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【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
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。
b站：https://www.bilibili.com/video/BV1Ta411x7nL/
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：https://cloud.videojj.com/auth/register?inviter=18452&activityChannel=student_invite
创建实例：
2.2 开始搭建
进入home
cd home- 1
下载项目
git clone https://github.com/Wenhai-Zhu/JN-OpenLib-mmaction2.git- 1
或者用码云（国内速度快）
git clone https://gitee.com/YFwinston/JN-OpenLib-mmaction2.git- 1
环境搭建（AI云平台的操作方法）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- 1
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环境搭建（非云平台的操作方法）
conda create -n JN-OpenLib-mmaction2-pytorch1.6-py3.6 -y python=3.6 conda activate JN-OpenLib-mmaction2-pytorch1.6-py3.6 pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html pip install mmcv-full==1.2.7 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html pip install mmpycocotools pip install moviepy opencv-python terminaltables seaborn decord -i https://pypi.douban.com/simple- 1
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进入JN-OpenLib-mmaction2
cd JN-OpenLib-mmaction2/ python setup.py develop- 1
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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'}- 1
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- 1
创建slowfastDataSet文件夹
mkdir slowfastDataSet- 1
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)- 1
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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- 1
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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- 1
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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-mmaction2cd JN-OpenLib-mmaction2- 1
创建软链接
ln -s /user-data/slowfastDataSet/Datasets data- 1
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- 1
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- 1
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self.f_bce = F_BCE() self.BN = nn.BatchNorm1d(8)- 1
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cls_score = self.BN(cls_score) f_bce_loss = self.f_bce losses['loss_action_cls'] = f_bce_loss(cls_score, labels)- 1
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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, rois, cls_score, img_shape, flip=False, crop_quadruple=None, cfg = None): # might be used by testing w. augmentation if isinstance(cls_score, list): cls_score = sum(cls_score) / float(len(cls_score)) assert self.multilabel scores = cls_score.sigmoid() if cls_score is not None else None bboxes = rois[:, 1:] assert bboxes.shape[-1] == 4 # First reverse the flip img_h, img_w = img_shape if flip: bboxes_ = bboxes.clone() bboxes_[:, 0] = img_w - 1 - bboxes[:, 2] bboxes_[:, 2] = img_w - 1 - bboxes[:, 0] bboxes = bboxes_ # Then normalize the bbox to [0, 1] bboxes[:, 0::2] /= img_w bboxes[:, 1::2] /= img_h def _bbox_crop_undo(bboxes, crop_quadruple): decropped = bboxes.clone() if crop_quadruple is not None: x1, y1, tw, th = crop_quadruple decropped[:, 0::2] = bboxes[..., 0::2] * tw + x1 decropped[:, 1::2] = bboxes[..., 1::2] * th + y1 return decropped bboxes = _bbox_crop_undo(bboxes, crop_quadruple) return bboxes, scores if mmdet_imported: MMDET_HEADS.register_module()(BBoxHeadAVA)- 1
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开始训练：
python tools/train.py configs/detection/via3/my_slowfast_kinetics_pretrained_r50_8x8x1_20e_via3_rgb.py --validate- 1
4.5 实验对比
经过15个小时的训练
首先是原模型（改进前）
来看看结果
