行人重识别：基于度量学习的行人重识别算法

%cd ~/work/metric_learning/data
!tar -xf ~/data/data1884/Market-1501-v15.09.15.tar
!mv Market-1501-v15.09.15 Market-1501

%cd ~import osimport random
imgs = os.listdir('work/metric_learning/data/Market-1501/gt_bbox')
imgs.sort()
image_id = 1super_class_id = 1class_id = 1class_dict = {}for img in imgs:    if 'jpg' in img:
        person_id = img[:4]        if person_id not in class_dict:
            class_dict[person_id] = class_id
            class_id += 1total = []for img in imgs:    if 'jpg' in img:
        person_id = img[:4]
        path = 'gt_bbox/'+img
        line = '%s %s %s %s\n' % (image_id, class_dict[person_id], super_class_id, path)
        image_id += 1
        total.append(line)# random.shuffle(total)train = total[:-1014]
dev = total[-1014:]with open('work/metric_learning/data/Market-1501/train.txt', 'w', encoding='UTF-8') as f:
    f.write('image_id class_id super_class_id path\n')    for line in train:
        f.write(line)    
with open('work/metric_learning/data/Market-1501/test.txt', 'w', encoding='UTF-8') as f:
    f.write('image_id class_id super_class_id path\n')    for line in dev:
        f.write(line)

%cd ~/work/metric_learning/pretrained_model

!wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_v2_pretrained.tar

!tar -xf ResNet50_vd_ssld_v2_pretrained.tar

%cd ~/work/metric_learning

!python train_elem.py \
    --model ResNet50_vd \
    --embedding_size 128 \
    --train_batch_size 256 \
    --test_batch_size 256 \
    --image_shape 3,128,128 \
    --class_dim 1421 \
    --lr 0.1 \
    --lr_strategy piecewise_decay \
    --lr_steps 5000,7000,9000 \
    --total_iter_num 10000 \
    --display_iter_step 10 \
    --test_iter_step 500 \
    --save_iter_step 500 \
    --use_gpu True \
    --pretrained_model pretrained_model/ResNet50_vd_ssld_v2_pretrained \
    --model_save_dir save_elem_model \
    --loss_name arcmargin \
    --arc_scale 80.0 \
    --arc_margin 0.15 \
    --arc_easy_margin False

%cd ~/work/metric_learning

!python train_pair.py \
    --model ResNet50_vd \
    --embedding_size 128 \
    --train_batch_size 64 \
    --test_batch_size 64 \
    --image_shape 3,128,128 \
    --class_dim 1421 \
    --lr 0.0001 \
    --lr_strategy piecewise_decay \
    --lr_steps 3000,6000,9000 \
    --total_iter_num 10000 \
    --display_iter_step 10 \
    --test_iter_step 500 \
    --save_iter_step 500 \
    --use_gpu True \
    --pretrained_model save_elem_model/ResNet50_vd/10000 \
    --model_save_dir save_pair_model \
    --loss_name eml \
    --samples_each_class 4 \
    --margin 0.1 \
    --npairs_reg_lambda 0.01

%cd ~/work/metric_learning# arcmargin未微调模型!python eval.py \
    --model ResNet50_vd \
    --embedding_size 128 \
    --batch_size 256 \
    --image_shape 3,128,128 \
    --use_gpu True \
    --pretrained_model pretrained_model/best_model_no_finetune

%cd ~/work/metric_learning

!python infer.py \
    --model ResNet50_vd \
    --embedding_size 128 \
    --batch_size 1 \
    --image_shape 3,128,128 \
    --use_gpu True \
    --pretrained_model pretrained_model/best_model_no_finetune

%cd ~/work/metric_learning

!python export_model.py \
    --model ResNet50_vd \
    --embedding_size 128 \
    --image_shape 3,128,128 \
    --use_gpu True \
    --pretrained_model=pretrained_model/best_model_no_finetune \
    --model_save_dir=save_inference_model