- An awesome project for person re-id.
- This project contains adequate support for
log recording,loss monitoringandvisualization ranked images. - This is the pytorch implementation the paper Joint multi-scale discrimination and region segmentation for person re-ID.
- This repository is for person re-id including supervised learning, unsupervised learning and occluded person re-id task. You can utilize this code to learn how to make
person re-idtasks. - For HJL, this is my name initials -- Huang Jialiang.
- I has estabilished the structure containing the introduction of person re-id models like
PCB,MGNandMDRS(my paper),PGFA,PyramidandHOReID. These structures are all reproduced by myself in this code framework. - If you have any quesions, please contact me by my email.
- My emails is: nickhuang1996@126.com.
These models are all reproduced by myself through this code framework.
- PCB [ECCV2018] Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
- MGN [ACM Multimedia 2018] Learning Discriminative Features with Multiple Granularities for Person Re-Identification
- PGFA [ICCV2019] Pose-Guided Feature Alignment for Occluded Person Re-Identification
- Pyramid [CVPR2019] Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
- HOReID [CVPR2020] High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
If you find our work useful, please kindly cite our paper:
@article{huang2020MDRS,
title={Joint multi-scale discrimination and region segmentation for person re-ID},
author={Huang, Jialiang and Lio, bo and Fu, lihua},
journal={Pattern Recognition Letters},
year={2020}
}
- Name: Joint multi-scale discrimination and region segmentation for person re-ID
- Journal: Pattern Recognition Letters
- Volume 138, October 2020, Pages 540-547
- JCR: Q2
- web of science
- Re-ID examples of MDRS on Market-1501 dataset. The retrieved images are all from the gallery set with similarity scores shown above each image. Images with similarity scores in red are negative results. The introduction of multi-scale discriminative feature extraction and region segmentation boost the Re-ID performance.
- Python >= 3.6
- Pytorch >= 1.0.0
- Numpy
- tqdm
- scipy
- torchvision==0.2.1
- Main structure illustrates the data structure for person re-id.
- Download link: ReID_dataset
- Pwd: eseu
- Download link: ReID_dataset
bounding_box_train_camstyleare supposed to be in right place for unsupervised learning.
${project_dir}/ReID_dataset
market1501
Market-1501-v15.09.15 # Extracted from Market-1501-v15.09.15.zip, http://www.liangzheng.org/Project/project_reid.html
Market-1501-v15.09.15_ps_label
bounding_box_train_duke_style
duke
bounding_box_train_market1501_style
DukeMTMC-reID # Extracted from DukeMTMC-reID.zip, https://github.com/layumi/DukeMTMC-reID_evaluation
DukeMTMC-reID_ps_label
Occluded_Duke
cuhk03_np_detected_jpg
cuhk03-np # Extracted from cuhk03-np.zip, https://pan.baidu.com/s/1RNvebTccjmmj1ig-LVjw7A
cuhk03-np-jpg # Created from code
cuhk03-np-jpg_ps_label
${project_dir}/ReID_dataset/market1501
Market-1501-v15.09.15 # Extracted from Market-1501-v15.09.15.zip, http://www.liangzheng.org/Project/project_reid.html
bounding_box_test
bounding_box_train
bounding_box_train_camstyle # unsupervised learning
bounding_box_train_resize # 256×256
gt_bbox
gt_query
pytorch
gallery
multi-query
query
train
train_all
val
query
Market-1501-v15.09.15_ps_label # segmentation label
bounding_box_test
bounding_box_train
gt_bbox
query
bounding_box_train_duke_style
${project_dir}/ReID_dataset/duke
bounding_box_train_market1501_style
DukeMTMC-reID # Extracted from DukeMTMC-reID.zip, https://github.com/layumi/DukeMTMC-reID_evaluation
bounding_box_test
bounding_box_train
bounding_box_train_camstyle # unsupervised learning
pytorch
gallery
query
train
train_all
val
query
DukeMTMC-reID_ps_label # segmentation label
bounding_box_test
bounding_box_train
gt_bbox
query
Occluded_Duke
bounding_box_test
bounding_box_train
heatmaps # .npy files
bounding_box_test
bounding_box_train
processed_data
gallery
query
train
processed_data # person ID is folder name
gallery
query
train
query
test_pose_storage
gallery
sep-json
alphapose-results.json
gallery.list
show.py
query
sep-json
1.list
0184_c4_f0056533.jpg
0184_c4_f0056533a.jpg
alphapose-results.json
show.py
show1.py
${project_dir}/ReID_dataset/cuhk03_np_detected_jpg
cuhk03-np # Extracted from cuhk03-np.zip, https://pan.baidu.com/s/1RNvebTccjmmj1ig-LVjw7A
datected
bounding_box_test
bounding_box_train
pytorch
gallery
query
train
train_all
val
query
labeled
bounding_box_test
bounding_box_train
query
cuhk03-np-jpg # Created from code
detected
bounding_box_test
bounding_box_train
pytorch
gallery
query
train
train_all
val
query
labeled
bounding_box_test
bounding_box_train
pytorch
gallery
query
train
train_all
val
query
cuhk03-np-jpg_ps_label
bounding_box_test
bounding_box_train
query
- Download link: weights_results
- Pwd: ei5o
- Download link: weights_results
- Pre-trained Pose Model pose_hrnet_w48_256x192.pth
is supposed to be set into path
${project_dir}/weights_results/HOReID/pre-trained/pose_model/pose_hrnet_w48_256x192.pthso that runHOReIDtask.
