【AI达人特训营】基于全卷积神经网络的图像分类复现

本文将ResNet50的全连接层替换为全卷积层构建ResNet50-FCN，在CIFAR-10数据集上训练，并与原始ResNet50对比。两者采用相同参数（100轮、lr=0.01等），结果显示ResNet50-FCN准确率更高，上升过程更平滑，抖动更小，验证了全卷积层在保留空间信息等方面的优势。

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ResNet50-FCN

论文地址：https://arxiv.org/abs/1411.4038

FCN：Fully Convolutional Networks

全卷积模型：本项目将CNN模式后面的全连接层换成卷积层，所以整个网络都是卷积层。其最后输出的是一张已经标记好的热图，而不是一个概率值。 通常的CNN网络中，在最后都会有几层全连接网络来融合特征信息，然后再对融合后的特征信息进行softmax分类，如下图所示：

假设最后一层的feature_map的大小是7x7x512，那么全连接层做的事就是用4096个7x7x512的滤波器去卷积这个最后的feature_map。所以可想而知这个参数量是很大的！！

但是全卷积网络就简单多了。FCN的做法是将最后的全连接层替换为4096个1x1x512的卷积核，所以最后得出来的就是一个二维的图像，然后再对这个二维图像进行上采样（反卷积），然后再对最后反卷积的图像的每个像素点进行softmax分类。
我们都知道卷积层后的全连接目的是将 卷积输出的二维特征图（feature map）转化成（N×1N×1）一维的一个向量因为传统的卷积神经网络的输出都是分类（一般都是一个概率值），也就是几个类别的概率甚至就是一个类别号，那么全连接层就是高度提纯的特征了，方便交给最后的分类器或者回归。
根据全连接的目的，我们完全可以利用卷积层代替全连接层，在输入端使用 M×MM×M 大小的卷积核将数据“扁平化处理”，在使用 1×11×1 卷积核对数据进行降维操作，最终卷积核的通道数即是我们预测数据的维度。这样在输入端不将数据进行扁平化处理，还可以使得图片保留其空间信息:

使用全卷积层的优点:

全卷积层能够兼容不同大小的尺寸输入。与global avg pooling类似，可以大大减少网络参数量

数据集介绍：Cifar10

链接：http://www.cs.toronto.edu/~kriz/cifar.html

CIFAR-10是一个更接近普适物体的彩色图像数据集。CIFAR-10 是由Hinton 的学生Alex Krizhevsky 和Ilya Sutskever 整理的一个用于识别普适物体的小型数据集。一共包含10 个类别的RGB彩色图片：飞机(airplane)、汽车(automobile)、鸟类(bird)、猫(cat)、鹿(deer)、狗(dog)、蛙类(frog)、马(horse)、船(ship)和卡车(truck).

每个图片的尺寸为32×3232×32，每个类别有6000个图像，数据集中一共有50000张训练图片和10000张测试图片。

代码复现

1.引入依赖包

In [1]

from __future__ import divisionfrom __future__ import print_functionimport paddleimport paddle.nn as nnfrom paddle.nn import functional as Ffrom paddle.utils.download import get_weights_path_from_urlimport pickleimport numpy as npfrom paddle import callbacksfrom paddle.vision.transforms import (    ToTensor, RandomHorizontalFlip, RandomResizedCrop, SaturationTransform, Compose,    HueTransform, BrightnessTransform, ContrastTransform, RandomCrop, Normalize, RandomRotation, Resize)from paddle.vision.datasets import Cifar10from paddle.io import DataLoaderfrom paddle.optimizer.lr import CosineAnnealingDecay, MultiStepDecay, LinearWarmupimport random

2.定义ResNet50-FCN网络

本代码参考Paddleclas实现，代码中将分类类别设定为100类

In [2]

