有关语义分割的奇技淫巧有哪些？
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作者：AlexL
来源：https://www.zhihu.com/question/272988870/answer/562262315
知乎问题：有关语义分割的奇技淫巧有哪些？
AlexL的回答：
代码取自在Kaggle论坛上看到的帖子和个人做过的project
1. 如何去优化IoU
在分割中我们有时会去用intersection over union去衡量模型的表现，具体定义如下：
在有了这个定义以后我们可以规定比如说对于predicted instance和actual instance，IoU大于0.5算一个positive。在这基础之上可以做一些F1，F2之类其他的更宏观的metric。
所以说怎么去优化IoU呢？¬_¬
拿二分类问题举例，做baseline的时先扔上个binary-crossentropy看下效果，于是就有了以下的实现（PyTorch）：
class BCELoss2d(nn.Module): def __init__(self, weight=None, size_average=True): super(BCELoss2d, self).__init__() self.bce_loss = nn.BCELoss(weight, size_average) def forward(self, logits, targets): probs = F.sigmoid(logits) probs_flat = probs.view (-1) targets_flat = targets.view(-1) return self.bce_loss(probs_flat, targets_flat)但是问题在于，优化BCE不等价于优化IoU。这篇文章（arxiv.org/pdf/1705.08790.pdf）说的显然比我要好，但是直观来说在一个minibatch里每个pixel的权重其实是不一样的。两张图片，一张正样本有1000个pixels，另一张只有4个，第二张一个pixel带来的IoU损失就能顶得上第一张中250个pixel的损失。
“那能不能直接优化IoU？“
可以，但这肯定不是最优的：
def iou_coef(y_true, y_pred, smooth=1): """ IoU = (|X & Y|)/ (|X or Y|) """ intersection = K.sum(K.abs(y_true * y_pred), axis=-1) union = K.sum((y_true,-1) + K.sum(y_pred,-1) - intersection return (intersection + smooth) / ( union + smooth)def iou_coef_loss(y_true, y_pred): return -iou_coef(y_true, y_pred)这次的问题在于训练过程的不稳定。一个模型从坏到好，我们希望监督它的loss/metric的过渡是平滑的，但直接暴力套用IoU显然不行。。。。
于是我们有了Lovász-Softmax！A tractable surrogate for the optimization of the intersection-over-union measure in neural networks：
https://github.com/bermanmaxim/LovaszSoftmax
具体为什么这个loss比BCE/Jaccard要好我不敢瞎说......但从个人使用体验来看效果拔群 \ (•◡•) /
还有一个很有意思的细节是：原implementation中这一段：
loss = torch.dot(F.relu(errors_sorted), Variable(grad))如果把relu换成elu+1的话，有时效果更好。我猜测可能是因为elu+1比relu更平滑一些？
1.1 如果你不在乎训练时间的话
试试这个：
def symmetric_lovasz(outputs, targets): return (lovasz_hinge(outputs, targets) + lovasz_hinge(-outputs, 1 - targets)) / 21.2 如果你的模型斗不过Hard Examples的话
在你的loss后面加上这个：
def focal_loss(self, output, target, alpha, gamma, OHEM_percent): output = output.contiguous().view(-1) target = target.contiguous().view(-1) max_val = (-output).clamp(min=0) loss = output - output * target + max_val + ((-max_val).exp() + (-output - max_val).exp()).log() # This formula gives us the log sigmoid of 1-p if y is 0 and of p if y is 1 invprobs = F.logsigmoid(-output * (target * 2 - 1)) focal_loss = alpha * (invprobs * gamma).exp() * loss # Online Hard Example Mining: top x% losses (pixel-wise). Refer to http://www.robots.ox.ac.uk/~tvg/publications/2017/0026.pdf OHEM, _ = focal_loss.topk(k=int(OHEM_percent * [*focal_loss.shape][0])) return OHEM.mean()2. 魔改U-Net
原始Unet长这样子（Keras)：
def conv_block(neurons, block_input, bn=False, dropout=None): conv1 = Conv2D(neurons, (3,3), padding='same', kernel_initializer='glorot_normal')(block_input) if bn: conv1 = BatchNormalization()(conv1) conv1 = Activation('relu')(conv1) if dropout is not None: conv1 = SpatialDropout2D(dropout)(conv1) conv2 = Conv2D(neurons, (3,3), padding='same', kernel_initializer='glorot_normal')(conv1) if bn: conv2 = BatchNormalization()(conv2) conv2 = Activation('relu')(conv2) if dropout is not None: conv2 = SpatialDropout2D(dropout)(conv2) pool = MaxPooling2D((2,2))(conv2) return pool, conv2 # returns the block output and the shortcut to use in the uppooling blocksdef middle_block(neurons, block_input, bn=False, dropout=None): conv1 = Conv2D(neurons, (3,3), padding='same', kernel_initializer='glorot_normal')(block_input) if bn: conv1 = BatchNormalization()(conv1) conv1 = Activation('relu')(conv1) if dropout is not None: conv1 = SpatialDropout2D(dropout)(conv1) conv2 = Conv2D(neurons, (3,3), padding='same', kernel_initializer='glorot_normal')(conv1) if bn: conv2 = BatchNormalization()(conv2) conv2 = Activation('relu')(conv2) if dropout is not None: conv2 = SpatialDropout2D(dropout)(conv2) return conv2def deconv_block(neurons, block_input, shortcut, bn=False, dropout=None): deconv = Conv2DTranspose(neurons, (3, 3), strides=(2, 2), padding="same")(block_input) uconv = concatenate([deconv, shortcut]) uconv = Conv2D(neurons, (3, 3), padding="same", kernel_initializer='glorot_normal')(uconv) if bn: uconv = BatchNormalization()(uconv) uconv = Activation('relu')(uconv) if dropout is not None: uconv = SpatialDropout2D(dropout)(uconv) uconv = Conv2D(neurons, (3, 3), padding="same", kernel_initializer='glorot_normal')(uconv) if bn: uconv = BatchNormalization()(uconv) uconv = Activation('relu')(uconv) if dropout is not None: uconv = SpatialDropout2D(dropout)(uconv) return uconvdef build_model(start_neurons, bn=False, dropout=None): input_layer = Input((128, 128, 1)) # 128 -> 64 conv1, shortcut1 = conv_block(start_neurons, input_layer, bn, dropout) # 64 -> 32 conv2, shortcut2 = conv_block(start_neurons * 2, conv1, bn, dropout) # 32 -> 16 conv3, shortcut3 = conv_block(start_neurons * 4, conv2, bn, dropout) # 16 -> 8 conv4, shortcut4 = conv_block(start_neurons * 8, conv3, bn, dropout) #Middle convm = middle_block(start_neurons * 16, conv4, bn, dropout) # 8 -> 16 deconv4 = deconv_block(start_neurons * 8, convm, shortcut4, bn, dropout) # 16 -> 32 deconv3 = deconv_block(start_neurons * 4, deconv4, shortcut3, bn, dropout) # 32 -> 64 deconv2 = deconv_block(start_neurons * 2, deconv3, shortcut2, bn, dropout) # 64 -> 128 deconv1 = deconv_block(start_neurons, deconv2, shortcut1, bn, dropout) #uconv1 = Dropout(0.5)(uconv1) output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(deconv1) model = Model(input_layer, output_layer) return model但一般与其是用transposed convolution我们会选择用upsampling+3*3 conv，具体原因请见这篇文章：Deconvolution and Checkerboard Artifacts （强烈安利distill，blog质量奇高）
再往下说，在实际做project的时候往往没有那么多的训练资源，所以我们得想办法把那些classification预训练模型嵌入到Unet中。ʕ•ᴥ•ʔ
把encoder替换预训练的模型的诀窍在于，如何很好的提取出pretrained models在不同尺度上提取出来的信息，并且如何把它们高效的接在decoder上。常见的用于嫁接的模型有Inception和Mobilenet，但我在这里就分析一下更直观一些的ResNet/ResNeXt这一类的模型：
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) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x我们可以很明显的看出不同尺度的feature map分别是由不同的layer来提取的，我们就可以从中选出几个来做concat，upsample，conv。唯一一点要注意的是千万不要错位concat，否则最后出来的output可能会和输入图大小不同。下面分享一个可行的搭法，其中为了提升各feature map的resolution我移去了原resnet conv1中的pool：
关于decoder的设计方法，还有两个可以参考的小技巧：
一是 Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks（https://distill.pub/2016/deconv-checkerboard/)，可以理解为是一种attention，用很少的参数来校准feature map，详情请见论文，但实现细节可参考以下的PyTorch代码：