| Name |
|---|
| ResNet50 |
| MDRS_ADAM_random_erasing_margin_0.3_market_best |
| MDRS_ADAM_random_erasing_margin_0.3_duke_best |
| MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best |
- Pretrained models(ResNet50) restores
backboneweights. HJL-ReIDis the project name, where three datasets results and weights are produced after training.MDRS_feat_dict.matrestores gallery and query data features by extracted from trained models for evaluations.- You are supposed to download and save these files according to the following structure:
tensorboardanddefault_config.pywill be automatically created if you run codes.
${project_dir}/weights_results
pretrained_models
resnet50-19c8e357.pth
HJL-ReID
MDRS_ADAM_random_erasing_margin_0.3_market_best
tensorboard
ckpt.pth
default_config.py
MDRS_feat_dict.mat
MDRS_ADAM_random_erasing_margin_0.3_duke_best
...
MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best
...
...
- if you want to command, followed by:
python demo.py --model_name MDRS --exp_dir ${project_dir}/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best --default_config_path $(project_dir)/HJL-ReID/MDRSREID/Settings/config/default_config.py --ow_config_path $(project_dir)/HJL-ReID/MDRSREID/Settings/config/overwrite_config/MDRS_config_ADAM_best_market1501.txt --ow_str "cfg.dataset.train.name = 'market1501'"
- or if you have IDE like
pycharm, you can just modifyMDRSREID/parser_args/parser_args.py - You just modify
--exp_dirand--ow_config_path. Then rundemo.py.
| dataset | exp_dir | ow_config_path |
|---|---|---|
| market | ${project_dir}/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best | $(project_dir)/HJL-ReID/MDRSREID/Settings/config/overwrite_config/MDRS_config_ADAM_best_market1501.txt |
| duke | ${project_dir}/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best | $(project_dir)/HJL-ReID/MDRSREID/Settings/config/overwrite_config/MDRS_config_ADAM_best_duke.txt |
| cuhk03 | ${project_dir}/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best | $(project_dir)/HJL-ReID/MDRSREID/Settings/config/overwrite_config/MDRS_config_ADAM_best_cuhk03_jpg.txt |
- If you want to test, just modify
cfg.only_test = Falsetocfg.only_test = Truein./MDRSREID/Settings/config/overwrite_config/${config_file}
Overwrite config files can be found in: ./MDRSREID/Settings/config/overwrite_config/
| config_file |
|---|
| MDRS_config_ADAM_best_market1501.txt |
| MDRS_config_ADAM_best_duke.txt |
| MDRS_config_ADAM_best_cuhk_jpg.txt |
| ... |
- e.g:
import argparse
def parser_args():
"""
:argument:
--exp_dir
--default_config_path
--ow_config_path
--ow_str
:return:
"""
parser = argparse.ArgumentParser()
parser.add_argument('--model_name',
type=str,
default='MDRS',
help='[Optional] Model Name for experiment directory in current directory if exp_dir is None')
parser.add_argument('--exp_dir',
type=str,
default='D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best',
help='[Optional] Directory to store experiment output, '
'including log files and model checkpoint, etc.')
parser.add_argument('--default_config_path',
type=str,
default='D:/Pycharm_Project/HJL-ReID/MDRSREID/Settings/config/default_config.py',
help='A configuration file.')
parser.add_argument('--ow_config_path',
type=str,
default='D:/Pycharm_Project/HJL-ReID/MDRSREID/Settings/config/overwrite_config/MDRS_config_ADAM_best_market1501.txt',
help='[Optional] A text file, each line being an item to overwrite the cfg_file.')
parser.add_argument('--ow_str',
type=str,
default='cfg.dataset.train.name = \'market1501\'',
help="""[Optional] Items to overwrite the cfg_file.
E.g. "cfg.dataset.train.name = \'market1501\''; cfg.model.em_dim = 256" """)
args, _ = parser.parse_known_args()
return args
- For saving time on feature extraction, I have provided three
MDRS_feat_dict.matinweights_results. You can use thesefeat_dict.matto test models. - Download link：weights_results
- These
feat_dict.matfiles are for testing so please avoid them being rewritten if you setcfg.only_test = False.
