slowfast 损失函数改进深度学习网络通用改进方案:slowfast的损失函数(使用focal loss解决不平衡数据)改进
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目录
引言
我最近一个月都在写论文,反反复复改了不下20次。我觉得还是写博客舒服,只要把思路写清楚就可以,不用在乎用词和语法问题。
本文所写的改进方案适合数据集中数据存在不平衡的情况。数据越失衡,效果越好。
二,项目搭建
使用的项目的例子就用我之前的slowfast项目:
01【mmaction2 slowfast 行为分析(商用级别)】项目下载
02【mmaction2 slowfast 行为分析(商用级别)】项目demo搭建
2.1 平台选择
我还是用极链AI
创建实例:
2.2 开始搭建
进入home
cd home
下载项目
git clone https://github.com/Wenhai-Zhu/JN-OpenLib-mmaction2.git
或者用码云(国内速度快)
git clone https://gitee.com/YFwinston/JN-OpenLib-mmaction2.git
环境搭建
pip install mmcv-full==1.2.7 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html
pip install mmpycocotools
pip install moviepy opencv-python terminaltables seaborn decord -i https://pypi.douban.com/simple
进入JN-OpenLib-mmaction2
cd JN-OpenLib-mmaction2/
python setup.py develop
注意:上面的 cu102/torch1.6.0 一定要和创建环境的配置一直,cuda版本,torch版本
三,数据集
3.1 数据集下载
我们先在AI云平台上创建上传数据集,这个数据集是一个监控打架的数据集
链接: https://pan.baidu.com/s/1wI7PVB9g5k6CcVDOfICW7A 提取码: du5o
这个数据集有6个动作分类:
options={'0':'None','1':'handshake', '2':'point', '3':'hug', '4':'push','5':'kick', '6':'punch'}
3.2 上传数据集
我们要把数据集放到数据AI云平台的数据管理的位置,放在这个位置,方便我们创建的所有实例的使用。
在根目录下,进入user-data
cd user-data
创建slowfastDataSet文件夹
mkdir slowfastDataSet
上传数据集:采用下面链接对应的方法
https://cloud.videojj.com/help/docs/data_manage.html#vcloud-oss-cli
数据集上传到slowfastDataSet文件夹下
3.3 数据集的统计
使用本文的改进方案,最重要的就是确保这个数据集是不平衡的,所以,我们来对这个数据集每个类别进行数据统计,看看数据集是不是不平衡的。
我们在AI平台上创建一个notebook(要在这里面写数据集统计代码)
重命名为dataTemp.ipynb
代码如下:
import json
#统计数据集中训练集/测试集的数据分布
file_dir = "/user-data/slowfastDataSet/Datasets/Interaction/annotations/train/"
#file_dir = "/user-data/slowfastDataSet/Datasets/Interaction/annotations/test/"
#训练集/测试集下文件名字
names = ['seq1','seq2','seq3','seq4','seq6','seq7','seq8','seq9','seq11','seq12',
'seq13','seq14','seq16','seq17','seq18','seq19']
#names = ['seq5','seq10','seq15','seq20']
#动作类别统计
action1=0
action2=0
action3=0
action4=0
action5=0
action6=0
#开始统计
for name in names:
file_name = file_dir + name + '.json'
f = open(file_name, encoding='utf-8')
setting = json.load(f) # 把json文件转化为python用的类型
f.close()
for file_1 in setting['metadata']:
str = file_1.split("_")
if str[1].isdigit():
action = setting['metadata'][file_1]['av']['1']
actions = action.split(",")
if '1' in actions:
action1 = 1 + action1
if '2' in actions:
action2 = 1 + action2
if '3' in actions:
action3 = 1 + action3
if '4' in actions:
action4 = 1 + action4
if '5' in actions:
action5 = 1 + action5
if '6' in actions:
action6 = 1 + action6
print("action1",action1)
print("action2",action2)
print("action3",action3)
print("action4",action4)
print("action5",action5)
print("action6",action6)
当我们对训练集进行统计时:
结果:
action1 1011
action2 709
action3 757
action4 358
action5 250
action6 320
当我们对测试集进行统计时:
结果:
action1 243
action2 132
action3 209
