paddle 图像分割学习总结

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paddle 图像分割学习总结

2022-12-17 04:38| 来源: 网络整理| 查看: 265

百度飞桨AI Studio社区 paddle 图像分割学习总结 paddle 图像分割学习总结

提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档文章目录前言一、pandas是什么？二、使用步骤1.引入库2.读入数据总结前言提示：这里可以添加本文要记录的大概内容：例如：随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习的基础内容。提示：以下是本篇文章正文内容，下面案例可供参考一、pandas是什么？示例：pandas 是基于N

耶耶耶耶耶~ 781人浏览 · 2020-10-26 11:17:14

耶耶耶耶耶~ · 2020-10-26 11:17:14

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文章目录前言一、图像分割综述二、FCNU-NetPSPNetDeepLab

前言

课程链接：https://aistudio.baidu.com/aistudio/course/introduce/1767

提示：以下是本篇文章正文内容

一、图像分割综述

根据不同的任务和数据类型：

-图像分割（image segmentation）（像素级分类）

图像语义分割（image semantic segmentation）图像实例分割（image instance segmentation）图像全景分割（image panoptic segmentation）视频目标分割（video object segmentation）视频实例分割（video instance segmentation）

示例：

语义分割：给每个pixel分类实例分割：给每个筐里的object分mask全景分割：背景pixel分类+框里maskVOS ：通常会给定目标的mask，求特定目标的maskVIS ：根据目标检测的框，求目标的mask 在这里插入图片描述

