self.out_channels = out_channels        self.groups = groups        self.norm = nn.BatchNorm2D(self.in_channels)        if activation == 'ReLU':            self.activation = nn.ReLU()        elif activation == 'HS':            self.activation = HS()        else:            raise NotImplementedError        self.conv = nn.Conv2D(self.in_channels, self.out_channels,                              kernel_size=kernel_size,                              stride=stride,                              padding=padding,                              groups=self.groups,                              bias_attr=False)        self.register_buffer('index', paddle.zeros(            (self.in_channels,), dtype='int64'))    def forward(self, x):        x = paddle.index_select(x, self.index, axis=1)        x = self.norm(x)        x = self.activation(x)        x = self.conv(x)        x = ShuffleLayer(x, self.groups)        return xclass CondenseSFR(nn.Layer):    def __init__(self, in_channels, out_channels, kernel_size,                 stride=1, padding=0, groups=1, activation='ReLU'):        super(CondenseSFR, self).__init__()        self.in_channels = in_channels        self.out_channels = out_channels        self.groups = groups        self.norm = nn.BatchNorm2D(self.in_channels)        if activation == 'ReLU':            self.activation = nn.ReLU()        elif activation == 'HS':            self.activation = HS()        else:            raise NotImplementedError        self.conv = nn.Conv2D(self.in_channels, self.out_channels,                              kernel_size=kernel_size,                              padding=padding,                              groups=self.groups,                              bias_attr=False,                              stride=stride)        self.register_buffer('index', paddle.zeros(            (self.out_channels, self.out_channels)))    def forward(self, x):        x = self.norm(x)        x = self.activation(x)        x = ShuffleLayerTrans(x, self.groups)        x = self.conv(x)  # SIZE: N, C, H, W        N, C, H, W = x.shape        x = x.reshape((N, C, H * W))        x = x.transpose((0, 2, 1))  # SIZE: N, HW, C        # x SIZE: N, HW, C; self.index SIZE: C, C; OUTPUT SIZE: N, HW, C        x = paddle.matmul(x, self.index)        x = x.transpose((0, 2, 1))  # SIZE: N, C, HW        x = x.reshape((N, C, H, W))  # SIZE: N, C, HW        return xclass _SFR_DenseLayer(nn.Layer):    def __init__(self, in_channels, growth_rate, group_1x1, group_3x3, group_trans, bottleneck, activation, use_se=False):        super(_SFR_DenseLayer, self).__init__()        self.group_1x1 = group_1x1        self.group_3x3 = group_3x3        self.group_trans = group_trans        self.use_se = use_se        # 1x1 conv i --> b*k        self.conv_1 = CondenseLGC(in_channels, bottleneck * growth_rate,                                  kernel_size=1, groups=self.group_1x1,                                  activation=activation)        # 3x3 conv b*k --> k        self.conv_2 = Conv(bottleneck * growth_rate, growth_rate,                           kernel_size=3, padding=1, groups=self.group_3x3,                           activation=activation)        # 1x1 res conv k(8-16-32)--> i (k*l)        self.sfr = CondenseSFR(growth_rate, in_channels, kernel_size=1,                               groups=self.group_trans, activation=activation)        if self.use_se:            self.se = SELayer(inplanes=growth_rate, reduction=1)    def forward(self, x):        x_ = x        x = self.conv_1(x)        x = self.conv_2(x)        if self.use_se:            x = self.se(x)        sfr_feature = self.sfr(x)        y = x_ + sfr_feature        return paddle.concat([y, x], 1)class _SFR_DenseBlock(nn.Sequential):    def __init__(self, num_layers, in_channels, growth_rate, group_1x1,                 group_3x3, group_trans, bottleneck, activation, use_se):        super(_SFR_DenseBlock, self).__init__()        for i in range(num_layers):            layer = _SFR_DenseLayer(                in_channels + i * growth_rate, growth_rate, group_1x1, group_3x3, group_trans, bottleneck, activation, use_se)            self.add_sublayer('denselayer_%d' % (i + 1), layer)class _Transition(nn.Layer):    def __init__(self):        super(_Transition, self).__init__()        self.pool = nn.AvgPool2D(kernel_size=2, stride=2)    def forward(self, x):        x = self.pool(x)        return xclass CondenseNetV2(nn.Layer):    def __init__(self, stages, growth, HS_start_block, SE_start_block, fc_channel, group_1x1,                 group_3x3, group_trans, bottleneck, last_se_reduction, class_dim=1000):        super(CondenseNetV2, self).__init__()        self.stages = stages        self.growth = growth        self.class_dim = class_dim        self.last_se_reduction = last_se_reduction        assert len(self.stages) == len(self.growth)        self.progress = 0.0        self.init_stride = 2        self.pool_size = 7        self.features = nn.Sequential()        # Initial nChannels should be 3        self.num_features = 2 * self.growth[0]        # Dense-block 1 (224x224)        self.features.add_sublayer('init_conv', nn.Conv2D(3, self.num_features,                                                          kernel_size=3,                                                          stride=self.init_stride,                                                          padding=1,                                                          bias_attr=False))        for i in range(len(self.stages)):            activation = 'HS' if i >= HS_start_block else 'ReLU'            use_se = True if i >= SE_start_block else False            # Dense-block i            self.add_block(i, group_1x1, group_3x3, group_trans,                           bottleneck, activation, use_se)        self.fc = nn.Linear(self.num_features, fc_channel)        self.fc_act = HS()        # Classifier layer        if class_dim > 0:            self.classifier = nn.Linear(fc_channel, class_dim)        self._initialize()    def add_block(self, i, group_1x1, group_3x3, group_trans, bottleneck, activation, use_se):        # Check if ith is the last one        last = (i == len(self.stages) - 1)        block = _SFR_DenseBlock(            num_layers=self.stages[i],            in_channels=self.num_features,            growth_rate=self.growth[i],            group_1x1=group_1x1,            group_3x3=group_3x3,            group_trans=group_trans,            bottleneck=bottleneck,            activation=activation,            use_se=use_se,        )        self.features.add_sublayer('denseblock_%d' % (i + 1), block)        self.num_features += self.stages[i] * self.growth[i]        if not last:            trans = _Transition()            self.features.add_sublayer('transition_%d' % (i + 1), trans)        else:            self.features.add_sublayer('norm_last',                                       nn.BatchNorm2D(self.num_features))            self.features.add_sublayer('relu_last',                                       nn.ReLU())            self.features.add_sublayer('pool_last',                                       nn.AvgPool2D(self.pool_size))            # if useSE:            self.features.add_sublayer('se_last',                                       SELayer(self.num_features, reduction=self.last_se_reduction))    def forward(self, x):        features = self.features(x)        out = features.reshape((features.shape[0], -1))        out = self.fc(out)        out = self.fc_act(out)        if self.class_dim > 0:            out = self.classifier(out)        return out    def _initialize(self):        # initialize        for m in self.sublayers():            if isinstance(m, nn.Conv2D):                kaiming_normal_(m.weight)            elif isinstance(m, nn.BatchNorm2D):                ones_(m.weight)                zeros_(m.bias)