action4 95
action5 64
action6 94
我们在用excel,把这些图用图表的形式展示出来。
从上面统计数据来看,可以判断这个数据集是不平衡的。
四,项目运行
4.1 focal loss
简而言之,focal loss的作用就是将预测值低的类,赋予更大的损失函数权重,在不平衡的数据中,难分类别的预测值低,那么这些难分样本的损失函数被赋予的权重就更大。
4.2 训练前准备
创建链接 /user-data/slowfastDataSet/Datasets 文件夹的软链接:
先进入JN-OpenLib-mmaction2
cd JN-OpenLib-mmaction2
创建软链接
ln -s /user-data/slowfastDataSet/Datasets data
4.3 slowfast对数据集训练
python tools/train.py configs/detection/via3/my_slowfast_kinetics_pretrained_r50_8x8x1_20e_via3_rgb.py --validate
这里红色框出来的地方代表训练剩余时间
4.4 改进的slowfast对数据集训练
修改slowfast损失函数的位置:home/JN-OpenLib-mmaction2/mmaction/models/heads/bbox_head.py
class F_BCE(nn.Module):
def __init__(self, pos_weight=1, reduction='mean'):
super(F_BCE, self).__init__()
self.pos_weight = pos_weight
self.reduction = reduction
def forward(self, logits, target):
# logits: [N, *], target: [N, *]
logits = F.sigmoid(logits)
loss = - self.pos_weight * target * (1-logits)**2 * torch.log(logits) - \\
(1 - target) * logits**2 * torch.log(1 - logits)
if self.reduction == 'mean':
loss = loss.mean()
elif self.reduction == 'sum':
loss = loss.sum()
return loss
self.f_bce = F_BCE()
self.BN = nn.BatchNorm1d(8)
cls_score = self.BN(cls_score)
f_bce_loss = self.f_bce
losses['loss_action_cls'] = f_bce_loss(cls_score, labels)
bbox_head.py完整代码如下:
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmaction.core.bbox import bbox_target
try:
from mmdet.models.builder import HEADS as MMDET_HEADS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
class F_BCE(nn.Module):
def __init__(self, pos_weight=1, reduction='mean'):
super(F_BCE, self).__init__()
self.pos_weight = pos_weight
self.reduction = reduction
def forward(self, logits, target):
# logits: [N, *], target: [N, *]
logits = F.sigmoid(logits)
loss = - self.pos_weight * target * (1-logits)**2 * torch.log(logits) - \\
(1 - target) * logits**2 * torch.log(1 - logits)
if self.reduction == 'mean':
loss = loss.mean()
elif self.reduction == 'sum':
loss = loss.sum()
return loss
class BBoxHeadAVA(nn.Module):
"""Simplest RoI head, with only two fc layers for classification and
regression respectively.
Args:
temporal_pool_type (str): The temporal pool type. Choices are 'avg' or
'max'. Default: 'avg'.
spatial_pool_type (str): The spatial pool type. Choices are 'avg' or
'max'. Default: 'max'.
in_channels (int): The number of input channels. Default: 2048.
num_classes (int): The number of classes. Default: 81.
dropout_ratio (float): A float in [0, 1], indicates the dropout_ratio.
Default: 0.
dropout_before_pool (bool): Dropout Feature before spatial temporal
pooling. Default: True.
topk (int or tuple[int]): Parameter for evaluating multilabel accuracy.
Default: (3, 5)
multilabel (bool): Whether used for a multilabel task. Default: True.
(Only support multilabel == True now).