0s2021-11-04 11:26:56,234 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq5.json with 556 images now 2021-11-04 11:26:56,234 - mmaction - INFO - Evaluating mAP ... ==> 0.035635 seconds to process groundtruth results ==> 0.0550323 seconds to process prediction results ==> 0.122445 seconds to Convert groundtruth ==> 0.302411 seconds to convert prediction ==> 0.0125372 seconds to run_evaluator mAP@0.5IOU= 0.822277391327469 PerformanceByCategory/AP@0.5IOU/handshake= 0.8867924528301887 PerformanceByCategory/AP@0.5IOU/point= 0.6091617933723197 PerformanceByCategory/AP@0.5IOU/hug= 0.7931456548347613 PerformanceByCategory/AP@0.5IOU/push= 0.8666666666666667 PerformanceByCategory/AP@0.5IOU/kick= 0.7 PerformanceByCategory/AP@0.5IOU/punch= 0.9945054945054945 2021-11-04 11:26:56,766 - mmaction - INFO - mAP@0.5IOU 0.8223 2021-11-04 11:26:56,768 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq10.json with 557 images now 2021-11-04 11:26:56,768 - mmaction - INFO - Evaluating mAP ... ==> 0.0580194 seconds to process groundtruth results ==> 0.0607066 seconds to process prediction results ==> 0.097141 seconds to Convert groundtruth ==> 0.315272 seconds to convert prediction ==> 0.015708 seconds to run_evaluator mAP@0.5IOU= 0.40562803653484647 PerformanceByCategory/AP@0.5IOU/handshake= 0.45454545454545453 PerformanceByCategory/AP@0.5IOU/point= 0.391812865497076 PerformanceByCategory/AP@0.5IOU/hug= 0.6896551724137931 PerformanceByCategory/AP@0.5IOU/push= 0.37356321839080453 PerformanceByCategory/AP@0.5IOU/kick= 0.0 PerformanceByCategory/AP@0.5IOU/punch= 0.0 2021-11-04 11:26:57,318 - mmaction - INFO - mAP@0.5IOU 0.4056 2021-11-04 11:26:57,318 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq15.json with 607 images now 2021-11-04 11:26:57,318 - mmaction - INFO - Evaluating mAP ... ==> 0.0335379 seconds to process groundtruth results ==> 0.0631692 seconds to process prediction results ==> 0.10759 seconds to Convert groundtruth ==> 0.37948 seconds to convert prediction ==> 0.0157986 seconds to run_evaluator mAP@0.5IOU= 0.5331220241603439 PerformanceByCategory/AP@0.5IOU/handshake= 0.8571428571428571 PerformanceByCategory/AP@0.5IOU/point= 0.25 PerformanceByCategory/AP@0.5IOU/hug= 0.7222222222222222 PerformanceByCategory/AP@0.5IOU/push= 0.452991452991453 PerformanceByCategory/AP@0.5IOU/kick= 0.1 PerformanceByCategory/AP@0.5IOU/punch= 0.38461538461538464 2021-11-04 11:26:57,921 - mmaction - INFO - mAP@0.5IOU 0.5331 2021-11-04 11:26:57,921 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq20.json with 622 images now 2021-11-04 11:26:57,921 - mmaction - INFO - Evaluating mAP ... ==> 0.0534511 seconds to process groundtruth results ==> 0.085907 seconds to process prediction results ==> 0.128184 seconds to Convert groundtruth ==> 0.38158 seconds to convert prediction ==> 0.0196507 seconds to run_evaluator mAP@0.5IOU= 0.49892216373873616 PerformanceByCategory/AP@0.5IOU/handshake= 0.5131485429992891 PerformanceByCategory/AP@0.5IOU/point= 0.5477982978485432 PerformanceByCategory/AP@0.5IOU/hug= 0.8906976744186047 PerformanceByCategory/AP@0.5IOU/push= 0.42094630515683146 PerformanceByCategory/AP@0.5IOU/kick= 0.0 PerformanceByCategory/AP@0.5IOU/punch= 0.17647058823529413 2021-11-04 11:26:58,594 - mmaction - INFO - mAP@0.5IOU 0.4989 2021-11-04 11:26:58,599 - mmaction - INFO - Epoch(val) [20][2643] 0_mAP@0.5IOU: 0.8223, 1_mAP@root@61826ea1b4e40269dc480687:/home/JN-OpenLib-mmaction2# , all_mAP@0.5IOU: 0.5650- 1
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可以看出原模型最后准确率：56.50%
然后是改进后的模型