__all__ = []model_urls = {    'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams',                 'cf548f46534aa3560945be4b95cd11c4'),    'resnet34': ('https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams',                 '8d2275cf8706028345f78ac0e1d31969'),    'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',                 'ca6f485ee1ab0492d38f323885b0ad80'),    'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams',                  '02f35f034ca3858e1e54d4036443c92d'),    'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams',                  '7ad16a2f1e7333859ff986138630fd7a'),}class BottleneckBlock(nn.Layer):    expansion = 4    def __init__(self,                 inplanes,                 planes,                 stride=1,                 downsample=None,                 groups=1,                 base_width=64,                 dilation=1,                 norm_layer=None):        super(BottleneckBlock, self).__init__()        if norm_layer is None:            norm_layer = nn.BatchNorm2D        width = int(planes * (base_width / 64.)) * groups        self.width = width        self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False)        self.bn1 = norm_layer(width)        self.conv2 = nn.Conv2D(            width,            width,            3,            padding=dilation,            stride=stride,            groups=groups,            dilation=dilation,            bias_attr=False)        self.bn2 = norm_layer(width)        self.conv3 = nn.Conv2D(            width, planes * self.expansion, 1, bias_attr=False)        self.width_2 = planes * self.expansion        self.bn3 = norm_layer(planes * self.expansion)        self.relu = nn.ReLU()        self.downsample = downsample        self.stride = stride    def forward(self, x):        identity = x        out = self.conv1(x)        out = self.bn1(out)        out = self.relu(out)        out = self.conv2(out)        out = self.bn2(out)        out = self.relu(out)        out = self.conv3(out)        out = self.bn3(out)        if self.downsample is not None:            identity = self.downsample(x)        out += identity        out = self.relu(out)        return outclass ResNet(nn.Layer):    """ResNet model from    `"Deep Residual Learning for Image Recognition" `_    Args:        Block (BasicBlock|BottleneckBlock): block module of model.        depth (int): layers of resnet, default: 50.        num_classes (int): output dim of last fc layer. If num_classes  0:            self.fc = nn.Linear(512 * block.expansion, num_classes)        self.final_conv = nn.Conv2D(512 * block.expansion, 1024, 2)        self.final_conv2 = nn.Conv2D(1024, 10, 1)    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):        norm_layer = self._norm_layer        downsample = None        previous_dilation = self.dilation        if dilate:            self.dilation *= stride            stride = 1        if stride != 1 or self.inplanes != planes * block.expansion:            downsample = nn.Sequential(                nn.Conv2D(                    self.inplanes,                    planes * block.expansion,                    1,                    stride=stride,                    bias_attr=False),                norm_layer(planes * block.expansion), )        layers = []        layers.append(            block(self.inplanes, planes, stride, downsample, 1, 64,                  previous_dilation, norm_layer))        self.inplanes = planes * block.expansion        for _ in range(1, blocks):            layers.append(block(self.inplanes, planes, norm_layer=norm_layer))        return nn.Sequential(*layers)    def forward(self, x):        x = self.conv1(x)        x = self.bn1(x)        x = self.relu(x)        # x = self.maxpool(x)        x = self.layer1(x)        x = self.layer2(x)        x = self.layer3(x)        x = self.layer4(x)        ###         # 更改为全卷积层         ###        # if self.with_pool:        #     x = self.avgpool(x)        # if self.num_classes > 0:        #     x = paddle.flatten(x, 1)        #     x = self.fc(x)        ###         # 全卷积层         ###                x = self.final_conv(x)        x = self.final_conv2(x)        x = x.reshape([-1, 10], -1)        return xdef _resnet(arch, Block, depth, pretrained, **kwargs):    model = ResNet(Block, depth, **kwargs)    if pretrained:        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(            arch)        weight_path = get_weights_path_from_url(model_urls[arch][0],                                                model_urls[arch][1])        param = paddle.load(weight_path)        model.set_dict(param)    return modeldef resnet50(pretrained=False, **kwargs):    """ResNet 50-layer model    Args:        pretrained (bool): If True, returns a model pre-trained on ImageNet    Examples:        .. code-block:: python            from paddle.vision.models import resnet50            # build model            model = resnet50()            # build model and load imagenet pretrained weight            # model = resnet50(pretrained=True)    """    return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)

   In [3]

net = resnet50()paddle.summary(net, (1,3,32,32))

W0616 14:52:04.385969  2132 gpu_context.cc:278] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0616 14:52:04.390283  2132 gpu_context.cc:306] device: 0, cuDNN Version: 7.6.