The stdout files as below：
=> Experiment Output Directory: D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best
[PYTORCH VERSION]: 1.0.1
keypoints_model :
test :
model_file : D:/weights_results/HOReID/pre-trained/pose_model/pose_hrnet_w48_256x192.pth
is_train : False
name : pose_hrnet
norm_scale : 10.0
num_joints : 17
extra :
pretrained_layers : ['*']
stem_inplanes : 64
stage2 :
num_modules : 1
num_branches : 2
num_blocks : [4, 4]
num_channels : [48, 96]
block : BASIC
fuse_method : SUM
stage3 :
num_modules : 4
num_branches : 3
num_blocks : [4, 4, 4]
num_channels : [48, 96, 192]
block : BASIC
fuse_method : SUM
stage4 :
num_modules : 3
num_branches : 4
num_blocks : [4, 4, 4, 4]
num_channels : [48, 96, 192, 384]
block : BASIC
fuse_method : SUM
final_conv_kernel : 1
init_weights : True
pretrained :
joints_groups : [[0, 1, 2, 3, 4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]]
branch_num : 14
weight_global_feature : 1.0
only_test : True
only_infer : False
model :
name : MDRS
backbone :
name : resnet50
last_conv_stride : 2
pretrained : True
pretrained_model_dir : D:/weights_results/pretrained_model/imagenet_model
pool_type : MDRSPool
max_or_avg : max
em_dim : 256
num_parts : 1
use_ps : True
reduction :
use_relu : False
use_leakyrelu : False
use_dropout : False
PGFA :
global_input_dim : 4096
part_feat_input_dim : 2048
multi_seg :
mid_c : 256
mid_c2 : 512
in_c1 : 2048
in_c2 : 1024
num_classes : 8
gcn :
scale : 20.0
num_classes : 751
dataset :
root : D:/datasets/ReID_dataset
use_occlude_duke : False
im :
h_w : [384, 128]
mean : [0.486, 0.459, 0.408]
std : [0.229, 0.224, 0.225]
interpolation : None
pad : 10
random_erasing :
epsilon : 0.5
proportion : True
random_crop :
output_size : [256, 128]
use_pose_landmark_mask : False
pose_landmark_mask :
h_w : [24, 8]
type : PL_18P
use_ps_label : True
ps_label :
h_w : [48, 16]
pad : 1
train :
before_to_tensor_transform_list : ['hflip', 'resize']
after_to_tensor_transform_list : ['random_erasing']
type : Supervised
source :
name : market1501
authority : train
target :
name : market1501
authority : train
cd_train :
name : duke
authority : train
transform_list : ['hflip', 'resize']
test :
names : ['market1501']
before_to_tensor_transform_list : ['resize']
after_to_tensor_transform_list : None
model_flow : train
stage : FeatureExtract
dataloader :
num_workers : 2
train :
batch_type : pk2
batch_size : 32
batch_id : 8
batch_image : 4
drop_last : True
cd_train :
batch_type : random
batch_size : 32
drop_last : True
test :
batch_type : seq
batch_size : 16
drop_last : False
pk :
k : 4
pk2 :
eval :
forward_type : reid
chunk_size : 1000
re_rank : False
separate_camera_set : False
single_gallery_shot : False
first_match_break : True
score_prefix :
ranked_images : False
ver_in_scale : 10.0
feat_dict :
use : True
reproduction : False
device : cuda
train :
id_loss :
name : idL
weight : 1
use : True
id_smooth_loss :
name : idSmoothL
weight : 0
use : False
epsilon : 0.1
reduce : True
use_gpu : True
tri_loss :
name : triL
weight : 2
use : True
margin : 0.3
dist_type : euclidean
hard_type : tri_hard
norm_by_num_of_effective_triplets : False
tri_hard_loss :
name : triHardL
weight : 0
use : False
margin : 0.3
dist_type : euclidean
permutation_loss :
name : permutationL
weight : 0
use : False
branch_num : 14
verification_loss :
name : verL
weight : 0
use : False
src_multi_seg_loss :
name : psL
weight : 1
use : False
normalize_size : True
num_classes : 8
src_multi_seg_gp_loss :
name : psL
weight : 0
use : False
normalize_size : True
num_classes : 8
pgfa_loss :
name : pgfaL
weight : 0
use : False
lamb : 0.2
inv_loss :
name : InvL
num_features : 2048
weight : 0
use : False
beta : 0.05
alpha : 0.01
knn : 6
lmd : 0.3
analyze_computer :
name : analyze_computer
use : False
verification_probability_analyze :
name : verification_probability_analyze
use : False
log :
use_tensorboard : True
only_base_lr : False
exp_dir : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best
ckpt_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best\ckpt.pth
score_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best\score_2020-09-19_14-16-16.txt
optim :
optimizer : adam
sgd :
momentum : 0.9
nesterov : False
weight_decay : 0.0005
adam :
beta1 : 0.9
beta2 : 0.99
eps : 1e-08
amsgrad : False
weight_decay : 0.0005
seperate_params : False
base_lr : 0.00035
ft_lr : 0.0002
new_params_lr : 0.0002
gcn_lr_scale : 0.1
gm_lr_scale : 1.0
ver_lr_scale : 1.0
every_lr_epoch : 40
easy_epochs : 60
