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main.py
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"""MCJA/main.py
It is the main entry point for training the Multi-level Cross-modality Joint Alignment (MCJA) method.
"""
import os
import glob
import pprint
import logging
import numpy as np
import scipy.io as sio
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from apex import amp
from data import get_train_loader
from data import get_test_loader
from models.mcja import MCJA
from engine import get_trainer
from engine.engine import create_eval_engine
from utils.eval_data import eval_sysu, eval_regdb
def train(cfg):
# Recorder ---------------------------------------------------------------------------------------------------------
logger = logging.getLogger('MCJA')
tb_dir = os.path.join(cfg.log_dir, 'tensorboard')
if not os.path.isdir(tb_dir):
os.makedirs(tb_dir, exist_ok=True)
writer = SummaryWriter(log_dir=tb_dir)
# Train DataLoader -------------------------------------------------------------------------------------------------
train_loader = get_train_loader(dataset=cfg.dataset, root=cfg.data_root,
sample_method=cfg.sample_method,
batch_size=cfg.batch_size,
p_size=cfg.p_size,
k_size=cfg.k_size,
image_size=cfg.image_size,
random_flip=cfg.random_flip,
random_crop=cfg.random_crop,
random_erase=cfg.random_erase,
color_jitter=cfg.color_jitter,
padding=cfg.padding,
vimc_wg=cfg.vimc_wg,
vimc_cc=cfg.vimc_cc,
vimc_sj=cfg.vimc_sj,
num_workers=4)
# Test DataLoader --------------------------------------------------------------------------------------------------
gallery_loader, query_loader = None, None
if cfg.eval_interval > 0:
gallery_loader, query_loader = get_test_loader(dataset=cfg.dataset,
root=cfg.data_root,
batch_size=cfg.batch_size,
image_size=cfg.image_size,
num_workers=4)
# Model ------------------------------------------------------------------------------------------------------------
model = MCJA(num_classes=cfg.num_id,
drop_last_stride=cfg.drop_last_stride,
mda_ratio=cfg.mda_ratio,
mda_m=cfg.mda_m,
loss_id=cfg.loss_id,
loss_cmr=cfg.loss_cmr)
def get_parameter_number(net):
total_num = sum(p.numel() for p in net.parameters())
trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
return {'Total': total_num, 'Trainable': trainable_num}
logger.info(f'Model Parameter Num - {get_parameter_number(model)}')
model.cuda()
# Optimizer --------------------------------------------------------------------------------------------------------
optimizer = optim.Adam(model.parameters(), lr=cfg.lr, weight_decay=cfg.wd)
model, optimizer = amp.initialize(model, optimizer, enabled=cfg.fp16, opt_level='O1', verbosity=0)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer, milestones=cfg.lr_step, gamma=0.1)
# Resume -----------------------------------------------------------------------------------------------------------
if cfg.resume:
checkpoint = torch.load(cfg.resume)
for key in list(checkpoint.keys()):
model_state_dict = model.state_dict()
if key in model_state_dict:
if torch.is_tensor(checkpoint[key]) and checkpoint[key].shape != model_state_dict[key].shape:
logger.info(f'Warning during loading weights - Auto remove mismatch key: {key}')
checkpoint.pop(key)
model.load_state_dict(checkpoint, strict=False)
# Engine -----------------------------------------------------------------------------------------------------------
checkpoint_dir = os.path.join('ckptlog/', cfg.dataset, cfg.prefix)
engine = get_trainer(dataset=cfg.dataset,
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
logger=logger,
writer=writer,
non_blocking=True,
log_period=cfg.log_period,
save_dir=checkpoint_dir,
prefix=cfg.prefix,
eval_interval=cfg.eval_interval,
start_eval=cfg.start_eval,
gallery_loader=gallery_loader,
query_loader=query_loader)
engine.run(train_loader, max_epochs=cfg.num_epoch)
writer.close()
def test(cfg):
# Logger -----------------------------------------------------------------------------------------------------------
logger = logging.getLogger('MCJA')
logger.info('\n## Starting the testing process...')
# Test DataLoader --------------------------------------------------------------------------------------------------
gallery_loader, query_loader = get_test_loader(dataset=cfg.dataset,
root=cfg.data_root,
batch_size=cfg.batch_size,
image_size=cfg.image_size,
num_workers=4,
mode=None)
if cfg.mser:
gallery_loader_r, query_loader_r = get_test_loader(dataset=cfg.dataset,
root=cfg.data_root,
batch_size=cfg.batch_size,
image_size=cfg.image_size,
num_workers=4,
mode='r')
gallery_loader_g, query_loader_g = get_test_loader(dataset=cfg.dataset,
root=cfg.data_root,
batch_size=cfg.batch_size,
image_size=cfg.image_size,
num_workers=4,
mode='g')
gallery_loader_b, query_loader_b = get_test_loader(dataset=cfg.dataset,
root=cfg.data_root,
batch_size=cfg.batch_size,
image_size=cfg.image_size,
num_workers=4,
mode='b')
# Model ------------------------------------------------------------------------------------------------------------
model = MCJA(num_classes=cfg.num_id,
drop_last_stride=cfg.drop_last_stride,
mda_ratio=cfg.mda_ratio,
mda_m=cfg.mda_m,
loss_id=cfg.loss_id,
loss_cmr=cfg.loss_cmr)
model.cuda()
model = amp.initialize(model, enabled=cfg.fp16, opt_level='O1', verbosity=0)
# Resume -----------------------------------------------------------------------------------------------------------
resume_path = cfg.resume if cfg.resume else glob.glob(f'{cfg.log_dir}/*best*')[0]
