involution：大家一起内卷起来吧

本文介绍用Layer方式搭建involution算子，以此魔改ResNet打造RedNet模型，已加入【Paddle-Image-Models】项目，含转换后的最新预训练参数，精度基本对齐。还展示了算子和模型的搭建代码、测试情况及精度验证结果，RedNet性能和效率优于ResNet等模型。

引入

相关资料

论文：【Involution: Inverting the Inherence of Convolution for Visual Recognition】

代码：【d-li14/involution】

论文概要

算子和模型搭建

导入必要的包

import paddleimport paddle.nn as nnfrom paddle.vision.models import resnet

involution（内卷）

class involution(nn.Layer):    def __init__(self,                 channels,                 kernel_size,                 stride):        super(involution, self).__init__()        self.kernel_size = kernel_size        self.stride = stride        self.channels = channels        reduction_ratio = 4        self.group_channels = 16        self.groups = self.channels // self.group_channels        self.conv1 = nn.Sequential(            ('conv', nn.Conv2D(                in_channels=channels,                out_channels=channels // reduction_ratio,                kernel_size=1,                bias_attr=False            )),            ('bn', nn.BatchNorm2D(channels // reduction_ratio)),            ('activate', nn.ReLU())        )        self.conv2 = nn.Sequential(            ('conv', nn.Conv2D(                in_channels=channels // reduction_ratio,                out_channels=kernel_size**2 * self.groups,                kernel_size=1,                stride=1))        )        if stride > 1:            self.avgpool = nn.AvgPool2D(stride, stride)    def forward(self, x):        weight = self.conv2(self.conv1(            x if self.stride == 1 else self.avgpool(x)))        b, c, h, w = weight.shape        weight = weight.reshape((            b, self.groups, self.kernel_size**2, h, w)).unsqueeze(2)        out = nn.functional.unfold(            x, self.kernel_size, strides=self.stride, paddings=(self.kernel_size-1)//2, dilations=1)        out = out.reshape(            (b, self.groups, self.group_channels, self.kernel_size**2, h, w))        out = (weight * out).sum(axis=3).reshape((b, self.channels, h, w))        return out

算子测试

inv = involution(128, 7, 1)paddle.summary(inv, (1, 128, 64, 64))out = inv(paddle.randn((1, 128, 64, 64)))print(out.shape)

--------------------------------------------------------------------------- Layer (type)       Input Shape          Output Shape         Param #    ===========================================================================   Conv2D-1      [[1, 128, 64, 64]]    [1, 32, 64, 64]         4,096      BatchNorm2D-1   [[1, 32, 64, 64]]     [1, 32, 64, 64]          128          ReLU-1       [[1, 32, 64, 64]]     [1, 32, 64, 64]           0          Conv2D-2      [[1, 32, 64, 64]]     [1, 392, 64, 64]       12,936     ===========================================================================Total params: 17,160Trainable params: 17,032Non-trainable params: 128---------------------------------------------------------------------------Input size (MB): 2.00Forward/backward pass size (MB): 15.25Params size (MB): 0.07Estimated Total Size (MB): 17.32---------------------------------------------------------------------------[1, 128, 64, 64]

/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.")

RedNet

class BottleneckBlock(resnet.BottleneckBlock):    def __init__(self,                 inplanes,                 planes,                 stride=1,                 downsample=None,                 groups=1,                 base_width=64,                 dilation=1,                 norm_layer=None):        super(BottleneckBlock, self).__init__(inplanes, planes, stride,                                              downsample, groups, base_width, dilation, norm_layer)        width = int(planes * (base_width / 64.)) * groups        self.conv2 = involution(width, 7, stride)        class RedNet(resnet.ResNet):    def __init__(self, block, depth, num_classes=1000, with_pool=True):        super(RedNet, self).__init__(block=block, depth=50,                                     num_classes=num_classes, with_pool=with_pool)        layer_cfg = {            26: [1, 2, 4, 1],            38: [2, 3, 5, 2],            50: [3, 4, 6, 3],            101: [3, 4, 23, 3],            152: [3, 8, 36, 3]        }        layers = layer_cfg[depth]        self.conv1 = None        self.bn1 = None        self.relu = None        self.inplanes = 64        self.stem = nn.Sequential(            nn.Sequential(                ('conv', nn.Conv2D(                    in_channels=3,                    out_channels=self.inplanes // 2,                    kernel_size=3,                    stride=2,                    padding=1,                    bias_attr=False                )),                ('bn', nn.BatchNorm2D(self.inplanes // 2)),                ('activate', nn.ReLU())            ),            involution(self.inplanes // 2, 3, 1),            nn.BatchNorm2D(self.inplanes // 2),            nn.ReLU(),            nn.Sequential(                ('conv', nn.Conv2D(                    in_channels=self.inplanes // 2,                    out_channels=self.inplanes,                    kernel_size=3,                    stride=1,                    padding=1,                    bias_attr=False                )),                ('bn', nn.BatchNorm2D(self.inplanes)),                ('activate', nn.ReLU())            )        )        self.layer1 = self._make_layer(block, 64, layers[0])        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)    def forward(self, x):        x = self.stem(x)        x = self.maxpool(x)        x = self.layer1(x)        x = self.layer2(x)        x = self.layer3(x)        x = self.layer4(x)        if self.with_pool:            x = self.avgpool(x)        if self.num_classes > 0:            x = paddle.flatten(x, 1)            x = self.fc(x)        return x