class sSE(nn.Module): def __init__(self, out_channels): super(sSE, self).__init__() self.conv = ConvBn2d(in_channels=out_channels,out_channels=1,kernel_size=1,padding=0) def forward(self,x): x=self.conv(x) #print('spatial',x.size()) x=F.sigmoid(x) return xclass cSE(nn.Module): def __init__(self, out_channels): super(cSE, self).__init__() self.conv1 = ConvBn2d(in_channels=out_channels,out_channels=int(out_channels/2),kernel_size=1,padding=0) self.conv2 = ConvBn2d(in_channels=int(out_channels/2),out_channels=out_channels,kernel_size=1,padding=0) def forward(self,x): x=nn.AvgPool2d(x.size()[2:])(x) #print('channel',x.size()) x=self.conv1(x) x=F.relu(x) x=self.conv2(x) x=F.sigmoid(x) return xclass Decoder(nn.Module): def __init__(self, in_channels, channels, out_channels): super(Decoder, self).__init__() self.conv1 = ConvBn2d(in_channels, channels, kernel_size=3, padding=1) self.conv2 = ConvBn2d(channels, out_channels, kernel_size=3, padding=1) self.spatial_gate = sSE(out_channels) self.channel_gate = cSE(out_channels) def forward(self, x, e=None): x = F.upsample(x, scale_factor=2, mode='bilinear', align_corners=True) #print('x',x.size()) #print('e',e.size()) if e is not None: x = torch.cat([x,e],1) x = F.relu(self.conv1(x),inplace=True) x = F.relu(self.conv2(x),inplace=True) #print('x_new',x.size()) g1 = self.spatial_gate(x) #print('g1',g1.size()) g2 = self.channel_gate(x) #print('g2',g2.size()) x = g1*x + g2*x return x还有一个就是为了进一步鼓励模型在多尺度上的鲁棒性，我们可以引入Hypercolumn(https://arxiv.org/pdf/1411.5752.pdf)去直接把各个scale的feature map concatenate起来：
f = torch.cat(( F.upsample(e1,scale_factor= 2, mode='bilinear',align_corners=False), d1, F.upsample(d2,scale_factor= 2, mode='bilinear',align_corners=False), F.upsample(d3,scale_factor= 4, mode='bilinear',align_corners=False), F.upsample(d4,scale_factor= 8, mode='bilinear',align_corners=False), F.upsample(d5,scale_factor=16, mode='bilinear',align_corners=False), ),1)f = F.dropout2d(f,p=0.50)logit = self.logit(f)更神奇的方法就是直接把每个scale的feature map和downsized gt进行比较计算loss，最后各个尺度的loss进行加权平均。详情请见这里的讨论：Deep semi-supervised learning | Kaggle
(https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/63715)这里就不再赘述了。
3. Training
其实训练我觉得真的是case by case，在task A上用的heuristics放到task B效果就反而没那么好，所以我就介绍一个大多场合下都能用的trick：Cosine Annealing w. Snapshot Ensemble (https://arxiv.org/abs/1704.00109)
听上去听酷炫的，实际上就是每个一段时间warm restart学习率，这样在单位时间内能得到多个而不是一个converged local minina，做融合的话手上的模型会多很多。放几张图上来感受一下：
实现的话，其实挺简单的：
CYCLE=8000LR_INIT=0.1LR_MIN=0.001scheduler = lambda x: ((LR_INIT-LR_MIN)/2)*(np.cos(PI*(np.mod(x-1,CYCLE)/(CYCLE)))+1)+LR_MIN然后每个batch/epoch去用scheduler(iteration)去更新学习率就可以了
4. 其他的一些小tricks（持续更新）
目前能想到的就是DSB2018 第一名的solution。与其是用mask rcnn去做instance segmentation，他们选择了U-Net生成class probability map+watershed小心翼翼分离离得比较近的instances。最后也是取得了领先第二名一截的成绩。不得不说有时候比起研究模型，研究数据并精炼出关键的insight往往能带来更多的收益......
最后安利一下我自己的repo： liaopeiyuan/ml-arsenal-public
(https://github.com/liaopeiyuan/ml-arsenal-public)，里面会有我所有参与过的Kaggle竞赛的源代码，目前有两个Top 1% solution：TGS Salt和Quick Draw Doodle。欢迎大家提issues/pull requests! XD
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