网站登录慢,赣州招标网官网,wordpress 插件 发布文章,实名认证sdk今天看到一篇挺有意思的文章#xff0c;做的是跟医疗图像分割相关的工作#xff0c;但是不像之前看到的一些工作一味地去追求高精度#xff0c;因为医疗领域本身就是一个相对特殊的行业#xff0c;对于模型产生的结果的精确性要求是很高的#xff0c;带来的是参数量级的庞…今天看到一篇挺有意思的文章做的是跟医疗图像分割相关的工作但是不像之前看到的一些工作一味地去追求高精度因为医疗领域本身就是一个相对特殊的行业对于模型产生的结果的精确性要求是很高的带来的是参数量级的庞大之所以觉得这篇论文挺有意思的就是因为这里的主要的点在于超轻量级但是并没有导致精度大幅下降。
官方论文地址在这里如下所示 可见刚发表不久。 EGE-UNet融合了两个主要模块 Group multi-axis Hadamard Product Attention module (GHPA) Group Aggregation Bridge module (GAB) GHPA 利用哈达玛积注意力机制HPA通过将输入特征进行分组对不同轴进行 HPA 操作从多个视角提取病变信息。 GAB 通过分组聚合将不同规模的高级语义特征和低级细节特征以及解码器生成的掩码进行融合从而有效提取多尺度信息 通过融合上述两个模块提出了EGE-UNet模型实现了在参数和计算复杂度极低的情况下优秀的分割性能。 EGE-UNet的设计沿用了 U 形架构包括对称的编码器-解码器部分。编码器由六个 stage 组成各阶段的通道数量为{8, 16, 24, 32, 48, 64}。前三个阶段采用了普通卷积而后三个阶段使用提出的 GHPA 来从多视角提取表征信息。 EGE-UNet 在编码器和解码器之间的每个阶段都集成了GAB。此外模型还利用深监督生成不同规模的掩膜预测这些预测用于损失函数并作为 GAB 的输入之一。通过这些高级模块的集成EGE-UNet 在比先前的方法提升了分割性能的同时显著减少了参数和计算负载。 进一步详情可以自行研读发表的论文。
这里我也是初步了解了一下主要是想要实际使用一下这个超轻量级的网络因为我觉得这种类型的网络在现实工作里的意义更大大参数量高精度模型固然很好但是并未所有的工业或者是医疗场景里面的设备都具备那么高的算力能够支撑如此庞大的计算量的如果能在高度轻量化的网络基础上保持不俗的精度性能的话着实还是很有实际意义的。
官方同时开源了项目地址在这里如下所示 感觉目前的star量很少估计是了解到的人还不多吧就让我来带一波热度吧。
从readme来看作者给出来的实操训练手册可以说是简单到了极致了 数据集也一并准备好了地址在这里如下所示 自行下载下来即可体积不大下载起来应该还是很快的。
下载下载放到项目data目录下面解压缩即可如下所示 可以看到作者同时提供了两组数据集项目源码默认使用的是isic2017的数据集的。
直接终端执行train.py模块即可如下所示 默认300个epoch的迭代计算 训练完成截图如下所示 结果默认存储在results目录下。如下所示 checkpoints目录下存放的是训练得到的模型文件如下所示 log目录下存放的是训练日志数据如下所示 outputs目录下存放的是实际测试的实例图像可视化结果如下所示 官方项目只提供了训练、评估使用的代码没有提供离线推理可直接使用的代码但是基于训练和评估部分的代码可以自行开发离线推理的代码这里我为了能够更加简单的使用开发了专用的可视化系统界面实例推理效果如下所示 到这里基本完整的实践就结束了前面也说过了源码默认使用的是isic2017的数据集所以后面我又考虑基于isic2018的数据集也开发训练一下模型只需要修改configs目录下的参数即可如下所示 修改后的config_setting模块如下所示
from torchvision import transforms
from utils import *from datetime import datetimeclass setting_config:the config of training setting.network egeunetmodel_config {num_classes: 1, input_channels: 3, c_list: [8,16,24,32,48,64], bridge: True,gt_ds: True,}datasets isic18 if datasets isic18:data_path ./data/isic2018/elif datasets isic17:data_path ./data/isic2017/else:raise Exception(datasets in not right!)criterion GT_BceDiceLoss(wb1, wd1)pretrained_path ./pre_trained/num_classes 1input_size_h 256input_size_w 256input_channels 3distributed Falselocal_rank -1num_workers 0seed 42world_size Nonerank Noneamp Falsegpu_id 0batch_size 8epochs 300work_dir results/ network _ datasets _ datetime.now().strftime(%A_%d_%B_%Y_%Hh_%Mm_%Ss) /print_interval 20val_interval 30save_interval 100threshold 0.5train_transformer transforms.Compose([myNormalize(datasets, trainTrue),myToTensor(),myRandomHorizontalFlip(p0.5),myRandomVerticalFlip(p0.5),myRandomRotation(p0.5, degree[0, 360]),myResize(input_size_h, input_size_w)])test_transformer transforms.Compose([myNormalize(datasets, trainFalse),myToTensor(),myResize(input_size_h, input_size_w)])opt AdamWassert opt in [Adadelta, Adagrad, Adam, AdamW, Adamax, ASGD, RMSprop, Rprop, SGD], Unsupported optimizer!if opt Adadelta:lr 0.01 # default: 1.0 – coefficient that scale delta before it is applied to the parametersrho 0.9 # default: 0.9 – coefficient used for computing a running average of squared gradientseps 1e-6 # default: 1e-6 – term added to the denominator to improve numerical stability weight_decay 0.05 # default: 0 – weight decay (L2 penalty) elif opt Adagrad:lr 0.01 # default: 0.01 – learning ratelr_decay 0 # default: 0 – learning rate decayeps 1e-10 # default: 1e-10 – term added to the denominator to improve numerical stabilityweight_decay 0.05 # default: 0 – weight decay (L2 penalty)elif opt Adam:lr 0.001 # default: 1e-3 – learning ratebetas (0.9, 0.999) # default: (0.9, 0.999) – coefficients used for computing running averages of gradient and its squareeps 1e-8 # default: 1e-8 – term added to the denominator to improve numerical stability weight_decay 0.0001 # default: 0 – weight decay (L2 penalty) amsgrad False # default: False – whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyondelif opt AdamW:lr 0.001 # default: 1e-3 – learning ratebetas (0.9, 0.999) # default: (0.9, 0.999) – coefficients used for computing running averages of gradient and its squareeps 1e-8 # default: 1e-8 – term added to the denominator to