------------------------------------------------------------------------------   Layer (type)        Input Shape          Output Shape         Param #    ==============================================================================     Conv2D-1        [[1, 3, 32, 32]]     [1, 64, 16, 16]         9,408       BatchNorm2D-1     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256            ReLU-1        [[1, 64, 16, 16]]     [1, 64, 16, 16]           0            Conv2D-3       [[1, 64, 16, 16]]     [1, 64, 16, 16]         4,096       BatchNorm2D-3     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256            ReLU-2        [[1, 256, 16, 16]]    [1, 256, 16, 16]          0            Conv2D-4       [[1, 64, 16, 16]]     [1, 64, 16, 16]        36,864       BatchNorm2D-4     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256           Conv2D-5       [[1, 64, 16, 16]]     [1, 256, 16, 16]       16,384       BatchNorm2D-5     [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024          Conv2D-2       [[1, 64, 16, 16]]     [1, 256, 16, 16]       16,384       BatchNorm2D-2     [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     BottleneckBlock-1   [[1, 64, 16, 16]]     [1, 256, 16, 16]          0            Conv2D-6       [[1, 256, 16, 16]]    [1, 64, 16, 16]        16,384       BatchNorm2D-6     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256            ReLU-3        [[1, 256, 16, 16]]    [1, 256, 16, 16]          0            Conv2D-7       [[1, 64, 16, 16]]     [1, 64, 16, 16]        36,864       BatchNorm2D-7     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256           Conv2D-8       [[1, 64, 16, 16]]     [1, 256, 16, 16]       16,384       BatchNorm2D-8     [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     BottleneckBlock-2   [[1, 256, 16, 16]]    [1, 256, 16, 16]          0            Conv2D-9       [[1, 256, 16, 16]]    [1, 64, 16, 16]        16,384       BatchNorm2D-9     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256            ReLU-4        [[1, 256, 16, 16]]    [1, 256, 16, 16]          0           Conv2D-10       [[1, 64, 16, 16]]     [1, 64, 16, 16]        36,864       BatchNorm2D-10    [[1, 64, 16, 16]]     [1, 64, 16, 16]          256          Conv2D-11       [[1, 64, 16, 16]]     [1, 256, 16, 16]       16,384       BatchNorm2D-11    [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     BottleneckBlock-3   [[1, 256, 16, 16]]    [1, 256, 16, 16]          0           Conv2D-13       [[1, 256, 16, 16]]    [1, 128, 16, 16]       32,768       BatchNorm2D-13    [[1, 128, 16, 16]]    [1, 128, 16, 16]         512            ReLU-5         [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-14       [[1, 128, 16, 16]]     [1, 128, 8, 8]        147,456      BatchNorm2D-14     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512          Conv2D-15        [[1, 128, 8, 8]]      [1, 512, 8, 8]        65,536       BatchNorm2D-15     [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048         Conv2D-12       [[1, 256, 16, 16]]     [1, 512, 8, 8]        131,072      BatchNorm2D-12     [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     BottleneckBlock-4   [[1, 256, 16, 16]]     [1, 512, 8, 8]           0           Conv2D-16        [[1, 512, 8, 8]]      [1, 128, 8, 8]        65,536       BatchNorm2D-16     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512            ReLU-6         [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-17        [[1, 128, 8, 8]]      [1, 128, 8, 8]        147,456      BatchNorm2D-17     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512          Conv2D-18        [[1, 128, 8, 8]]      [1, 512, 8, 8]        