lr_decay_epochs : [320, 360, 400]
normal_epochs : 400
warmup_epochs : 0
warmup : False
warmup_init_lr : 0
milestones : [40, 70]
use_gm_after_epoch : 20
warmup_multi_step_epochs : 120
pretrain_new_params_epochs : 0
pretrain_new_params : False
epochs_per_val : 20
steps_per_log : 5
trial_run : False
phase : normal
resume : False
resume_epoch :
resume_from : pretrained
cft : False
cft_iters : 1
cft_rho : 0.0008
blocksetting :
backbone : 3
pool : 3
reduction : 8
classifier : 8
multi_seg : 3
vis :
use : False
heat_map :
use : False
save_dir : D:/weights_results/HJL-ReID/heat_map
device : cuda
Keys not found in source state_dict:
num_batches_tracked x53
Keys not found in destination state_dict:
fc.weight
fc.bias
=> Loaded ImageNet Model: D:/weights_results/pretrained_model/imagenet_model\resnet50-19c8e357.pth
Model(
(model): MDRS(
(backbone): MDRSBackbone(
(backbone): Sequential(
(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(5): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(6): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(p0): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p1): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p2): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
)
(pool): MDRSPool(
(pool0): AdaptiveMaxPool2d(output_size=(1, 1))
(pool1): AdaptiveMaxPool2d(output_size=(2, 1))
(pool2): AdaptiveMaxPool2d(output_size=(3, 1))
)
(reduction): MDRSReduction(
(reduction): ModuleList(
(0): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(5): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(6): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(7): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(classifier): MDRSClassifier(
(classifier): ModuleList(
(0): Linear(in_features=256, out_features=751, bias=True)
(1): Linear(in_features=256, out_features=751, bias=True)
(2): Linear(in_features=256, out_features=751, bias=True)
(3): Linear(in_features=256, out_features=751, bias=True)
(4): Linear(in_features=256, out_features=751, bias=True)
(5): Linear(in_features=256, out_features=751, bias=True)
(6): Linear(in_features=256, out_features=751, bias=True)
(7): Linear(in_features=256, out_features=751, bias=True)
)
)
(multi_seg): MDRSMultiSeg(
(deconv): ConvTranspose2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv3): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1), output_padding=(2, 2), bias=False)
(deconv3_pool): AdaptiveAvgPool2d(output_size=(24, 8))
(deconv1): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv2): ConvTranspose2d(1024, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1))
)
)
)
Model size: 113.637 M
=> Loaded [model] from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best\ckpt.pth, epoch 440, score:
market1501 -> market1501 [mAP: 87.5%], [cmc1: 95.1%], [cmc5: 98.2%], [cmc10: 98.8%]
check query dataset success!!
check gallery dataset success!!
Test...
Loading feat dict mat from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best/MDRS_feat_dict.mat...
D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_market_best/MDRS_feat_dict.mat has been existed.
market1501 -> market1501 [mAP: 87.5%], [cmc1: 95.1%], [cmc5: 98.2%], [cmc10: 98.8%]
=> Experiment Output Directory: D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best
[PYTORCH VERSION]: 1.0.1
keypoints_model :
test :
model_file : D:/weights_results/HOReID/pre-trained/pose_model/pose_hrnet_w48_256x192.pth
is_train : False
name : pose_hrnet
norm_scale : 10.0
num_joints : 17
extra :
pretrained_layers : ['*']
stem_inplanes : 64
stage2 :
num_modules : 1
num_branches : 2
num_blocks : [4, 4]
num_channels : [48, 96]
block : BASIC
fuse_method : SUM
stage3 :
num_modules : 4
num_branches : 3
num_blocks : [4, 4, 4]
num_channels : [48, 96, 192]
block : BASIC
fuse_method : SUM
stage4 :
num_modules : 3
num_branches : 4
num_blocks : [4, 4, 4, 4]
num_channels : [48, 96, 192, 384]
block : BASIC
fuse_method : SUM
final_conv_kernel : 1
init_weights : True
pretrained :
joints_groups : [[0, 1, 2, 3, 4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]]
branch_num : 14
weight_global_feature : 1.0
only_test : True
only_infer : False
model :
name : MDRS
backbone :
name : resnet50
last_conv_stride : 2
pretrained : True
pretrained_model_dir : D:/weights_results/pretrained_model/imagenet_model
pool_type : MDRSPool
max_or_avg : max
em_dim : 256
num_parts : 1
use_ps : True
reduction :
use_relu : False
use_leakyrelu : False
use_dropout : False
PGFA :
global_input_dim : 4096