improve numerical stabilityweight_decay 1e-2 # default: 1e-2 – weight decay coefficientamsgrad False # default: False – whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond elif opt Adamax:lr 2e-3 # default: 2e-3 – learning ratebetas (0.9, 0.999) # default: (0.9, 0.999) – coefficients used for computing running averages of gradient and its squareeps 1e-8 # default: 1e-8 – term added to the denominator to improve numerical stabilityweight_decay 0 # default: 0 – weight decay (L2 penalty) elif opt ASGD:lr 0.01 # default: 1e-2 – learning rate lambd 1e-4 # default: 1e-4 – decay termalpha 0.75 # default: 0.75 – power for eta updatet0 1e6 # default: 1e6 – point at which to start averagingweight_decay 0 # default: 0 – weight decayelif opt RMSprop:lr 1e-2 # default: 1e-2 – learning ratemomentum 0 # default: 0 – momentum factoralpha 0.99 # default: 0.99 – smoothing constanteps 1e-8 # default: 1e-8 – term added to the denominator to improve numerical stabilitycentered False # default: False – if True, compute the centered RMSProp, the gradient is normalized by an estimation of its varianceweight_decay 0 # default: 0 – weight decay (L2 penalty)elif opt Rprop:lr 1e-2 # default: 1e-2 – learning rateetas (0.5, 1.2) # default: (0.5, 1.2) – pair of (etaminus, etaplis), that are multiplicative increase and decrease factorsstep_sizes (1e-6, 50) # default: (1e-6, 50) – a pair of minimal and maximal allowed step sizes elif opt SGD:lr 0.01 # – learning ratemomentum 0.9 # default: 0 – momentum factor weight_decay 0.05 # default: 0 – weight decay (L2 penalty) dampening 0 # default: 0 – dampening for momentumnesterov False # default: False – enables Nesterov momentum sch CosineAnnealingLRif sch StepLR:step_size epochs // 5 # – Period of learning rate decay.gamma 0.5 # – Multiplicative factor of learning rate decay. Default: 0.1last_epoch -1 # – The index of last epoch. Default: -1.elif sch MultiStepLR:milestones [60, 120, 150] # – List of epoch indices. Must be increasing.gamma 0.1 # – Multiplicative factor of learning rate decay. Default: 0.1.last_epoch -1 # – The index of last epoch. Default: -1.elif sch ExponentialLR:gamma 0.99 # – Multiplicative factor of learning rate decay.last_epoch -1 # – The index of last epoch. Default: -1.elif sch CosineAnnealingLR:T_max 50 # – Maximum number of iterations. Cosine function period.eta_min 0.00001 # – Minimum learning rate. Default: 0.last_epoch -1 # – The index of last epoch. Default: -1. elif sch ReduceLROnPlateau:mode min # – One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing. Default: ‘min’.factor 0.1 # – Factor by which the learning rate will be reduced. new_lr lr * factor. Default: 0.1.patience 10 # – Number of epochs with no improvement after which learning rate will be reduced. For example, if patience 2, then we will ignore the first 2 epochs with no improvement, and will only decrease the LR after the 3rd epoch if the loss still hasn’t improved then. Default: 10.threshold 0.0001 # – Threshold for measuring the new optimum, to only focus on significant changes. Default: 1e-4.threshold_mode rel # – One of rel, abs. In rel mode, dynamic_threshold best * ( 1 threshold ) in ‘max’ mode or best * ( 1 - threshold ) in min mode. In abs mode, dynamic_threshold best threshold in max mode or best - threshold in min mode. Default: ‘rel’.cooldown 0 # – Number of epochs to wait before resuming normal operation after lr has been reduced. Default: 0.min_lr 0 # – A scalar or a list of scalars. A lower bound on the learning rate of all param groups or each group respectively. Default: 0.eps 1e-08 # – Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.elif sch CosineAnnealingWarmRestarts:T_0 50 # – Number of iterations for the first restart.T_mult 2 # – A factor increases T_{i} after a restart. Default: 1.eta_min 1e-6 # – Minimum learning rate. Default: 0.last_epoch -1 # – The index of last epoch. Default: -1. elif sch WP_MultiStepLR:warm_up_epochs 10gamma 0.1milestones [125, 225]elif sch WP_CosineLR:warm_up_epochs 20
重新训练启动日志输出如下所示 整体的资源占用可以看到还是很低的如下所示 等到模型训练完成后再来看下实际效果感兴趣的话都可以自己尝试实践一下。后面可以考虑将本文中的超轻量级的模型应用到实际项目开发过程中。
