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train_prcc_hpm.py
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train_prcc_hpm.py
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# encoding: utf-8
import argparse
import os
import sys
import torch
from torch.backends import cudnn
sys.path.append('.')
from data import make_data_loader_prcc_hrnet as make_data_loader
from engine.trainer import do_train_prcc_hpm as do_train
from modeling import build_model
from layers import make_loss_with_triplet_entropy
from solver import make_optimizer_with_global
from solver import WarmupMultiStepLR
from engine.inference import inference_prcc_global, inference_prcc_visual_rank
import datetime
def load_network_pretrain(model, cfg):
path = os.path.join(cfg.logs_dir, 'checkpoint_best.pth')
if not os.path.exists(path):
return model, 0, 0.0
pre_dict = torch.load(path)
model.load_state_dict(pre_dict['state_dict'])
start_epoch = pre_dict['epoch']
best_acc = pre_dict['best_acc']
print('start_epoch:', start_epoch)
print('best_acc:', best_acc)
return model, start_epoch, best_acc
def main(cfg):
# prepare dataset
train_loader, train_loader_ca, train_loader_cb, val_loader_c, val_loader_b, num_query_c, num_query_b, num_classes = make_data_loader(cfg, h=256, w=128) # num_query=3368, num_classes=751
# prepare model
model = build_model(num_classes, 'hpm') # num_classes=751
model = torch.nn.DataParallel(model).cuda() if torch.cuda.is_available() else model
loss_func = make_loss_with_triplet_entropy(cfg, num_classes) # modified by gu
optimizer = make_optimizer_with_global(cfg, model)
if cfg.lr_type == 'step':
scheduler = WarmupMultiStepLR(optimizer, cfg.steps, cfg.gamma, cfg.warmup_factor, cfg.warmup_iters, cfg.warmup_method)
else:
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=10, factor=0.5)
if cfg.train == 'train':
start_epoch = int(0)
acc_best = 0.0
if cfg.resume == 1:
model, start_epoch, acc_best = load_network_pretrain(model, cfg)
do_train(cfg, model, train_loader, val_loader_c, optimizer, scheduler, loss_func, num_query_c, start_epoch, acc_best, lr_type=cfg.lr_type)
elif cfg.train == 'test':
# Test
last_model_wts = torch.load(os.path.join(cfg.logs_dir, 'checkpoint_best.pth'))
model.load_state_dict(last_model_wts['state_dict'])
mAP, cmc1 = inference_prcc_global(model, val_loader_c, num_query_c)
start_time = datetime.datetime.now()
start_time = '%4d:%d:%d-%2d:%2d:%2d' % (start_time.year, start_time.month, start_time.day, start_time.hour, start_time.minute, start_time.second)
line = '{} - Test: cmc1: {:.1%}, mAP: {:.1%}\n'.format(start_time, cmc1, mAP)
print(line)
elif cfg.train == 'rank':
# Test
last_model_wts = torch.load(os.path.join(cfg.logs_dir, 'checkpoint_best.pth'))
model.load_state_dict(last_model_wts['state_dict'])
home = os.path.join('logs', 'rank', os.path.basename(cfg.logs_dir))
inference_prcc_visual_rank(model, val_loader_c, num_query_c, home=home, show_rank=20, use_flip=True)
print('finish')
if __name__ == '__main__':
gpu_id = 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
cudnn.benchmark = True
parser = argparse.ArgumentParser(description="ReID Baseline Training")
# DATA
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--img_per_id', type=int, default=4)
parser.add_argument('--batch_size_test', type=int, default=128)
parser.add_argument('--workers', type=int, default=8)
parser.add_argument('--height', type=int, default=256)
parser.add_argument('--width', type=int, default=128)
parser.add_argument('--height_mask', type=int, default=256)
parser.add_argument('--width_mask', type=int, default=128)
# MODEL
parser.add_argument('--features', type=int, default=128)
parser.add_argument('--dropout', type=float, default=0.0)
parser.add_argument('--parts', type=int, default=6)
# OPTIMIZER
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--lr', type=float, default=0.0035)
parser.add_argument('--lr_center', type=float, default=0.5)
parser.add_argument('--lr_type', type=str, default='step', help='step, plateau')
parser.add_argument('--center_loss_weight', type=float, default=0.0005)
parser.add_argument('--steps', type=list, default=[40, 80])
parser.add_argument('--gamma', type=float, default=0.1)
parser.add_argument('--cluster_margin', type=float, default=0.3)
parser.add_argument('--bias_lr_factor', type=float, default=1.0)
parser.add_argument('--weight_decay', type=float, default=5e-4)
parser.add_argument('--weight_decay_bias', type=float, default=5e-4)
parser.add_argument('--range_k', type=float, default=2)
parser.add_argument('--range_margin', type=float, default=0.3)
parser.add_argument('--range_alpha', type=float, default=0)
parser.add_argument('--range_beta', type=float, default=1)
parser.add_argument('--range_loss_weight', type=float, default=1)
parser.add_argument('--warmup_factor', type=float, default=0.01)
parser.add_argument('--warmup_iters', type=float, default=10)
parser.add_argument('--warmup_method', type=str, default='linear')
parser.add_argument('--margin', type=float, default=0.3)
parser.add_argument('--optimizer_name', type=str, default="SGD")
parser.add_argument('--momentum', type=float, default=0.9)
# TRAINER
parser.add_argument('--max_epochs', type=int, default=60)
parser.add_argument('--train', type=str, default='train', help='train, test, rank') # change train or test mode
parser.add_argument('--resume', type=int, default=0)
parser.add_argument('--num_works', type=int, default=0)
# misc
working_dir = os.path.dirname(os.path.abspath(__file__))
parser.add_argument('--dataset', type=str, default='prcc_gcn')
parser.add_argument('--data_dir', type=str, default='/data/prcc/')
parser.add_argument('--logs_dir', type=str, default=os.path.join(working_dir, 'logs/prcc_hpm/'))
cfg = parser.parse_args()
if not os.path.exists(cfg.logs_dir):
os.makedirs(cfg.logs_dir)
main(cfg)