"""
def __init__(
self,
temporal_pool_type='avg',
spatial_pool_type='max',
in_channels=2048,
# The first class is reserved, to classify bbox as pos / neg
num_classes=81,
dropout_ratio=0,
dropout_before_pool=True,
topk=(3, 5),
multilabel=True,
loss_cfg = None):
super(BBoxHeadAVA, self).__init__()
assert temporal_pool_type in ['max', 'avg']
assert spatial_pool_type in ['max', 'avg']
self.temporal_pool_type = temporal_pool_type
self.spatial_pool_type = spatial_pool_type
self.in_channels = in_channels
self.num_classes = num_classes
self.dropout_ratio = dropout_ratio
self.dropout_before_pool = dropout_before_pool
self.multilabel = multilabel
if topk is None:
self.topk = ()
elif isinstance(topk, int):
self.topk = (topk, )
elif isinstance(topk, tuple):
assert all([isinstance(k, int) for k in topk])
self.topk = topk
else:
raise TypeError('topk should be int or tuple[int], '
f'but get {type(topk)}')
# Class 0 is ignored when calculaing multilabel accuracy,
# so topk cannot be equal to num_classes
assert all([k < num_classes for k in self.topk])
# Handle AVA first
assert self.multilabel
in_channels = self.in_channels
# Pool by default
if self.temporal_pool_type == 'avg':
self.temporal_pool = nn.AdaptiveAvgPool3d((1, None, None))
else:
self.temporal_pool = nn.AdaptiveMaxPool3d((1, None, None))
if self.spatial_pool_type == 'avg':
self.spatial_pool = nn.AdaptiveAvgPool3d((None, 1, 1))
else:
self.spatial_pool = nn.AdaptiveMaxPool3d((None, 1, 1))
if dropout_ratio > 0:
self.dropout = nn.Dropout(dropout_ratio)
self.fc_cls = nn.Linear(in_channels, num_classes)
self.debug_imgs = None
self.f_bce = F_BCE()
self.BN = nn.BatchNorm1d(6)
def init_weights(self):
nn.init.normal_(self.fc_cls.weight, 0, 0.01)
nn.init.constant_(self.fc_cls.bias, 0)
def forward(self, x):
if self.dropout_before_pool and self.dropout_ratio > 0:
x = self.dropout(x)
x = self.temporal_pool(x)
x = self.spatial_pool(x)
if not self.dropout_before_pool and self.dropout_ratio > 0:
x = self.dropout(x)
x = x.view(x.size(0), -1)
cls_score = self.fc_cls(x)
# We do not predict bbox, so return None
return cls_score, None
def get_targets(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
cls_reg_targets = bbox_target(pos_proposals, neg_proposals,
pos_gt_labels, rcnn_train_cfg)
return cls_reg_targets
def recall_prec(self, pred_vec, target_vec):
"""
Args:
pred_vec (tensor[N x C]): each element is either 0 or 1
target_vec (tensor[N x C]): each element is either 0 or 1
"""
correct = pred_vec & target_vec
# Seems torch 1.5 has no auto type conversion
recall = correct.sum(1) / (target_vec.sum(1).float()+ 1e-6)
prec = correct.sum(1) / (pred_vec.sum(1) + 1e-6)
return recall.mean(), prec.mean()
def multilabel_accuracy(self, pred, target, thr=0.5):
pred = pred.sigmoid()
pred_vec = pred > thr
# Target is 0 or 1, so using 0.5 as the borderline is OK
target_vec = target > 0.5
recall_thr, prec_thr = self.recall_prec(pred_vec, target_vec)
recalls, precs = [], []
for k in self.topk:
_, pred_label = pred.topk(k, 1, True, True)
pred_vec = pred.new_full(pred.size(), 0, dtype=torch.bool)
num_sample = pred.shape[0]
for i in range(num_sample):
pred_vec[i, pred_label[i]] = 1
recall_k, prec_k = self.recall_prec(pred_vec, target_vec)
recalls.append(recall_k)
precs.append(prec_k)
return recall_thr, prec_thr, recalls, precs
def loss(self,
cls_score,
bbox_pred,
rois,
labels,
label_weights,
bbox_targets=None,
bbox_weights=None,
reduce=True):
losses = dict()
if cls_score is not None:
# Only use the cls_score
#labels = labels[:, 1:]
# pos_inds = torch.sum(labels, dim=-1) > 0
# cls_score = cls_score[pos_inds, 1:]
# labels = labels[pos_inds]
labels = labels[:, 1:]
cls_score = cls_score[:, 1:]
cls_score = self.BN(cls_score)
f_bce_loss = self.f_bce
losses['loss_action_cls'] = f_bce_loss(cls_score, labels)
#bce_loss = F.binary_cross_entropy_with_logits
#losses['loss_action_cls'] = bce_loss(cls_score, labels)
recall_thr, prec_thr, recall_k, prec_k = self.multilabel_accuracy(
cls_score, labels, thr=0.5)
losses['recall@thr=0.5'] = recall_thr
losses['prec@thr=0.5'] = prec_thr
for i, k in enumerate(self.topk):
losses[f'recall@top{k}'] = recall_k[i]
losses[f'prec@top{k}'] = prec_k[i]
return losses
def get_det_bboxes(self以上是关于slowfast 损失函数改进深度学习网络通用改进方案:slowfast的损失函数(使用focal loss解决不平衡数据)改进的主要内容,如果未能解决你的问题,请参考以下文章
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