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。
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
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（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
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
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,
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)
开始训练：
python tools/train.py configs/detection/via3/my_slowfast_kinetics_pretrained_r50_8x8x1_20e_via3_rgb.py --validate
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
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
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
然后使用：
gpustat -cp
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