part_feat_input_dim : 2048
multi_seg :
mid_c : 256
mid_c2 : 512
in_c1 : 2048
in_c2 : 1024
num_classes : 8
gcn :
scale : 20.0
num_classes : 702
dataset :
root : D:/datasets/ReID_dataset
use_occlude_duke : False
im :
h_w : [384, 128]
mean : [0.486, 0.459, 0.408]
std : [0.229, 0.224, 0.225]
interpolation : None
pad : 10
random_erasing :
epsilon : 0.5
proportion : True
random_crop :
output_size : [256, 128]
use_pose_landmark_mask : False
pose_landmark_mask :
h_w : [24, 8]
type : PL_18P
use_ps_label : True
ps_label :
h_w : [48, 16]
pad : 1
train :
before_to_tensor_transform_list : ['hflip', 'resize']
after_to_tensor_transform_list : ['random_erasing']
type : Supervised
source :
name : duke
authority : train
target :
name : duke
authority : train
cd_train :
name : duke
authority : train
transform_list : ['hflip', 'resize']
test :
names : ['duke']
before_to_tensor_transform_list : ['resize']
after_to_tensor_transform_list : None
model_flow : train
stage : FeatureExtract
dataloader :
num_workers : 2
train :
batch_type : pk2
batch_size : 32
batch_id : 8
batch_image : 4
drop_last : True
cd_train :
batch_type : random
batch_size : 32
drop_last : True
test :
batch_type : seq
batch_size : 16
drop_last : False
pk :
k : 4
pk2 :
eval :
forward_type : reid
chunk_size : 1000
re_rank : False
separate_camera_set : False
single_gallery_shot : False
first_match_break : True
score_prefix :
ranked_images : False
ver_in_scale : 10.0
feat_dict :
use : True
reproduction : False
device : cuda
train :
id_loss :
name : idL
weight : 1
use : True
id_smooth_loss :
name : idSmoothL
weight : 0
use : False
epsilon : 0.1
reduce : True
use_gpu : True
tri_loss :
name : triL
weight : 2
use : True
margin : 0.3
dist_type : euclidean
hard_type : tri_hard
norm_by_num_of_effective_triplets : False
tri_hard_loss :
name : triHardL
weight : 0
use : False
margin : 0.3
dist_type : euclidean
permutation_loss :
name : permutationL
weight : 0
use : False
branch_num : 14
verification_loss :
name : verL
weight : 0
use : False
src_multi_seg_loss :
name : psL
weight : 1
use : False
normalize_size : True
num_classes : 8
src_multi_seg_gp_loss :
name : psL
weight : 0
use : False
normalize_size : True
num_classes : 8
pgfa_loss :
name : pgfaL
weight : 0
use : False
lamb : 0.2
inv_loss :
name : InvL
num_features : 2048
weight : 0
use : False
beta : 0.05
alpha : 0.01
knn : 6
lmd : 0.3
analyze_computer :
name : analyze_computer
use : False
verification_probability_analyze :
name : verification_probability_analyze
use : False
log :
use_tensorboard : True
only_base_lr : False
exp_dir : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best
ckpt_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best\ckpt.pth
score_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best\score_2020-09-19_13-26-20.txt
optim :
optimizer : adam
sgd :
momentum : 0.9
nesterov : False
weight_decay : 0.0005
adam :
beta1 : 0.9
beta2 : 0.99
eps : 1e-08
amsgrad : False
weight_decay : 0.0005
seperate_params : False
base_lr : 0.00035
ft_lr : 0.0002
new_params_lr : 0.0002
gcn_lr_scale : 0.1
gm_lr_scale : 1.0
ver_lr_scale : 1.0
every_lr_epoch : 40
easy_epochs : 60
lr_decay_epochs : [500, 560, 600]
normal_epochs : 600
warmup_epochs : 0
warmup : False
warmup_init_lr : 0
milestones : [40, 70]
use_gm_after_epoch : 20
warmup_multi_step_epochs : 120
pretrain_new_params_epochs : 0
pretrain_new_params : False
epochs_per_val : 20
steps_per_log : 5
trial_run : False
phase : normal
resume : False
resume_epoch :
resume_from : pretrained
cft : False
cft_iters : 1
cft_rho : 0.0008
blocksetting :
backbone : 3
pool : 3
reduction : 8
classifier : 8
multi_seg : 3
vis :
use : False
heat_map :
use : False
save_dir : D:/weights_results/HJL-ReID/heat_map
device : cuda
Keys not found in source state_dict:
num_batches_tracked x53
Keys not found in destination state_dict:
fc.bias
fc.weight
=> Loaded ImageNet Model: D:/weights_results/pretrained_model/imagenet_model\resnet50-19c8e357.pth
Model(
(model): MDRS(
(backbone): MDRSBackbone(
(backbone): Sequential(
(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(5): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(6): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(p0): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p1): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p2): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
)
(pool): MDRSPool(
(pool0): AdaptiveMaxPool2d(output_size=(1, 1))
(pool1): AdaptiveMaxPool2d(output_size=(2, 1))