模型测试

model = RedNet(BottleneckBlock, 26)paddle.summary(model, (1, 3, 224, 224))out = model(paddle.randn((1, 3, 224, 224)))print(out.shape)

-------------------------------------------------------------------------------   Layer (type)         Input Shape          Output Shape         Param #    ===============================================================================     Conv2D-88       [[1, 3, 224, 224]]   [1, 32, 112, 112]         864        BatchNorm2D-71    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128            ReLU-35       [[1, 32, 112, 112]]   [1, 32, 112, 112]          0            Conv2D-89      [[1, 32, 112, 112]]    [1, 8, 112, 112]         256        BatchNorm2D-72     [[1, 8, 112, 112]]    [1, 8, 112, 112]         32             ReLU-36        [[1, 8, 112, 112]]    [1, 8, 112, 112]          0            Conv2D-90       [[1, 8, 112, 112]]   [1, 18, 112, 112]         162         involution-18    [[1, 32, 112, 112]]   [1, 32, 112, 112]          0         BatchNorm2D-73    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128            ReLU-37       [[1, 32, 112, 112]]   [1, 32, 112, 112]          0            Conv2D-91      [[1, 32, 112, 112]]   [1, 64, 112, 112]       18,432       BatchNorm2D-74    [[1, 64, 112, 112]]   [1, 64, 112, 112]         256            ReLU-38       [[1, 64, 112, 112]]   [1, 64, 112, 112]          0           MaxPool2D-1     [[1, 64, 112, 112]]    [1, 64, 56, 56]           0            Conv2D-93       [[1, 64, 56, 56]]     [1, 64, 56, 56]         4,096       BatchNorm2D-76     [[1, 64, 56, 56]]     [1, 64, 56, 56]          256            ReLU-39        [[1, 256, 56, 56]]    [1, 256, 56, 56]          0            Conv2D-96       [[1, 64, 56, 56]]     [1, 16, 56, 56]         1,024       BatchNorm2D-79     [[1, 16, 56, 56]]     [1, 16, 56, 56]          64             ReLU-40        [[1, 16, 56, 56]]     [1, 16, 56, 56]           0            Conv2D-97       [[1, 16, 56, 56]]     [1, 196, 56, 56]        3,332        involution-19     [[1, 64, 56, 56]]     [1, 64, 56, 56]           0         BatchNorm2D-77     [[1, 64, 56, 56]]     [1, 64, 56, 56]          256           Conv2D-95       [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384       BatchNorm2D-78     [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024          Conv2D-92       [[1, 64, 56, 56]]     [1, 256, 56, 56]       16,384       BatchNorm2D-75     [[1, 256, 56, 56]]    [1, 256, 56, 56]        1,024     BottleneckBlock-17   [[1, 64, 56, 56]]     [1, 256, 56, 56]          0            Conv2D-99       [[1, 256, 56, 56]]    [1, 128, 56, 56]       32,768       BatchNorm2D-81     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512            ReLU-41        [[1, 512, 28, 28]]    [1, 512, 28, 28]          0           AvgPool2D-4      [[1, 128, 56, 56]]    [1, 128, 28, 28]          0           Conv2D-102       [[1, 128, 28, 28]]    [1, 32, 28, 28]         4,096       BatchNorm2D-84     [[1, 32, 28, 28]]     [1, 32, 28, 28]          128            ReLU-42        [[1, 32, 28, 28]]     [1, 32, 28, 28]           0           Conv2D-103       [[1, 32, 28, 28]]     [1, 392, 28, 28]       12,936        involution-20     [[1, 128, 56, 56]]    [1, 128, 28, 28]          0         BatchNorm2D-82     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512          Conv2D-101       [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536       BatchNorm2D-83     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048          Conv2D-98       [[1, 256, 56, 56]]    [1, 512, 28, 28]       131,072      BatchNorm2D-80     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     BottleneckBlock-18   [[1, 256, 56, 56]]    [1, 512, 28, 28]          0           Conv2D-104       [[1, 512, 28, 28]]    [1, 128, 28, 28]       65,536       BatchNorm2D-85     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512            ReLU-43        [[1, 512, 28, 28]]    [1, 512, 28, 28]          0           Conv2D-107       [[1, 128, 28, 28]]    [1, 32, 28, 28]         4,096       BatchNorm2D-88     [[1, 32, 28, 28]]     [1, 32, 28, 28]          128            ReLU-44        [[1, 32, 28, 28]]     [1, 32, 28, 28]           0           Conv2D-108       [[1, 32, 28, 28]]     [1, 392, 28, 28]       12,936        involution-21     [[1, 128, 28, 28]]    [1, 128, 28, 28]          0         BatchNorm2D-86     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512          Conv2D-106       [[1, 128, 28, 28]]    [1, 512, 28, 28]       65,536       BatchNorm2D-87     [[1, 512, 28, 28]]    [1, 512, 28, 28]        2,048     BottleneckBlock-19   [[1, 512, 28, 28]]    [1, 512, 28, 28]          0           Conv2D-110       [[1, 512, 28, 28]]    [1, 256, 28, 28]       131,072      BatchNorm2D-90     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024           ReLU-45       [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           AvgPool2D-5      [[1, 256, 28, 28]]    [1, 256, 14, 14]          0           Conv2D-113       [[1, 256, 14, 14]]    [1, 64, 14, 14]        16,384       BatchNorm2D-93     [[1, 64, 14, 14]]     [1, 64, 14, 14]          256            ReLU-46        [[1, 64, 14, 14]]     [1, 64, 14, 14]           0           Conv2D-114       [[1, 64, 14, 14]]     [1, 784, 14, 14]       50,960        involution-22     [[1, 256, 28, 28]]    [1, 256, 14, 14]          0         BatchNorm2D-91     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024         Conv2D-112       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144      BatchNorm2D-92    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096         Conv2D-109       [[1, 512, 28, 28]]   [1, 1024, 14, 14]       524,288      BatchNorm2D-89    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     BottleneckBlock-20   [[1, 512, 28, 28]]   [1, 1024, 14, 14]          0           Conv2D-115      [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144      BatchNorm2D-94     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024           ReLU-47       [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-118       [[1, 256, 14, 14]]    [1, 64, 14, 14]        16,384       BatchNorm2D-97     [[1, 64, 14, 14]]     [1, 64, 14, 14]          256            ReLU-48        [[1, 64, 14, 14]]     [1, 64, 14, 14]           0           Conv2D-119       [[1, 64, 14, 14]]     [1, 784, 14, 14]       50,960        involution-23     [[1, 256, 14, 14]]    [1, 256, 14, 14]          0         BatchNorm2D-95     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024         Conv2D-117       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144      BatchNorm2D-96    [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     BottleneckBlock-21  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-120      [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144      BatchNorm2D-98     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024           ReLU-49       [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-123       [[1, 256, 14, 14]]    [1, 64, 14, 14]        16,384       BatchNorm2D-101    [[1, 64, 14, 14]]     [1, 64, 14, 14]          256            ReLU-50        [[1, 64, 14, 14]]     [1, 64, 14, 14]           0           Conv2D-124       [[1, 64, 14, 14]]     [1, 784, 14, 14]       50,960        involution-24     [[1, 256, 14, 14]]    [1, 256, 14, 14]          0         BatchNorm2D-99     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024         Conv2D-122       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144      BatchNorm2D-100   [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     BottleneckBlock-22  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-125      [[1, 1024, 14, 14]]    [1, 256, 14, 14]       262,144      BatchNorm2D-102    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024           ReLU-51       [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-128       [[1, 256, 14, 14]]    [1, 64, 14, 14]        16,384       BatchNorm2D-105    [[1, 64, 14, 14]]     [1, 64, 14, 14]          256            ReLU-52        [[1, 64, 14, 14]]     [1, 64, 14, 14]           0           Conv2D-129       [[1, 64, 14, 14]]     [1, 784, 14, 14]       50,960        involution-25     [[1, 256, 14, 14]]    [1, 256, 14, 14]          0         BatchNorm2D-103    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024         Conv2D-127       [[1, 256, 14, 14]]   [1, 1024, 14, 14]       262,144      BatchNorm2D-104   [[1, 1024, 14, 14]]   [1, 1024, 14, 14]        4,096     BottleneckBlock-23  [[1, 1024, 14, 14]]   [1, 1024, 14, 14]          0           Conv2D-131      [[1, 1024, 14, 14]]    [1, 512, 14, 14]       524,288      BatchNorm2D-107    [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048           ReLU-53        [[1, 2048, 7, 7]]     [1, 2048, 7, 7]           0           AvgPool2D-6      [[1, 512, 14, 14]]     [1, 512, 7, 7]           0           Conv2D-134        [[1, 512, 7, 7]]      [1, 128, 7, 7]        65,536       BatchNorm2D-110     [[1, 128, 7, 7]]      [1, 128, 7, 7]          512            ReLU-54         [[1, 128, 7, 7]]      [1, 128, 7, 7]           0           Conv2D-135        [[1, 128, 7, 7]]     [1, 1568, 7, 7]        202,272       involution-26     [[1, 512, 14, 14]]     [1, 512, 7, 7]           0         BatchNorm2D-108     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048         Conv2D-133        [[1, 512, 7, 7]]     [1, 2048, 7, 7]       1,048,576     BatchNorm2D-109    [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192         Conv2D-130      [[1, 1024, 14, 14]]    [1, 2048, 7, 7]       2,097,152     BatchNorm2D-106    [[1, 2048, 7, 7]]     [1, 2048, 7, 7]         8,192     BottleneckBlock-24  [[1, 1024, 14, 14]]    [1, 2048, 7, 7]           0       AdaptiveAvgPool2D-1  [[1, 2048, 7, 7]]     [1, 2048, 1, 1]           0            Linear-1           [[1, 2048]]           [1, 1000]          2,049,000   ===============================================================================Total params: 9,264,318Trainable params: 9,202,014Non-trainable params: 62,304-------------------------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 188.62Params size (MB): 35.34Estimated Total Size (MB): 224.53-------------------------------------------------------------------------------[1, 1000]