## Note: if cfg.resume is specified, it will be used;
## otherwise, the best model trained in the current experiment will be automatically loaded.
checkpoint = torch.load(resume_path)
for key in list(checkpoint.keys()):
model_state_dict = model.state_dict()
if key in model_state_dict:
if torch.is_tensor(checkpoint[key]) and checkpoint[key].shape != model_state_dict[key].shape:
logger.info(f'Warning during loading weights - Auto remove mismatch key: {key}')
checkpoint.pop(key)
model.load_state_dict(checkpoint, strict=False)
# Evaluator --------------------------------------------------------------------------------------------------------
non_blocking = True
evaluator = create_eval_engine(model, non_blocking)
# extract query feature
evaluator.run(query_loader)
q_feats = torch.cat(evaluator.state.feat_list, dim=0)
q_ids = torch.cat(evaluator.state.id_list, dim=0).numpy()
q_cams = torch.cat(evaluator.state.cam_list, dim=0).numpy()
q_img_paths = np.concatenate(evaluator.state.img_path_list, axis=0)
# extract gallery feature
evaluator.run(gallery_loader)
g_feats = torch.cat(evaluator.state.feat_list, dim=0)
g_ids = torch.cat(evaluator.state.id_list, dim=0).numpy()
g_cams = torch.cat(evaluator.state.cam_list, dim=0).numpy()
g_img_paths = np.concatenate(evaluator.state.img_path_list, axis=0)
if cfg.mser:
###### Multi-Spectral Enhanced Ranking (MSER) ######
evaluator = create_eval_engine(model, non_blocking)
# extract query feature mode - r
evaluator.run(query_loader_r)
q_feats_r = torch.cat(evaluator.state.feat_list, dim=0)
q_ids_r = torch.cat(evaluator.state.id_list, dim=0).numpy()
q_cams_r = torch.cat(evaluator.state.cam_list, dim=0).numpy()
q_img_paths_r = np.concatenate(evaluator.state.img_path_list, axis=0)
# extract gallery feature mode - r
evaluator.run(gallery_loader_r)
g_feats_r = torch.cat(evaluator.state.feat_list, dim=0)
g_ids_r = torch.cat(evaluator.state.id_list, dim=0).numpy()
g_cams_r = torch.cat(evaluator.state.cam_list, dim=0).numpy()
g_img_paths_r = np.concatenate(evaluator.state.img_path_list, axis=0)
evaluator = create_eval_engine(model, non_blocking)
# extract query feature mode - g
evaluator.run(query_loader_g)
q_feats_g = torch.cat(evaluator.state.feat_list, dim=0)
q_ids_g = torch.cat(evaluator.state.id_list, dim=0).numpy()
q_cams_g = torch.cat(evaluator.state.cam_list, dim=0).numpy()
q_img_paths_g = np.concatenate(evaluator.state.img_path_list, axis=0)
# extract gallery feature mode - g
evaluator.run(gallery_loader_g)
g_feats_g = torch.cat(evaluator.state.feat_list, dim=0)
g_ids_g = torch.cat(evaluator.state.id_list, dim=0).numpy()
g_cams_g = torch.cat(evaluator.state.cam_list, dim=0).numpy()
g_img_paths_g = np.concatenate(evaluator.state.img_path_list, axis=0)
evaluator = create_eval_engine(model, non_blocking)
# extract query feature mode - b
evaluator.run(query_loader_b)
q_feats_b = torch.cat(evaluator.state.feat_list, dim=0)
q_ids_b = torch.cat(evaluator.state.id_list, dim=0).numpy()
q_cams_b = torch.cat(evaluator.state.cam_list, dim=0).numpy()
q_img_paths_b = np.concatenate(evaluator.state.img_path_list, axis=0)
# extract gallery feature mode - b
evaluator.run(gallery_loader_b)
g_feats_b = torch.cat(evaluator.state.feat_list, dim=0)
g_ids_b = torch.cat(evaluator.state.id_list, dim=0).numpy()
g_cams_b = torch.cat(evaluator.state.cam_list, dim=0).numpy()
g_img_paths_b = np.concatenate(evaluator.state.img_path_list, axis=0)
q_feats_mser = [q_feats, q_feats_r, q_feats_g, q_feats_b]
q_ids_mser = [q_ids, q_ids_r, q_ids_g, q_ids_b]
q_cams_mser = [q_cams, q_cams_r, q_cams_g, q_cams_b]
q_img_paths_mser = [q_img_paths, q_img_paths_r, q_img_paths_g, q_img_paths_b]
g_feats_mser = [g_feats, g_feats_r, g_feats_g, g_feats_b]
g_ids_mser = [g_ids, g_ids_r, g_ids_g, g_ids_b]
g_cams_mser = [g_cams, g_cams_r, g_cams_g, g_cams_b]
g_img_paths_mser = [g_img_paths, g_img_paths_r, g_img_paths_g, g_img_paths_b]
if cfg.dataset == 'sysu':
perm = sio.loadmat(os.path.join(cfg.data_root, 'exp', 'rand_perm_cam.mat'))['rand_perm_cam']
eval_sysu(q_feats, q_ids, q_cams, q_img_paths,
g_feats, g_ids, g_cams, g_img_paths, perm, mode='all', num_shots=1)
eval_sysu(q_feats, q_ids, q_cams, q_img_paths,
g_feats, g_ids, g_cams, g_img_paths, perm, mode='all', num_shots=10)
eval_sysu(q_feats, q_ids, q_cams, q_img_paths,