65,536       BatchNorm2D-18     [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     BottleneckBlock-5    [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-19        [[1, 512, 8, 8]]      [1, 128, 8, 8]        65,536       BatchNorm2D-19     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512            ReLU-7         [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-20        [[1, 128, 8, 8]]      [1, 128, 8, 8]        147,456      BatchNorm2D-20     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512          Conv2D-21        [[1, 128, 8, 8]]      [1, 512, 8, 8]        65,536       BatchNorm2D-21     [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     BottleneckBlock-6    [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-22        [[1, 512, 8, 8]]      [1, 128, 8, 8]        65,536       BatchNorm2D-22     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512            ReLU-8         [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-23        [[1, 128, 8, 8]]      [1, 128, 8, 8]        147,456      BatchNorm2D-23     [[1, 128, 8, 8]]      [1, 128, 8, 8]          512          Conv2D-24        [[1, 128, 8, 8]]      [1, 512, 8, 8]        65,536       BatchNorm2D-24     [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     BottleneckBlock-7    [[1, 512, 8, 8]]      [1, 512, 8, 8]           0           Conv2D-26        [[1, 512, 8, 8]]      [1, 256, 8, 8]        131,072      BatchNorm2D-26     [[1, 256, 8, 8]]      [1, 256, 8, 8]         1,024           ReLU-9        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-27        [[1, 256, 8, 8]]      [1, 256, 4, 4]        589,824      BatchNorm2D-27     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-28        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-28    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096         Conv2D-25        [[1, 512, 8, 8]]     [1, 1024, 4, 4]        524,288      BatchNorm2D-25    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-8    [[1, 512, 8, 8]]     [1, 1024, 4, 4]           0           Conv2D-29       [[1, 1024, 4, 4]]      [1, 256, 4, 4]        262,144      BatchNorm2D-29     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024          ReLU-10        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-30        [[1, 256, 4, 4]]      [1, 256, 4, 4]        589,824      BatchNorm2D-30     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-31        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-31    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-9   [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-32       [[1, 1024, 4, 4]]      [1, 256, 4, 4]        262,144      BatchNorm2D-32     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024          ReLU-11        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-33        [[1, 256, 4, 4]]      [1, 256, 4, 4]        589,824      BatchNorm2D-33     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-34        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-34    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-10  [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-35       [[1, 1024, 4, 4]]      [1, 256, 4, 4]        262,144      BatchNorm2D-35     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024          ReLU-12        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-36        [[1, 256, 4, 4]]      [1, 256, 4, 4]        589,824      BatchNorm2D-36     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-37        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-37    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-11  [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-38       [[1, 1024, 4, 4]]      [1, 256, 4, 4]        262,144      BatchNorm2D-38     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024          ReLU-13        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-39        [[1, 256, 4, 4]]      [1, 256, 4, 4]        589,824      BatchNorm2D-39     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-40        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-40    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-12  [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-41       [[1, 1024, 4, 4]]      [1, 256, 4, 4]        262,144      BatchNorm2D-41     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024          ReLU-14        [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-42        [[1, 256, 4, 4]]      [1, 256, 4, 4]        589,824      BatchNorm2D-42     [[1, 256, 4, 4]]      [1, 256, 4, 4]         1,024         Conv2D-43        [[1, 256, 4, 4]]     [1, 1024, 4, 4]        262,144      BatchNorm2D-43    [[1, 1024, 4, 4]]     [1, 1024, 4, 4]         4,096     BottleneckBlock-13  [[1, 1024, 4, 4]]     [1, 1024, 4, 4]           0           Conv2D-45       [[1, 1024, 4, 4]]      [1, 512, 4, 4]        524,288      BatchNorm2D-45     [[1, 512, 4, 4]]      [1, 512, 4, 4]         2,048          ReLU-15        [[1, 2048, 2, 2]]     [1, 2048, 2, 2]           0           Conv2D-46        [[1, 512, 4, 4]]      [1, 512, 2, 2]       2,359,296     BatchNorm2D-46     [[1, 512, 2, 2]]      [1, 512, 2, 2]         2,048         Conv2D-47        [[1, 512, 2, 2]]     [1, 2048, 2, 2]       1,048,576     BatchNorm2D-47    [[1, 2048, 2, 2]]     [1, 2048, 2, 2]         8,192         Conv2D-44       [[1, 1024, 4, 4]]     [1, 2048, 2, 2]       2,097,152     BatchNorm2D-44    [[1, 2048, 2, 2]]     [1, 2048, 2, 2]         8,192     BottleneckBlock-14  [[1, 1024, 4, 4]]     [1, 2048, 2, 2]           0           Conv2D-48       [[1, 2048, 2, 2]]      [1, 512, 2, 2]       1,048,576     BatchNorm2D-48     [[1, 512, 2, 2]]      [1, 512, 2, 2]         2,048          ReLU-16        [[1, 2048, 2, 2]]     [1, 2048, 2, 2]           0           Conv2D-49        [[1, 512, 2, 2]]      [1, 512, 2, 2]       2,359,296     BatchNorm2D-49     [[1, 512, 2, 2]]      [1, 512, 2, 2]         2,048         Conv2D-50        [[1, 512, 2, 2]]     [1, 2048, 2, 2]       1,048,576     BatchNorm2D-50    [[1, 2048, 2, 2]]     [1, 2048, 2, 2]         8,192     BottleneckBlock-15  [[1, 2048, 2, 2]]     [1, 2048, 2, 2]           0           Conv2D-51       [[1, 2048, 2, 2]]      [1, 512, 2, 2]       1,048,576     BatchNorm2D-51     [[1, 512, 2, 2]]      [1, 512, 2, 2]         2,048          ReLU-17        [[1, 2048, 2, 2]]     [1, 2048, 2, 2]           0           Conv2D-52        [[1, 512, 2, 2]]      [1, 512, 2, 2]       2,359,296     BatchNorm2D-52     [[1, 512, 2, 2]]      [1, 512, 2, 2]         2,048         Conv2D-53        [[1, 512, 2, 2]]     [1, 2048, 2, 2]       1,048,576     BatchNorm2D-53    [[1, 2048, 2, 2]]     [1, 2048, 2, 2]         8,192     BottleneckBlock-16  [[1, 2048, 2, 2]]     [1, 2048, 2, 2]           0           Conv2D-54       [[1, 2048, 2, 2]]     [1, 1024, 1, 1]       8,389,632       Conv2D-55       [[1, 1024, 1, 1]]      [1, 10, 1, 1]         10,250     ==============================================================================Total params: 31,961,034Trainable params: 31,854,794Non-trainable params: 106,240------------------------------------------------------------------------------Input size (MB): 0.01Forward/backward pass size (MB): 20.10Params size (MB): 121.92Estimated Total Size (MB): 142.04------------------------------------------------------------------------------