(pool2): AdaptiveMaxPool2d(output_size=(3, 1))
)
(reduction): MDRSReduction(
(reduction): ModuleList(
(0): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(5): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(6): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(7): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(classifier): MDRSClassifier(
(classifier): ModuleList(
(0): Linear(in_features=256, out_features=702, bias=True)
(1): Linear(in_features=256, out_features=702, bias=True)
(2): Linear(in_features=256, out_features=702, bias=True)
(3): Linear(in_features=256, out_features=702, bias=True)
(4): Linear(in_features=256, out_features=702, bias=True)
(5): Linear(in_features=256, out_features=702, bias=True)
(6): Linear(in_features=256, out_features=702, bias=True)
(7): Linear(in_features=256, out_features=702, bias=True)
)
)
(multi_seg): MDRSMultiSeg(
(deconv): ConvTranspose2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv3): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1), output_padding=(2, 2), bias=False)
(deconv3_pool): AdaptiveAvgPool2d(output_size=(24, 8))
(deconv1): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv2): ConvTranspose2d(1024, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1))
)
)
)
Model size: 113.637 M
=> Loaded [model] from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best\ckpt.pth, epoch 600, score:
duke -> duke [mAP: 79.8%], [cmc1: 89.4%], [cmc5: 94.9%], [cmc10: 96.1%]
Test...
Loading feat dict mat from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best/MDRS_feat_dict.mat...
D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best/MDRS_feat_dict.mat has been existed.
duke -> duke [mAP: 79.8%], [cmc1: 89.4%], [cmc5: 94.9%], [cmc10: 96.1%]
destination path dir D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best has been already exist.
=> Checkpoint Saved to D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_duke_best\ckpt.pth
=> Experiment Output Directory: D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best
[PYTORCH VERSION]: 1.0.1
keypoints_model :
test :
model_file : D:/weights_results/HOReID/pre-trained/pose_model/pose_hrnet_w48_256x192.pth
is_train : False
name : pose_hrnet
norm_scale : 10.0
num_joints : 17
extra :
pretrained_layers : ['*']
stem_inplanes : 64
stage2 :
num_modules : 1
num_branches : 2
num_blocks : [4, 4]
num_channels : [48, 96]
block : BASIC
fuse_method : SUM
stage3 :
num_modules : 4
num_branches : 3
num_blocks : [4, 4, 4]
num_channels : [48, 96, 192]
block : BASIC
fuse_method : SUM
stage4 :
num_modules : 3
num_branches : 4
num_blocks : [4, 4, 4, 4]
num_channels : [48, 96, 192, 384]
block : BASIC
fuse_method : SUM
final_conv_kernel : 1
init_weights : True
pretrained :
joints_groups : [[0, 1, 2, 3, 4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]]
branch_num : 14
weight_global_feature : 1.0
only_test : True
only_infer : False
model :
name : MDRS
backbone :
name : resnet50
last_conv_stride : 2
pretrained : True
pretrained_model_dir : D:/weights_results/pretrained_model/imagenet_model
pool_type : MDRSPool
max_or_avg : max
em_dim : 256
num_parts : 1
use_ps : True
reduction :
use_relu : False
use_leakyrelu : False
use_dropout : False
PGFA :
global_input_dim : 4096
part_feat_input_dim : 2048
multi_seg :
mid_c : 256
mid_c2 : 512
in_c1 : 2048
in_c2 : 1024
num_classes : 8
gcn :
scale : 20.0
num_classes : 0
dataset :
root : D:/datasets/ReID_dataset
use_occlude_duke : False
im :
h_w : [384, 128]
mean : [0.486, 0.459, 0.408]
std : [0.229, 0.224, 0.225]
interpolation : None
pad : 10
random_erasing :
epsilon : 0.5
proportion : True
random_crop :
output_size : [256, 128]
use_pose_landmark_mask : False
pose_landmark_mask :
h_w : [24, 8]
type : PL_18P
use_ps_label : True
ps_label :
h_w : [48, 16]
pad : 1
train :
before_to_tensor_transform_list : ['hflip', 'resize']
after_to_tensor_transform_list : ['random_erasing']
type : Supervised
source :
name : cuhk03_np_detected_jpg
authority : train
target :
name : cuhk03_np_detected_jpg
authority : train
cd_train :
name : duke
authority : train
transform_list : ['hflip', 'resize']
test :
names : ['cuhk03_np_detected_jpg']
before_to_tensor_transform_list : ['resize']
after_to_tensor_transform_list : None
model_flow : train
stage : FeatureExtract
dataloader :
num_workers : 2
train :
batch_type : pk2
batch_size : 32
batch_id : 8
batch_image : 4
drop_last : True
cd_train :
batch_type : random
batch_size : 32
drop_last : True
test :