Evaluateing data/Interaction/annotations/test/seq5.json with 556 images now 2021-11-04 12:14:31,246 - mmaction - INFO - Evaluating mAP ... ==> 0.0391157 seconds to process groundtruth results ==> 0.0535824 seconds to process prediction results ==> 0.127968 seconds to Convert groundtruth ==> 0.653159 seconds to convert prediction ==> 0.0301211 seconds to run_evaluator mAP@0.5IOU= 0.9069741460566432 PerformanceByCategory/AP@0.5IOU/handshake= 0.9942538308459411 PerformanceByCategory/AP@0.5IOU/point= 0.6242095754290875 PerformanceByCategory/AP@0.5IOU/hug= 0.9607235142118863 PerformanceByCategory/AP@0.5IOU/push= 1.0 PerformanceByCategory/AP@0.5IOU/kick= 0.9636363636363636 PerformanceByCategory/AP@0.5IOU/punch= 0.9395604395604397 2021-11-04 12:14:32,155 - mmaction - INFO - mAP@0.5IOU 0.9070 2021-11-04 12:14:32,155 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq10.json with 557 images now 2021-11-04 12:14:32,156 - mmaction - INFO - Evaluating mAP ... ==> 0.0397999 seconds to process groundtruth results ==> 0.0588491 seconds to process prediction results ==> 0.0968714 seconds to Convert groundtruth ==> 0.665299 seconds to convert prediction ==> 0.0389373 seconds to run_evaluator mAP@0.5IOU= 0.7747222320890791 PerformanceByCategory/AP@0.5IOU/handshake= 0.9065866429798629 PerformanceByCategory/AP@0.5IOU/point= 0.8147161450436735 PerformanceByCategory/AP@0.5IOU/hug= 0.9150861013434618 PerformanceByCategory/AP@0.5IOU/push= 0.9839296652614131 PerformanceByCategory/AP@0.5IOU/kick= 0.5192805038670355 PerformanceByCategory/AP@0.5IOU/punch= 0.43140462721305534 2021-11-04 12:14:33,073 - mmaction - INFO - mAP@0.5IOU 0.7747 2021-11-04 12:14:33,073 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq15.json with 607 images now 2021-11-04 12:14:33,073 - mmaction - INFO - Evaluating mAP ... ==> 0.0371342 seconds to process groundtruth results ==> 0.0646772 seconds to process prediction results ==> 0.101891 seconds to Convert groundtruth ==> 0.739635 seconds to convert prediction ==> 0.0439842 seconds to run_evaluator mAP@0.5IOU= 0.7889970077502289 PerformanceByCategory/AP@0.5IOU/handshake= 0.8908877061148943 PerformanceByCategory/AP@0.5IOU/point= 0.8125 PerformanceByCategory/AP@0.5IOU/hug= 0.9966124661246613 PerformanceByCategory/AP@0.5IOU/push= 0.6567905778432095 PerformanceByCategory/AP@0.5IOU/kick= 0.7602408702408703 PerformanceByCategory/AP@0.5IOU/punch= 0.4837407437954634 2021-11-04 12:14:34,069 - mmaction - INFO - mAP@0.5IOU 0.7890 2021-11-04 12:14:34,070 - mmaction - INFO - Evaluateing data/Interaction/annotations/test/seq20.json with 622 images now 2021-11-04 12:14:34,070 - mmaction - INFO - Evaluating mAP ... ==> 0.0685616 seconds to process groundtruth results ==> 0.0815992 seconds to process prediction results ==> 0.108798 seconds to Convert groundtruth ==> 0.813663 seconds to convert prediction ==> 0.0507228 seconds to run_evaluator mAP@0.5IOU= 0.7211447472837192 PerformanceByCategory/AP@0.5IOU/handshake= 0.9027139539608279 PerformanceByCategory/AP@0.5IOU/point= 0.4597024418984696 PerformanceByCategory/AP@0.5IOU/hug= 0.9709107878976433 PerformanceByCategory/AP@0.5IOU/push= 0.7328256368708507 PerformanceByCategory/AP@0.5IOU/kick= 0.7415679515722716 PerformanceByCategory/AP@0.5IOU/punch= 0.35279228700716714 2021-11-04 12:14:35,201 - mmaction - INFO - mAP@0.5IOU 0.7211 2021-11-04 12:14:35,207 - mmaction - INFO - Epoch(val) [20][2643] 0_mAP@0.5IOU: 0.9070, 1_mAP@0.5IOU: 0.7747, 2_mAP@0.5IOU: 0.7890, 3_mAP@0.5IOU: 0.7211, all_mAP@0.5IOU: 0.7980- 1
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可以看出原模型最后准确率：79.80%
可以看出这是明显的提高4.6 实时查看GPU使用情况
参考：https://blog.csdn.net/u014261408/article/details/109853936
有人写了个小工具gpustat把nvidia-smi封装了起来，用起来很爽很方便，推荐给大家。
首先安装：pip install gpustat- 1
然后使用：
gpustat -cp- 1
输出为：
注意-c选项为显示进程名，-p选项为显示进程PID。如果想要不间断持续输出，请使用：
gpustat -cp -i 1- 1
-
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- 原文地址：https://blog.csdn.net/WhiffeYF/article/details/125421998