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行人重识别(ReID) ——基于MGN-pytorch进行可视化展示

pytorch教程  2023-03-08 13:344790
https://github.com/seathiefwang/MGN-pytorch

下载Market1501数据集：http://www.liangzheng.org/Project/project_reid.html

模型训练，修改demo.sh，将 --datadir修改已下载的Market1501数据集地址，将修改CUDA_VISIBLE_DEVICES=2,3自己的GPU设备ID，将修改--nGPU自己的GPU数量。

部分demo.sh示例：

#mAP: 0.9204 rank1: 0.9469 rank3: 0.9664 rank5: 0.9715 rank10: 0.9780 (Best: 0.9204 @epoch 4)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 160 --decay_type step_120_140 --loss 1*CrossEntropy+2*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_adam --nGPU 2  --lr 2e-4 --optimizer ADAM

CUDA_VISIBLE_DEVICES=0 python main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 8 --batchtest 16 --test_every 40 --epochs 160 --decay_type step_120_140 --loss 1*CrossEntropy+2*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_adam --nGPU 1  --lr 2e-4 --optimizer ADAM --save_models

CUDA_VISIBLE_DEVICES=0 python main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 8 --batchtest 8 --test_every 10 --epochs 10 --decay_type step_120_140 --loss 1*CrossEntropy+2*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_adam --nGPU 1  --lr 2e-4 --optimizer ADAM --test_only --resume -1 --load MGN_adam


#mAP: 0.9094 rank1: 0.9388 rank3: 0.9596 rank5: 0.9659 rank10: 0.9748 (Best: 0.9094 @epoch 4)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 160 --decay_type step_120_140 --loss 1*CrossEntropy+1*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_adam_1 --nGPU 2  --lr 1e-4 --optimizer ADAM

#mAP: 0.9217 rank1: 0.9460 rank3: 0.9653 rank5: 0.9706 rank10: 0.9801 (Best: 0.9217 @epoch 4)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 160 --decay_type step_120_140 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --re_rank --random_erasing --save MGN_adam_margin_1.2 --nGPU 2  --lr 2e-4 --optimizer ADAM

#mAP: 0.8986 rank1: 0.9356 rank3: 0.9567 rank5: 0.9620 rank10: 0.9727 (Best: 0.8986 @epoch 4)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 160 --decay_type step_120_140 --loss 1*CrossEntropy+2*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_adamax --nGPU 2  --lr 2e-4 --optimizer ADAMAX

#mAP: 0.5494 rank1: 0.7058 rank3: 0.7696 rank5: 0.8023 rank10: 0.8432 (Best: 0.5494 @epoch 4)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 160 --decay_type step_80_120 --loss 1*CrossEntropy+1*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_sgd --nGPU 2 --lr 1e-2 --optimizer SGD 

#mAP: 0.8480 rank1: 0.9008 rank3: 0.9317 rank5: 0.9436 rank10: 0.9555 (Best: 0.8480 @epoch 3)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 120 --decay_type step_60_80 --loss 1*CrossEntropy+1*Triplet --margin 0.3 --re_rank --random_erasing --save MGN_sgd_1 --nGPU 2 --lr 1e-2 --optimizer SGD 

#mAP: 0.8455 rank1: 0.9032 rank3: 0.9350 rank5: 0.9433 rank10: 0.9537 (Best: 0.8455 @epoch 3)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 40 --epochs 120 --decay_type step_60_80 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --re_rank --random_erasing --save MGN_sgd_2 --nGPU 2 --lr 1e-2 --optimizer SGD 

#mAP: 0.8979 rank1: 0.9376 rank3: 0.9569 rank5: 0.9623 rank10: 0.9745 (Best: 0.8979 @epoch 200)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 50 --epochs 200 --decay_type step_130_170 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --re_rank --random_erasing --save sgd_1 --nGPU 2 --lr 1e-2 --optimizer SGD --reset

#mAP: 0.8053 rank1: 0.9228 rank3: 0.9581 rank5: 0.9676 rank10: 0.9804 (Best: 0.8054 @epoch 190)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --reset --batchid 16 --batchtest 32 --test_every 10 --epochs 200 --decay_type step_240_250 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --save sgd_2 --nGPU 2 --lr 1e-2 --optimizer SGD --save_models --random_erasing --reset

#mAP: 0.8251 rank1: 0.9353 rank3: 0.9679 rank5: 0.9783 rank10: 0.9866 (Best: 0.8251 @epoch 200)
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --reset --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 10 --epochs 200 --decay_type step_240_250 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --random_erasing --save adam_1 --nGPU 2  --lr 2e-4 --optimizer ADAM --save_models