g_feats, g_ids, g_cams, g_img_paths, perm, mode='indoor', num_shots=1)
eval_sysu(q_feats, q_ids, q_cams, q_img_paths,
g_feats, g_ids, g_cams, g_img_paths, perm, mode='indoor', num_shots=10)
if cfg.mser:
eval_sysu(q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser,
g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser,
perm, mode='all', num_shots=1, mser=True)
eval_sysu(q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser,
g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser,
perm, mode='all', num_shots=10, mser=True)
eval_sysu(q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser,
g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser,
perm, mode='indoor', num_shots=1, mser=True)
eval_sysu(q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser,
g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser,
perm, mode='indoor', num_shots=10, mser=True)
elif cfg.dataset == 'regdb':
logger.info('Test Mode - infrared to visible')
eval_regdb(q_feats, q_ids, q_cams, q_img_paths,
g_feats, g_ids, g_cams, g_img_paths, mode='i2v')
logger.info('Test Mode - visible to infrared')
eval_regdb(g_feats, g_ids, g_cams, g_img_paths,
q_feats, q_ids, q_cams, q_img_paths, mode='v2i')
if cfg.mser:
logger.info('Test Mode - infrared to visible')
eval_regdb(q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser,
g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser, mode='i2v', mser=True)
logger.info('Test Mode - visible to infrared')
eval_regdb(g_feats_mser, g_ids_mser, g_cams_mser, g_img_paths_mser,
q_feats_mser, q_ids_mser, q_cams_mser, q_img_paths_mser, mode='v2i', mser=True)
else:
raise NotImplementedError(f'Dataset - {cfg.dataset} is not supported')
evaluator.state.feat_list.clear()
evaluator.state.id_list.clear()
evaluator.state.cam_list.clear()
evaluator.state.img_path_list.clear()
if __name__ == '__main__':
# Tools ------------------------------------------------------------------------------------------------------------
import argparse
from configs.default import strategy_cfg
from configs.default import dataset_cfg
from utils.tools import set_seed, time_str
# Argument Parser --------------------------------------------------------------------------------------------------
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='configs/SYSU_MCJA.yml', help='customized strategy config')
parser.add_argument('--seed', type=int, default=8, help='random seed - choose a lucky number')
parser.add_argument('--desc', type=str, default=None, help='auxiliary description of this experiment')
parser.add_argument('--gpu', type=str, default='0', help='GPU device for the training process')
args = parser.parse_args()
# Environment ------------------------------------------------------------------------------------------------------
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
set_seed(args.seed)
# Configuration ----------------------------------------------------------------------------------------------------
## strategy_cfg
cfg = strategy_cfg
cfg.merge_from_file(args.cfg)
## dataset_cfg
dataset_cfg = dataset_cfg.get(cfg.dataset)
for k, v in dataset_cfg.items():
cfg[k] = v
## other cfg
cfg.prefix += f'_Time-{time_str()}'
cfg.prefix += f'_{args.desc}' if (args.desc is not None) else ''
cfg['log_dir'] = os.path.join('ckptlog/', cfg.dataset, cfg.prefix)
## freeze cfg
cfg.freeze()
# Logger ---------------------------------------------------------------------------------------------------------
if not os.path.isdir(cfg.log_dir):
os.makedirs(cfg.log_dir, exist_ok=True)
logger = logging.getLogger('MCJA')
logger.setLevel(logging.DEBUG)
consoleHandler = logging.StreamHandler()
consoleHandler.setLevel(logging.INFO)
fileHandler = logging.FileHandler(filename=os.path.join(cfg.log_dir, 'log.txt'))
fileHandler.setLevel(logging.INFO)
formatter = logging.Formatter(fmt='%(asctime)s %(message)s', datefmt='[%Y-%m-%d %H:%M:%S]')
consoleHandler.setFormatter(formatter)
fileHandler.setFormatter(formatter)
logger.addHandler(consoleHandler)
logger.addHandler(fileHandler)
logger.info('\n' + pprint.pformat(cfg))
# Train & Test -----------------------------------------------------------------------------------------------------
if not cfg.test_only:
train(cfg)
test(cfg)
# ------------------------------------------------------------------------------------------------------------------