分割网络的评价指标：mIoU 和 mAcc

mean intersection-over-unio 分割每一类别的交并比mean accuracy 分对的像素 / 所有像素二、FCN

网络结构：在这里插入图片描述

代码如下（示例）：

import numpy as np import paddle.fluid as fluid from paddle.fluid.dygraph import to_variable from paddle.fluid.dygraph import Conv2D from paddle.fluid.dygraph import Conv2DTranspose from paddle.fluid.dygraph import Dropout from paddle.fluid.dygraph import BatchNorm from paddle.fluid.dygraph import Pool2D from paddle.fluid.dygraph import Linear # from vgg import VGG16BN class VGG(fluid.dygraph.Layer): def __init__(self, layers=16, use_bn=False, num_classes=1000): super(VGG, self).__init__() self.layers = layers self.use_bn = use_bn supported_layers = [16, 19] assert layers in supported_layers if layers == 16: depth = [2, 2, 3, 3, 3] elif layers == 19: depth = [2, 2, 4, 4, 4] num_channels = [3, 64, 128, 256, 512] num_filters = [64, 128, 256, 512, 512] self.layer1 = fluid.dygraph.Sequential(*self.make_layer(num_channels[0], num_filters[0], depth[0], use_bn, name='layer1')) self.layer2 = fluid.dygraph.Sequential(*self.make_layer(num_channels[1], num_filters[1], depth[1], use_bn, name='layer2')) self.layer3 = fluid.dygraph.Sequential(*self.make_layer(num_channels[2], num_filters[2], depth[2], use_bn, name='layer3')) self.layer4 = fluid.dygraph.Sequential(*self.make_layer(num_channels[3], num_filters[3], depth[3], use_bn, name='layer4')) self.layer5 = fluid.dygraph.Sequential(*self.make_layer(num_channels[4], num_filters[4], depth[4], use_bn, name='layer5')) self.classifier = fluid.dygraph.Sequential( Linear(input_dim=512 * 7 * 7, output_dim=4096, act='relu'), Dropout(), Linear(input_dim=4096, output_dim=4096, act='relu'), Dropout(), Linear(input_dim=4096, output_dim=num_classes)) self.out_dim = 512 * 7 * 7 def forward(self, inputs): x = self.layer1(inputs) x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2) x = self.layer2(x) x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2) x = self.layer3(x) x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2) x = self.layer4(x) x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2) x = self.layer5(x) x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2) x = fluid.layers.adaptive_pool2d(x, pool_size=(7,7), pool_type='avg') x = fluid.layers.reshape(x, shape=[-1, self.out_dim]) x = self.classifier(x) return x class FCN8s(fluid.dygraph.Layer): # TODO: create fcn8s model def __init__(self, num_classes=59): super(FCN8s, self).__init__() self.num_classes = num_classes self.layer1 = fluid.dygraph.Sequential( Conv2D(num_channels=3, num_filters=64, filter_size=3, padding=1), BatchNorm(num_channels=64, act='relu'), Conv2D(num_channels=64, num_filters=64, filter_size=3, padding=1), BatchNorm(num_channels=64, act='relu'), Pool2D(pool_size=2, pool_stride=2, pool_type='max') ) # 1/ 2 self.layer2 = fluid.dygraph.Sequential( Conv2D(num_channels=64, num_filters=128, filter_size=3, padding=1), BatchNorm(num_channels=128, act='relu'), Conv2D(num_channels=128, num_filters=128, filter_size=3, padding=1), BatchNorm(num_channels=128, act='relu'), Pool2D(pool_size=2, pool_stride=2, pool_type='max') ) # 1/ 4 self.layer3 = fluid.dygraph.Sequential( Conv2D(num_channels=128, num_filters=256, filter_size=3, padding=1), BatchNorm(num_channels=256, act='relu'), Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1), BatchNorm(num_channels=256, act='relu'), Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1), BatchNorm(num_channels=256, act='relu'), Pool2D(pool_size=2, pool_stride=2, pool_type='max') ) # 1 / 8 self.layer4 = fluid.dygraph.Sequential( Conv2D(num_channels=256, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Pool2D(pool_size=2, pool_stride=2, pool_type='max') ) # 1 / 16 self.layer5 = fluid.dygraph.Sequential( Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1), BatchNorm(num_channels=512, act='relu'), Pool2D(pool_size=2, pool_stride=2, pool_type='max') ) # 1 / 32 self.conv67 = fluid.dygraph.Sequential( Conv2D(num_channels=512, num_filters=512, filter_size=1), BatchNorm(num_channels=512, act='relu'), Conv2D(num_channels=512, num_filters=512, filter_size=1), BatchNorm(num_channels=512, act='relu') ) self.conv = Conv2D(num_channels=512, num_filters=self.num_classes, filter_size=1) def forward(self, inputs): x = self.layer1(inputs) x = self.layer2(x) x = self.layer3(x) pool3 = x # 1/8 256 x = self.layer4(x) pool4 = x # 1/16 512 x = self.layer5(x) x = self.conv67(x) x = fluid.layers.interpolate(x, pool4.shape[2:]) x = fluid.layers.elementwise_add(pool4, x) x = fluid.layers.interpolate(x, pool3.shape[2:]) pool3 = Conv2D(num_channels= 256, num_filters=512, filter_size=1, act='relu')(pool3) x = fluid.layers.elementwise_add(pool3, x) x = fluid.layers.interpolate(x, inputs.shape[2:]) x = self.conv(x) return x def main(): with fluid.dygraph.guard(): x_data = np.random.rand(2, 3, 512, 512).astype(np.float32) x = to_variable(x_data) model = FCN8s(num_classes=59) model.eval() pred = model(x) print(pred.shape) if __name__ == '__main__': main() U-Net