#mAP: 0.9097 rank1: 0.9442 rank3: 0.9614 rank5: 0.9679 rank10: 0.9751
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 100 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --save sgd_3 --nGPU 2 --lr 1e-2 --optimizer SGD --save_models --random_erasing --reset --re_rank

#mAP: 0.9353 rank1: 0.9534 rank3: 0.9706 rank5: 0.9768 rank10: 0.9849
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 100 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_2 --nGPU 2  --lr 2e-4 --optimizer ADAM --save_models --random_erasing --reset --re_rank

#mAP: 0.9174 rank1: 0.9433 rank3: 0.9617 rank5: 0.9679 rank10: 0.9754
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 20 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --save sgd_3 --nGPU 2 --lr 1e-2 --optimizer SGD --random_erasing --reset --re_rank --nesterov

#mAP: 0.9376 rank1: 0.9558 rank3: 0.9712 rank5: 0.9765 rank10: 0.9816
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 100 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_3 --nGPU 2  --lr 2e-4 --optimizer ADAM --random_erasing --reset --re_rank --amsgrad

CUDA_VISIBLE_DEVICES=0 python main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 8 --batchtest 32 --test_every 100 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_3 --nGPU 1  --lr 2e-4 --optimizer ADAM --random_erasing --reset --re_rank --amsgrad

CUDA_VISIBLE_DEVICES=0 python main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 20 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_3 --nGPU 1  --lr 2e-4 --optimizer ADAM --random_erasing --re_rank --load adam_3 --test_only --resume -1


#mAP: 0.9323 rank1: 0.9513 rank3: 0.9700 rank5: 0.9745 rank10: 0.9813
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 100 --epochs 300 --decay_type step_250_290 --loss 1*CrossEntropy+2*Triplet --margin 0.3 --save adam_1 --nGPU 2  --lr 2e-4 --optimizer ADAM --random_erasing --reset --re_rank --amsgrad

#mAP: 0.9270 rank1: 0.9510 rank3: 0.9691 rank5: 0.9751 rank10: 0.9810
#CUDA_VISIBLE_DEVICES=2,3 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 50 --epochs 500 --decay_type step_300_420 --loss 1*CrossEntropy+1*Triplet --margin 1.2 --pool avg --save sgd_1 --nGPU 2 --lr 1e-2 --optimizer SGD --random_erasing --reset --re_rank --nesterov

#0.9383 rank1: 0.9578 rank3: 0.9721 rank5: 0.9783 rank10: 0.9843 (Best: 0.9383 @epoch 400)
#CUDA_VISIBLE_DEVICES=1 python3 main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 50 --epochs 400 --decay_type step_320_380 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_1 --nGPU 1  --lr 2e-4 --optimizer ADAM --random_erasing --reset --re_rank --amsgrad

CUDA_VISIBLE_DEVICES=0 python main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 50 --epochs 400 --decay_type step_320_380 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_1 --nGPU 1  --lr 2e-4 --optimizer ADAM --random_erasing --reset --re_rank --amsgrad

CUDA_VISIBLE_DEVICES=0 python main.py --datadir /home/hylink/eclipse-workspace/Market/ --batchid 16 --batchtest 32 --test_every 50 --epochs 400 --decay_type step_320_380 --loss 1*CrossEntropy+2*Triplet --margin 1.2 --save adam_1 --nGPU 1  --lr 2e-4 --optimizer ADAM --random_erasing --re_rank --load adam_1 --test_only --resume -1

修改trainer.py

import os
import torch
import numpy as np
import utils.utility as utility
from scipy.spatial.distance import cdist
from utils.functions import cmc, mean_ap
from utils.re_ranking import re_ranking
import MyUtil
import scipy.io
import matplotlib
import matplotlib.pyplot as plt

class Trainer():
    def __init__(self, args, model, loss, loader, ckpt):
        self.args = args
        self.train_loader = loader.train_loader
        self.test_loader = loader.test_loader
        self.query_loader = loader.query_loader
        self.testset = loader.testset
        self.queryset = loader.queryset

        self.ckpt = ckpt
        self.model = model
        self.loss = loss
        self.lr = 0.
        self.optimizer = utility.make_optimizer(args, self.model)
        self.scheduler = utility.make_scheduler(args, self.optimizer)
        self.device = torch.device('cpu' if args.cpu else 'cuda')