模型精度验证

安装 PPIM

!pip install ppim==1.0.1 -i https://pypi.python.org/pypi

解压数据集

# 解压数据集!mkdir ~/data/ILSVRC2012!tar -xf ~/data/data68594/ILSVRC2012_img_val.tar -C ~/data/ILSVRC2012

模型评估

import osimport cv2import numpy as npimport paddleimport paddle.vision.transforms as Tfrom ppim import rednet26, rednet38, rednet50, rednet101, rednet152# 构建数据集# backend cv2class ILSVRC2012(paddle.io.Dataset):    def __init__(self, root, label_list, transform):        self.transform = transform        self.root = root        self.label_list = label_list        self.load_datas()    def load_datas(self):        self.imgs = []        self.labels = []        with open(self.label_list, 'r') as f:            for line in f:                img, label = line[:-1].split(' ')                self.imgs.append(os.path.join(self.root, img))                self.labels.append(int(label))    def __getitem__(self, idx):        label = self.labels[idx]        image = self.imgs[idx]        image = cv2.imread(image)        image = self.transform(image)        return image.astype('float32'), np.array(label).astype('int64')    def __len__(self):        return len(self.imgs)# 配置模型model, val_transforms = rednet26(pretrained=True)model = paddle.Model(model)model.prepare(metrics=paddle.metric.Accuracy(topk=(1, 5)))# 配置数据集val_dataset = ILSVRC2012('data/ILSVRC2012', transform=val_transforms, label_list='data/data68594/val_list.txt')# 模型验证model.evaluate(val_dataset, batch_size=16)

{'acc_top1': 0.75956, 'acc_top5': 0.9319}

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热点：involution：大家一起内卷起来吧要求：
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2. 再总结它为什么重要
3. 说明会影响哪些 AI 产品或内容方向
4. 最后给出 3 个适合资讯站使用的标题