batch_type : seq
batch_size : 16
drop_last : False
pk :
k : 4
pk2 :
eval :
forward_type : reid
chunk_size : 1000
re_rank : False
separate_camera_set : False
single_gallery_shot : False
first_match_break : True
score_prefix :
ranked_images : False
ver_in_scale : 10.0
feat_dict :
use : True
reproduction : False
device : cuda
train :
id_loss :
name : idL
weight : 1
use : True
id_smooth_loss :
name : idSmoothL
weight : 0
use : False
epsilon : 0.1
reduce : True
use_gpu : True
tri_loss :
name : triL
weight : 2
use : True
margin : 0.3
dist_type : euclidean
hard_type : tri_hard
norm_by_num_of_effective_triplets : False
tri_hard_loss :
name : triHardL
weight : 0
use : False
margin : 0.3
dist_type : euclidean
permutation_loss :
name : permutationL
weight : 0
use : False
branch_num : 14
verification_loss :
name : verL
weight : 0
use : False
src_multi_seg_loss :
name : psL
weight : 1
use : False
normalize_size : True
num_classes : 8
src_multi_seg_gp_loss :
name : psL
weight : 0
use : False
normalize_size : True
num_classes : 8
pgfa_loss :
name : pgfaL
weight : 0
use : False
lamb : 0.2
inv_loss :
name : InvL
num_features : 2048
weight : 0
use : False
beta : 0.05
alpha : 0.01
knn : 6
lmd : 0.3
analyze_computer :
name : analyze_computer
use : False
verification_probability_analyze :
name : verification_probability_analyze
use : False
log :
use_tensorboard : True
only_base_lr : False
exp_dir : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best
ckpt_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best\ckpt.pth
score_file : D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best\score_2020-09-20_16-45-54.txt
optim :
optimizer : adam
sgd :
momentum : 0.9
nesterov : False
weight_decay : 0.0005
adam :
beta1 : 0.9
beta2 : 0.99
eps : 1e-08
amsgrad : False
weight_decay : 0.0005
seperate_params : False
base_lr : 0.00035
ft_lr : 0.0002
new_params_lr : 0.0002
gcn_lr_scale : 0.1
gm_lr_scale : 1.0
ver_lr_scale : 1.0
every_lr_epoch : 40
easy_epochs : 60
lr_decay_epochs : [360, 420, 440]
normal_epochs : 440
warmup_epochs : 0
warmup : False
warmup_init_lr : 0
milestones : [40, 70]
use_gm_after_epoch : 20
warmup_multi_step_epochs : 120
pretrain_new_params_epochs : 0
pretrain_new_params : False
epochs_per_val : 20
steps_per_log : 5
trial_run : False
phase : normal
resume : False
resume_epoch :
resume_from : pretrained
cft : False
cft_iters : 1
cft_rho : 0.0008
blocksetting :
backbone : 3
pool : 3
reduction : 8
classifier : 8
multi_seg : 3
vis :
use : False
heat_map :
use : False
save_dir : D:/weights_results/HJL-ReID/heat_map
device : cuda
Keys not found in source state_dict:
num_batches_tracked x53
Keys not found in destination state_dict:
fc.weight
fc.bias
=> Loaded ImageNet Model: D:/weights_results/pretrained_model/imagenet_model\resnet50-19c8e357.pth
Model(
(model): MDRS(
(backbone): MDRSBackbone(
(backbone): Sequential(
(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(5): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(6): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(p0): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p1): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(p2): Sequential(
(0): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
)
(pool): MDRSPool(
(pool0): AdaptiveMaxPool2d(output_size=(1, 1))
(pool1): AdaptiveMaxPool2d(output_size=(2, 1))
(pool2): AdaptiveMaxPool2d(output_size=(3, 1))
)
(reduction): MDRSReduction(
(reduction): ModuleList(
(0): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(5): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(6): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(7): Sequential(
(0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(multi_seg): MDRSMultiSeg(
(deconv): ConvTranspose2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv3): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1), output_padding=(2, 2), bias=False)
(deconv3_pool): AdaptiveAvgPool2d(output_size=(24, 8))
(deconv1): ConvTranspose2d(2048, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(deconv2): ConvTranspose2d(1024, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1))
)
)
)
Model size: 113.637 M
=> Loaded [model] from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best\ckpt.pth, epoch 440, score:
cuhk03_np_detected_jpg -> cuhk03_np_detected_jpg[mAP: 75.6%], [cmc1: 78.1%], [cmc5: 90.5%], [cmc10: 93.9%]
Test...
Loading feat dict mat from D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best/MDRS_feat_dict.mat...