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  def convert_to_list(value, n, name, dtype=np.int):

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预设模型

In [3]

def cdnv2_a(pretrained=False, **kwargs):    model = CondenseNetV2(        stages=[1, 1, 4, 6, 8],        growth=[8, 8, 16, 32, 64],        HS_start_block=2,        SE_start_block=3,        fc_channel=828,        group_1x1=8,        group_3x3=8,        group_trans=8,        bottleneck=4,        last_se_reduction=16,        **kwargs    )    if pretrained:        params = paddle.load('data/data80680/cdnv2_a.pdparams')        model.set_dict(params)    return modeldef cdnv2_b(pretrained=False, **kwargs):    model = CondenseNetV2(        stages=[2, 4, 6, 8, 6],        growth=[6, 12, 24, 48, 96],        HS_start_block=2,        SE_start_block=3,        fc_channel=1024,        group_1x1=6,        group_3x3=6,        group_trans=6,        bottleneck=4,        last_se_reduction=16,        **kwargs    )    if pretrained:        params = paddle.load('data/data80680/cdnv2_b.pdparams')        model.set_dict(params)    return modeldef cdnv2_c(pretrained=False, **kwargs):    model = CondenseNetV2(        stages=[4, 6, 8, 10, 8],        growth=[8, 16, 32, 64, 128],        HS_start_block=2,        SE_start_block=3,        fc_channel=1024,        group_1x1=8,        group_3x3=8,        group_trans=8,        bottleneck=4,        last_se_reduction=16,        **kwargs    )    if pretrained:        params = paddle.load('data/data80680/cdnv2_c.pdparams')        model.set_dict(params)    return model

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模型测试

In [5]

model = cdnv2_a()out = model(paddle.randn((1, 3, 224, 224)))print(out.shape)model.eval()out = model(paddle.randn((1, 3, 224, 224)))print(out.shape)

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:648: UserWarning: When training, we now always track global mean and variance.  "When training, we now always track global mean and variance.")

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[1, 1000][1, 1000]

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精度表现

具体的模型精度表现如下（ImageNet-1k）：

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