        if args.load != '':
            self.optimizer.load_state_dict(
                torch.load(os.path.join(ckpt.dir, 'optimizer.pt'))
            )
            for _ in range(len(ckpt.log)*args.test_every): self.scheduler.step()

    def train(self):
        self.scheduler.step()
        self.loss.step()
        epoch = self.scheduler.last_epoch + 1
        lr = self.scheduler.get_lr()[0]
        if lr != self.lr:
            self.ckpt.write_log('[INFO] Epoch: {}\tLearning rate: {:.2e}'.format(epoch, lr))
            self.lr = lr
        self.loss.start_log()
        self.model.train()

        for batch, (inputs, labels) in enumerate(self.train_loader):
            inputs = inputs.to(self.device)
            labels = labels.to(self.device)

            self.optimizer.zero_grad()
            outputs = self.model(inputs)
            loss = self.loss(outputs, labels)
            loss.backward()
            self.optimizer.step()

            self.ckpt.write_log('\r[INFO] [{}/{}]\t{}/{}\t{}'.format(
                epoch, self.args.epochs,
                batch + 1, len(self.train_loader),
                self.loss.display_loss(batch)), 
            end='' if batch+1 != len(self.train_loader) else '\n')

        self.loss.end_log(len(self.train_loader))

    def test(self):
        epoch = self.scheduler.last_epoch + 1
        self.ckpt.write_log('\n[INFO] Test:')
        self.model.eval()

        self.ckpt.add_log(torch.zeros(1, 5))
        qf = self.extract_feature(self.query_loader).numpy()
        gf = self.extract_feature(self.test_loader).numpy()

        if self.args.re_rank:
            q_g_dist = np.dot(qf, np.transpose(gf))
            q_q_dist = np.dot(qf, np.transpose(qf))
            g_g_dist = np.dot(gf, np.transpose(gf))
            dist = re_ranking(q_g_dist, q_q_dist, g_g_dist)
        else:
            dist = cdist(qf, gf)
        r = cmc(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras,
                separate_camera_set=False,
                single_gallery_shot=False,
                first_match_break=True)
        m_ap = mean_ap(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras)

        self.ckpt.log[-1, 0] = m_ap
        self.ckpt.log[-1, 1] = r[0]
        self.ckpt.log[-1, 2] = r[2]
        self.ckpt.log[-1, 3] = r[4]
        self.ckpt.log[-1, 4] = r[9]
        best = self.ckpt.log.max(0)
        self.ckpt.write_log(
            '[INFO] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f} (Best: {:.4f} @epoch {})'.format(
            m_ap,
            r[0], r[2], r[4], r[9],
            best[0][0],
            (best[1][0] + 1)*self.args.test_every
            )
        )
        print(not self.args.test_only)
        if not self.args.test_only:
            self.ckpt.save(self, epoch, is_best=((best[1][0] + 1)*self.args.test_every == epoch))

    def fliphor(self, inputs):
        inv_idx = torch.arange(inputs.size(3)-1,-1,-1).long()  # N x C x H x W
        return inputs.index_select(3,inv_idx)

    def extract_feature(self, loader):
        features = torch.FloatTensor()
        for (inputs, labels) in loader:
            ff = torch.FloatTensor(inputs.size(0), 2048).zero_()
            for i in range(2):
                if i==1:
                    inputs = self.fliphor(inputs)
                input_img = inputs.to(self.device)
                outputs = self.model(input_img)
                f = outputs[0].data.cpu()
                ff = ff + f

            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)
            ff = ff.div(fnorm.expand_as(ff))

            features = torch.cat((features, ff), 0)
        return features

    def terminate(self):
        if self.args.test_only:
            self.test()
            return True
        else:
            epoch = self.scheduler.last_epoch + 1
            return epoch >= self.args.epochs


    def mytest(self):

        gallery_cam,gallery_label = MyUtil.get_id(self.testset.imgs)
        #print(gallery_label)
        query_cam,query_label = MyUtil.get_id(self.queryset.imgs)  
        #print(gallery_cam,gallery_label)
        self.ckpt.write_log('\n[INFO] MyTest:')
        self.model.eval()

        self.ckpt.add_log(torch.zeros(1, 5))
       
        gf = self.extract_feature(self.test_loader).numpy()
        qf = self.extract_feature(self.query_loader).numpy()
         
        result = {'gallery_f':gf,'gallery_label':gallery_label,'gallery_cam':gallery_cam,'query_f':qf,'query_label':query_label,'query_cam':query_cam}
        scipy.io.savemat('pytorch_result.mat',result)
        self.ckpt.write_log('\n[OVER]')



    def mydemo(self):
        result = scipy.io.loadmat('pytorch_result.mat')
        query_feature = torch.FloatTensor(result['query_f'])
        query_cam = result['query_cam'][0]
        query_label = result['query_label'][0]
        gallery_feature = torch.FloatTensor(result['gallery_f'])
        gallery_cam = result['gallery_cam'][0]
        gallery_label = result['gallery_label'][0]
        
        query_feature = query_feature.cuda()
        gallery_feature = gallery_feature.cuda()
       
        i = 2
        index = MyUtil.sort_img(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
        