{'total_params': 31961034, 'trainable_params': 31854794}

3.自定义数据集处理方式

In [4]

class ToArray(object):    def __call__(self, img):        img = np.array(img)        img = np.transpose(img, [2, 0, 1])        img = img / 255.        return img.astype('float32')class RandomApply(object):    def __init__(self, transform, p=0.5):        super().__init__()        self.p = p        self.transform = transform            def __call__(self, img):        if self.p = self.model._optimizer._learning_rate.warmup_steps:                self.warm_up = Falsedef _on_train_batch_end(self, step, logs=None):    logs = logs or {}    logs['lr'] = self.model._optimizer.get_lr()    self.train_step += 1    if self._is_write():        self._updates(logs, 'train')def _on_train_begin(self, logs=None):    self.epochs = self.params['epochs']    assert self.epochs    self.train_metrics = self.params['metrics'] + ['lr']    assert self.train_metrics    self._is_fit = True    self.train_step = 0callbacks.VisualDL.on_train_batch_end = _on_train_batch_endcallbacks.VisualDL.on_train_begin = _on_train_begin

4.在Cifar10数据集上训练模型

使用Paddle自带的Cifar10数据集API加载

In [ ]

model = paddle.Model(resnet50())# 加载checkpoint# model.load('output/ResNet50-FCN/299.pdparams')MAX_EPOCH = 300LR = 0.01WEIGHT_DECAY = 5e-4MOMENTUM = 0.9BATCH_SIZE = 256CIFAR_MEAN = [0.5071, 0.4865, 0.4409]CIFAR_STD = [0.1942, 0.1918, 0.1958]DATA_FILE = Nonemodel.prepare(    paddle.optimizer.Momentum(        learning_rate=LinearWarmup(CosineAnnealingDecay(LR, MAX_EPOCH), 2000, 0., LR),        momentum=MOMENTUM,        parameters=model.parameters(),        weight_decay=WEIGHT_DECAY),    paddle.nn.CrossEntropyLoss(),    paddle.metric.Accuracy(topk=(1,5)))# 定义数据集增强方式transforms = Compose([    RandomCrop(32, padding=4),    RandomApply(BrightnessTransform(0.1)),    RandomApply(ContrastTransform(0.1)),    RandomHorizontalFlip(),    RandomRotation(15),    ToArray(),    Normalize(CIFAR_MEAN, CIFAR_STD),    # Resize(size=224)])val_transforms = Compose([ToArray(), Normalize(CIFAR_MEAN, CIFAR_STD)])# 加载训练和测试数据集train_set = Cifar10(DATA_FILE, mode='train', transform=transforms)test_set = Cifar10(DATA_FILE, mode='test', transform=val_transforms)# 定义保存方式和训练可视化checkpoint_callback = paddle.callbacks.ModelCheckpoint(save_freq=1, save_dir='output/ResNet50-FCN')callbacks = [LRSchedulerM(),checkpoint_callback, callbacks.VisualDL('vis_logs/resnet50_FCN.log')]# 训练模型model.fit(    train_set,    test_set,    epochs=MAX_EPOCH,     batch_size=BATCH_SIZE,    shuffle=True,    num_workers=4,    verbose=1,     callbacks=callbacks,)

对照实验：原始ResNet50

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model = paddle.Model(paddle.vision.models.resnet50(pretrained=False))# 加载checkpoint# model.load('output/ResNet50/299.pdparams')MAX_EPOCH = 300LR = 0.01WEIGHT_DECAY = 5e-4MOMENTUM = 0.9BATCH_SIZE = 256CIFAR_MEAN = [0.5071, 0.4865, 0.4409]CIFAR_STD = [0.1942, 0.1918, 0.1958]DATA_FILE = Nonemodel.prepare(    paddle.optimizer.Momentum(        learning_rate=LinearWarmup(CosineAnnealingDecay(LR, MAX_EPOCH), 2000, 0., LR),        momentum=MOMENTUM,        parameters=model.parameters(),        weight_decay=WEIGHT_DECAY),    paddle.nn.CrossEntropyLoss(),    paddle.metric.Accuracy(topk=(1,5)))# 定义数据集增强方式transforms = Compose([    RandomCrop(32, padding=4),    RandomApply(BrightnessTransform(0.1)),    RandomApply(ContrastTransform(0.1)),    RandomHorizontalFlip(),    RandomRotation(15),    ToArray(),    Normalize(CIFAR_MEAN, CIFAR_STD),])val_transforms = Compose([ToArray(), Normalize(CIFAR_MEAN, CIFAR_STD)])# 加载训练和测试数据集train_set = Cifar100(DATA_FILE, mode='train', transform=transforms)test_set = Cifar100(DATA_FILE, mode='test', transform=val_transforms)# 定义保存方式和训练可视化checkpoint_callback = paddle.callbacks.ModelCheckpoint(save_freq=1, save_dir='output/ResNet50')callbacks = [LRSchedulerM(),checkpoint_callback, callbacks.VisualDL('vis_logs/resnet50.log')]# 训练模型model.fit(    train_set,    test_set,    epochs=MAX_EPOCH,     batch_size=BATCH_SIZE,    shuffle=True,    num_workers=4,    verbose=1,     callbacks=callbacks,)

实验结果

两次实验均使用相同的参数：

epoch = 100lr = 0.01weight_decay = 5e-4momentum = 0.9pretrained = False

ResNet50-FCN模型的Top-1 acc和Top-5 acc如下图所示:

ResNet50模型的Top-1 acc和Top-5 acc如下图所示:

通过比较，经过修改后的模型效果得到了提升，且准确率上升过程更加平滑，抖动较小。

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