作者：AlexL
作者的 Github 项目：liaopeiyuan/ml-arsenal-public.
项目里会有作者所有参与过的Kaggle竞赛的源代码，目前有两个Top 1% solution：TGS Salt和Quick Draw Doodle.
1. 如何优化 IoU
在分割中我们有时会去用 IoU(intersection over union)去衡量模型的表现，具体定义如下：
$$ IoU(A, B) = \frac{A \cap B}{A \cup B } $$
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.
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 and 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. . . .
于是就有了 LovaszSoftmax. 具体为什么这个 loss 比 BCE/Jaccard 要好，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. 作者猜测可能是因为elu+1比relu更平滑一些？
1.1. 如果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 (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 blocks
def 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 conv2
def 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 uconv
def 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.
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， 作者移去了原 resnet conv1中的pool：
def __init__(self):
super().__init__()
self.resnet = models.resnet34(pretrained=True)
self.conv1 = nn.Sequential(
self.resnet.conv1,
self.resnet.bn1,
self.resnet.relu,
)
self.encoder2 = self.resnet.layer1 # 64
self.encoder3 = self.resnet.layer2 #128
self.encoder4 = self.resnet.layer3 #256
self.encoder5 = self.resnet.layer4 #512
self.center = nn.Sequential(
ConvBn2d(512,512,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
ConvBn2d(512,256,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2,stride=2),
)
self.decoder5 = Decoder(256+512,512,64)
self.decoder4 = Decoder(64 +256,256,64)
self.decoder3 = Decoder(64 +128,128,64)
self.decoder2 = Decoder(64 +64 ,64 ,64)
self.decoder1 = Decoder(64 ,32 ,64)
self.logit = nn.Sequential(
nn.Conv2d(384, 64, kernel_size=3, padding=1),
nn.ELU(inplace=True),
nn.Conv2d(64, 1, kernel_size=1, padding=0),
)
def forward(self, x):
mean=[0.485, 0.456, 0.406]
std=[0.229,0.224,0.225]
x=torch.cat([
(x-mean[2])/std[2],
(x-mean[1])/std[1],
(x-mean[0])/std[0],
],1)
e1 = self.conv1(x)
e2 = self.encoder2(e1)
e3 = self.encoder3(e2)
e4 = self.encoder4(e3)
e5 = self.encoder5(e4)
f = self.center(e5)
d5 = self.decoder5(f, e5)
d4 = self.decoder4(d5,e4)
d3 = self.decoder3(d4,e3)
d2 = self.decoder2(d3,e2)
d1 = self.decoder1(d2)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 - 2018，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 x
class 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 x
class 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 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，所以就介绍一个大多场合下都能用的trick：Cosine Annealing w. Snapshot Ensemble
听上去听酷炫的，实际上就是 每隔一段时间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=8000
LR_INIT=0.1
LR_MIN=0.001
scheduler = 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......