        # Visualize the rank result
        query_path = self.queryset.imgs[i]
        query_label = query_label[i]
        print(query_path)
        print('Top 10 images are as follow:')
        try: # Visualize Ranking Result 
            # Graphical User Interface is needed
            fig = plt.figure(figsize=(16,4))
            ax = plt.subplot(1,11,1)
            ax.axis('off')
            MyUtil.imshow(query_path,'query')
            for i in range(10):
                ax = plt.subplot(1,11,i+2)
                ax.axis('off')
                img_path = self.testset.imgs[index[i]]
                label = gallery_label[index[i]]
                MyUtil.imshow(img_path)
                if label == query_label:
                    ax.set_title('%d'%(i+1), color='green')
                else:
                    ax.set_title('%d'%(i+1), color='red')
                print(img_path)
        except RuntimeError:
            print('If you want to see the visualization of the ranking result, graphical user interface is needed.')
        
        fig.savefig("show.png")

修改main.py

import data
import loss
import torch
import model
from trainer import Trainer

from option import args
import utils.utility as utility
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

ckpt = utility.checkpoint(args)

loader = data.Data(args)
model = model.Model(args, ckpt)
loss = loss.Loss(args, ckpt) if not args.test_only else None
trainer = Trainer(args, model, loss, loader, ckpt)

'''
n = 0
while not trainer.terminate():
	n += 1
	trainer.train()
	if args.test_every!=0 and n%args.test_every==0:
		trainer.test()
'''
#trainer.mytest()
trainer.mydemo()

新建MyUtil.py

'''
Created on 2018年10月30日

@author: hylink
'''
import argparse
import scipy.io
import torch
import numpy as np
import os
from torchvision import datasets
import matplotlib
import matplotlib.pyplot as plt

def get_id(img_path):
    camera_id = []
    labels = []
    for path in img_path:
        #filename = path.split('/')[-1]
        filename = os.path.basename(path)
        label = filename[0:4]
        camera = filename.split('c')[1]
        if label[0:2]=='-1':
            labels.append(-1)
        else:
            labels.append(int(label))
        camera_id.append(int(camera[0]))
    return camera_id, labels

def imshow(path, title=None):
        """Imshow for Tensor."""
        im = plt.imread(path)
        plt.imshow(im)
        if title is not None:
            plt.title(title)
        plt.pause(0.001)  # pause a bit so that plots are updated 
        
 # sort the images
def sort_img(qf, ql, qc, gf, gl, gc):
    query = qf.view(-1,1)
    # print(query.shape)
    score = torch.mm(gf,query)
    score = score.squeeze(1).cpu()
    score = score.numpy()
    # predict index
    index = np.argsort(score)  #from small to large
    index = index[::-1]
    # index = index[0:2000]
    # good index
    query_index = np.argwhere(gl==ql)
    #same camera
    camera_index = np.argwhere(gc==qc)

    good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)
    junk_index1 = np.argwhere(gl==-1)
    junk_index2 = np.intersect1d(query_index, camera_index)
    junk_index = np.append(junk_index2, junk_index1) 

    mask = np.in1d(index, junk_index, invert=True)
    index = index[mask]
    return index

自定义检索结果集
行人重识别(ReID) ——基于MGN-pytorch进行可视化展示
查询结果集
行人重识别(ReID) ——基于MGN-pytorch进行可视化展示

注意：检索结果集的摄像头ID与查询结果集的摄像头ID必须不同，因为：可视化时自动过滤相同摄像头ID的图片

新建检索与查询底库
trainer.mytest()

检索效果测试
修改trainer.py 168行的i值
运行trainer.mydemo()

效果展示
行人重识别(ReID) ——基于MGN-pytorch进行可视化展示

Top 10 images are as follow:
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165107_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165100_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165114_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165106_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165115_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165105_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165058_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165113_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165059_01.jpg
/home/hylink/eclipse-workspace/reID/Market//bounding_box_test/0010_c1s1_165104_01.jpg

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