D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best/MDRS_feat_dict.mat has been existed.
cuhk03_np_detected_jpg -> cuhk03_np_detected_jpg[mAP: 75.6%], [cmc1: 78.1%], [cmc5: 90.5%], [cmc10: 93.9%]
destination path dir D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best has been already exist.
=> Checkpoint Saved to D:/weights_results/HJL-ReID/MDRS_ADAM_random_erasing_margin_0.3_cuhk_jpg_best\ckpt.pth
| Methods | mAP | Rank-1 | Rank-5 | Rank-10 |
|---|---|---|---|---|
| MDRS | 87.6 | 95.8 | 98.4 | 99.1 |
| Pyramid | 88.2 | 95.7 | 98.4 | 99.0 |
| DSA-reID | 87.6 | 95.7 | – | – |
| MGN | 86.9 | 95.7 | – | – |
| PCB+triplet | 83.0 | 93.4 | 97.8 | 98.4 |
| CASN(PCB) | 82.8 | 94.4 | – | – |
| PCB+RPP | 81.6 | 93.8 | 97.5 | 98.5 |
| VPM | 80.8 | 93.0 | 97.8 | 98.8 |
| PCB | 77.4 | 92.3 | 97.2 | 98.2 |
| GLAD | 73.9 | 89.9 | – | – |
| MultiScale | 73.1 | 88.9 | – | – |
| PartLoss | 69.3 | 88.2 | – | – |
| PDC | 63.4 | 84.4 | – | – |
| MultiLoss | 64.4 | 83.9 | – | – |
| PAR | 63.4 | 81.0 | 92.0 | 94.7 |
| HydraPlus | – | 76.9 | 91.3 | 94.5 |
| MultiRegion | 41.2 | 66.4 | 85.0 | 90.2 |
| SPReID | 83.4 | 93.7 | 97.6 | 98.4 |
| AOS | 70.4 | 86.5 | – | – |
| Triplet Loss | 69.1 | 84.9 | 94.2 | – |
| Transfer | 65.5 | 83.7 | – | – |
| PAN | 63.4 | 82.8 | – | – |
| SVDNet | 62.1 | 82.3 | 92.3 | 95.2 |
| Methods | mAP | Rank-1 | Rank-5 | Rank-10 |
|---|---|---|---|---|
| MDRS | 79.4 | 89.4 | 95.1 | 96.8 |
| Pyramid | 79.0 | 89.0 | 94.7 | 96.3 |
| MGN | 78.4 | 88.7 | – | – |
| CASN(PCB) | 73.7 | 87.7 | – | – |
| DSA-reID | 74.3 | 86.2 | – | – |
| SPReID | 73.3 | 86.0 | 93.0 | 94.5 |
| PCB+triplet | 73.2 | 84.1 | 92.4 | 94.5 |
| VPM | 72.6 | 83.6 | 91.7 | 94.2 |
| PCB+RPP | 69.2 | 83.3 | – | – |
| PSE+ECN | 75.7 | 84.5 | – | – |
| DNN + CRF | 69.5 | 84.9 | – | – |
| GP-reid | 72.8 | 85.2 | – | – |
| AOS | 62.1 | 79.2 | – | – |
| – | Labelled | - | - | - | Detected | - | - | - |
|---|---|---|---|---|---|---|---|---|
| Methods | mAP | Rank-1 | Rank-5 | Rank-10 | mAP | Rank-1 | Rank-5 | Rank-10 |
| MDRS | 76.4 | 79.0 | 91.1 | 94.6 | 74.2 | 78.7 | 90.5 | 94.1 |
| Pyramid | 76.9 | 78.9 | 91.0 | 94.4 | 74.8 | 78.9 | 90.7 | 94.5 |
| DSA-reID | 75.2 | 78.9 | – | – | 73.1 | 78.2 | – | – |
| CASN(PCB) | 68.0 | 73.7 | – | – | 64.4 | 71.5 | – | – |
| MGN | 67.4 | 68.0 | – | – | 66.0 | 68.0 | – | – |
| PCB+RPP | – | – | – | – | 57.5 | 63.7 | – | – |
| MLFN | 49.2 | 54.7 | – | – | 47.8 | 52.8 | – | – |
| AOS | – | – | – | – | 43.3 | 47.1 | – | – |
| SVDNet | 37.8 | 40.9 | – | – | 37.3 | 41.5 | – | – |
| PAN | 35.0 | 36.9 | – | – | 34.0 | 36.3 | – | – |
| IDE | 21.0 | 22.2 | – | – | 19.7 | 21.3 | – | – |
- We have provided PGFAReIDTrainer.py, PyramidalReIDTrainer.pyand
HOReIDTrainer.py for
PGFAandHOReID.
- Add instructions of other model
MGN,PCB,PGFA,PyramidandHOReID. - I will consider if
MGNReID.pyandPCBReIDTRainer.pyare necessary.