网络结构：

采用编码器和解码器的U形结构输入输出大小不变skip结合方式：concatenation 在这里插入图片描述

Paper Title: U-net:Convolutional networks for biomedical image segmentaion

在这里插入图片描述

PSPNet

psp模块在这里插入图片描述

import numpy as np import paddle.fluid as fluid from paddle.fluid.dygraph import to_variable from paddle.fluid.dygraph import Layer from paddle.fluid.dygraph import Conv2D from paddle.fluid.dygraph import BatchNorm from paddle.fluid.dygraph import Dropout from resnet_dilated import ResNet50 # pool with different bin_size # interpolate back to input size # concat class PSPModule(Layer): def __init__(self, num_channels, bin_size_list): super(PSPModule, self).__init__() self.bin_size_list = bin_size_list num_filters = num_channels // len(bin_size_list) self.features = [] for i in range(len(bin_size_list)): self.features.append( fluid.dygraph.Sequential( Conv2D(num_channels, num_filters, 1), BatchNorm(num_filters, act='relu') ) ) def forward(self, inputs): out = [inputs] for idx, f in enumerate(self.features): x = fluid.layers.adaptive_pool2d(inputs, self.bin_size_list[idx]) x = f(x) x = fluid.layers.interpolate(x, inputs.shape[2:], align_corners=True) out.append(x) out = fluid.layers.concat(out, axis=1) return out class PSPNet(Layer): def __init__(self, num_classes=59, backbone='resnet50'): super(PSPNet, self).__init__() res = ResNet50() # stem: res.conv, res.pool2d_max self.layer0 = fluid.dygraph.Sequential( res.conv, res.pool2d_max ) self.layer1 = res.layer1 self.layer2 = res.layer2 self.layer3 = res.layer3 self.layer4 = res.layer4 num_channels = 2048 # psp: 2048 -> 2048*2 self.pspmodule = PSPModule(num_channels, [1,2,3,6]) num_channels *= 2 # cls: 2048*2 -> 512 -> num_classes self.classifier = fluid.dygraph.Sequential( Conv2D(num_channels = num_channels, num_filters=512, filter_size=3, padding=1), BatchNorm(512, act = 'relu'), Dropout(0.1), Conv2D(num_channels=512, num_filters=num_classes, filter_size=1) ) # aux: 1024 -> 256 -> num_classes def forward(self, inputs): # aux: tmp_x = layer3 x = self.layer0(inputs) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) aux = x x = self.layer4(x) x = self.pspmodule(x) x = self.classifier(x) x = fluid.layers.interpolate(x, inputs.shape[2:]) return x, aux def main(): with fluid.dygraph.guard(fluid.CPUPlace()): x_data=np.random.rand(2,3, 473, 473).astype(np.float32) x = to_variable(x_data) model = PSPNet(num_classes=59) model.train() pred, aux = model(x) print(pred.shape, aux.shape) if __name__ =="__main__": main() DeepLab

aspp模块升级版在这里插入图片描述

class ASPPPooling(Layer): # TODO: def __init__(self, num_channels, num_filters): super(ASPPPooling, self).__init__() self.features = fluid.dygraph.Sequential( Conv2D(num_channels, num_filters, 1), BatchNorm(num_filters, act='relu') ) def forward(self, inputs): n, c, h, w = inputs.shape x = fluid.layers.adaptive_pool2d(inputs, 1) x = self.features(x) x = fluid.layers.interpolate(x, [h, w]) return x class ASPPConv(fluid.dygraph.Sequential): # TODO: def __init__(self, num_channels, num_filters, dilation): super(ASPPConv, self).__init__( Conv2D(num_channels=num_channels, num_filters=num_filters, filter_size=3, padding=dilation, dilation=dilation), BatchNorm(num_filters, act='relu') ) class ASPPModule(Layer): # TODO: def __init__(self, num_channels, num_filters, rates): super(ASPPModule, self).__init__() self.features = [] self.features.append( fluid.dygraph.Sequential( Conv2D(num_channels, num_filters, 1), BatchNorm(num_filters, act='relu') ) ) self.features.append(ASPPPooling(num_channels, num_filters)) for r in rates: self.features.append(ASPPConv(num_channels, num_filters, r)) self.project = fluid.dygraph.Sequential( Conv2D(1280, 256, 1), BatchNorm(256, act='relu') ) def forward(self, inputs): res = [] for f in self.features: res.append(f(inputs)) x = fluid.layers.concat(res, axis=1) x = self.project(x) return x class DeepLabHead(fluid.dygraph.Sequential): def __init__(self, num_channels, num_classes): super(DeepLabHead, self).__init__( ASPPModule(num_channels, 256, [12, 24, 36]), Conv2D(256, 256, 3, padding=1), BatchNorm(256, act='relu'), Conv2D(256, num_classes, 1) ) class DeepLab(Layer): # TODO: def __init__(self, num_classes=59): super(DeepLab, self).__init__() bone = ResNet50(pretrained=False, duplicate_blocks=True) self.layer0 = fluid.dygraph.Sequential( bone.conv, bone.pool2d_max ) self.layer1 = bone.layer1 self.layer2 = bone.layer2 self.layer3 = bone.layer3 self.layer4 = bone.layer4 #multi grid self.layer5 = bone.layer5 self.layer6 = bone.layer6 self.layer7 = bone.layer7 # feature_dim = 2048 self.classifier = DeepLabHead(2048, 59) def forward(self, inputs): n, c, h, w = inputs.shape x = self.layer0(inputs) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.layer5(x) x = self.layer6(x) x = self.layer7(x) x = self.classifier(x) x = fluid.layers.interpolate(x, [h, w], align_corners=False) return x Logo

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paddle 图像分割学习总结

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