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celeba_train_base.py
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# encoding: utf-8
import argparse
import os
import sys
import torch
from torch.backends import cudnn
import numpy as np
sys.path.append('.')
from data import make_data_loader_path as make_data_loader
from engine.trainer import do_train_strong_path as do_train
from modeling import build_model
from layers import make_loss_with_triplet_entropy
from solver import make_optimizer, make_optimizer_map, WarmupMultiStepLR
from engine.inference import inference
import datetime
def load_network_pretrain(model, cfg):
path = os.path.join(cfg.logs_dir, 'checkpoint.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
dataset, train_loader, test_loader, num_query, num_classes = make_data_loader(cfg)
# prepare model
model = build_model(num_classes, 'base', pretrain_choice=True) # num_classes=5000
model = torch.nn.DataParallel(model).cuda() if torch.cuda.is_available() else model
if cfg.mode == '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, cmc5, cmc10, cmc20 = inference(model, test_loader, num_query)
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)
print('{} - Final: cmc1: {:.1%} cmc5: {:.1%} cmc10: {:.1%} cmc20: {:.1%} mAP: {:.1%}\n'.format(start_time, cmc1, cmc5, cmc10, cmc20, mAP))
return
if cfg.mode == 'ext':
# 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, cmc5, cmc10, cmc20, feat_dict = inference(model, test_loader, num_query, True)
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)
print('{} - Final: cmc1: {:.1%} cmc5: {:.1%} cmc10: {:.1%} cmc20: {:.1%} mAP: {:.1%}\n'.format(start_time, cmc1, cmc5, cmc10, cmc20, mAP))
path_f = os.path.join(cfg.logs_dir, 'feat_test.npy')
feats = torch.stack([feat_dict[name] for name, _, _ in dataset.query_test + dataset.gallery_test]).cpu().data.numpy() # [70264, 2048]
np.save(path_f, feats)
return
loss_func = make_loss_with_triplet_entropy(cfg, num_classes)
optimizer = make_optimizer_map(cfg, model)
scheduler = WarmupMultiStepLR(optimizer, cfg.steps, cfg.gamma, cfg.warmup_factor, cfg.warmup_iters, cfg.warmup_method)
start_epoch = 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, test_loader, optimizer, scheduler, loss_func, num_query, start_epoch, acc_best)
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=16)
parser.add_argument('--batch_size_dis', type=int, default=64)
parser.add_argument('--img_per_id_dis', type=int, default=8)
parser.add_argument('--batch_size_test', type=int, default=128)
parser.add_argument('--workers', type=int, default=4)
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)
# OPTIMIZER
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--lr', type=float, default=0.00035)
parser.add_argument('--lr_center', type=float, default=0.5)
parser.add_argument('--center_loss_weight', type=float, default=0.0005)
parser.add_argument('--steps', type=list, default=[40, 100])
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="Adam")
parser.add_argument('--momentum', type=float, default=0.9)
# TRAINER
parser.add_argument('--max_epochs', type=int, default=120)
parser.add_argument('--max_iters', type=int, default=10)
parser.add_argument('--train', type=int, default=0) # change train or test mode
parser.add_argument('--mode', type=str, default='train', help=['train', 'val', 'test', 'ext']) # change train or test mode
parser.add_argument('--resume', type=int, default=0)
parser.add_argument('--num_works', type=int, default=4)
# misc
working_dir = os.path.dirname(os.path.abspath(__file__))
parser.add_argument('--dataset', type=str, default='celeba')
parser.add_argument('--data_dir', type=str, default='/data/shuxj/data/PReID/Celeb-reID/')
parser.add_argument('--logs_dir', type=str, default=os.path.join(working_dir, 'logs/20210206_celeb_ap_base_sgd'))
cfg = parser.parse_args()
if not os.path.exists(cfg.logs_dir):
os.makedirs(cfg.logs_dir)
main(cfg)