From 2016a13fd77312423acde6973e26c9f68e01b40f Mon Sep 17 00:00:00 2001
From: LIN Xiaoyu <xiaoyu.lin@inria.fr>
Date: Fri, 25 Feb 2022 08:42:07 +0000
Subject: [PATCH] Update KF_tracking.py
---
KF_tracking.py | 200 +++++++++++++++++++++++++++----------------------
1 file changed, 111 insertions(+), 89 deletions(-)
diff --git a/KF_tracking.py b/KF_tracking.py
index 883b839..34ac6e6 100644
--- a/KF_tracking.py
+++ b/KF_tracking.py
@@ -1,89 +1,111 @@
-import sys
-import os
-import torch
-import shutil
-import numpy as np
-from configparser import ConfigParser
-from data.data_loader import create_dataloader
-from save_model import SaveLog
-from models.vem_KF import VEM_KF_MODEL
-from utils import tracking_evaluation_onebatch_KF
-import motmetrics as mm
-
-from utils import get_basic_info
-
-
-def train(cfg_file):
- # Read the config file
- if not os.path.isfile(cfg_file):
- raise ValueError('Invalid config file path')
- cfg = ConfigParser()
- cfg.read(cfg_file)
-
- # Create save log directory
- save_log = SaveLog(cfg)
- save_dir = save_log.save_dir
-
- # Save config file
- save_cfg_path = os.path.join(save_dir, 'config.ini')
- shutil.copy(cfg_file, save_cfg_path)
-
- # Print basic information
- use_cuda = cfg.getboolean('Training', 'use_cuda')
- device = 'cuda' if torch.cuda.is_available() and use_cuda else 'cpu'
-
- basic_info = get_basic_info(device)
- save_log.print_info(basic_info)
- for info in basic_info:
- print('%s' % info)
-
- # Create and initialize model
- vem_model = VEM_KF_MODEL(cfg, device, save_log)
-
- # Load data
- vem_data_loader, vem_data_size = create_dataloader(cfg, data_type='mot')
-
- # Print data information
- data_info = []
- data_info.append('========== DATA INFO ==========')
- data_info.append('Training data: %s' % vem_data_size)
- save_log.print_info(data_info)
- for info in data_info:
- print('%s' % info)
-
- # Start training
- print('Start training...')
- total_iter = int(cfg.get('VEM', 'N_iter'))
- save_frequency = int(cfg.get('Training', 'save_frequency'))
- normalize_range = np.array([int(i) for i in cfg.get('DataFrame', 'normalize_range').split(',')], dtype='float64').reshape(-1,4)
- acc_list = [[] for i in range(total_iter)]
- for idx, data in enumerate(vem_data_loader):
- print('batch {}\n'.format(idx))
- data_obs = data['det'].to(device)
- data_gt = data['gt'].to('cpu')
- Eta_iter, x_mean_vem_iter, x_var_vem_iter, Lambda_iter\
- = vem_model.model_training(data_obs, data_gt, idx, save_frequency)
-
- acc_list = tracking_evaluation_onebatch_KF(data_gt, normalize_range, acc_list, Eta_iter, x_mean_vem_iter)
-
- summary_list = []
- mota_list = [[] for i in range(total_iter)]
- for iter_number in range(total_iter):
- mh = mm.metrics.create()
- name = ['sample_{}'.format(i) for i in range(vem_data_size)]
- summary = mh.compute_many(acc_list[iter_number], metrics=mm.metrics.motchallenge_metrics, names=name, generate_overall=True)
- mota_list[iter_number].append(summary.loc['OVERALL']['mota'])
- strsummary = mm.io.render_summary(
- summary,
- formatters=mh.formatters,
- namemap=mm.io.motchallenge_metric_names
- )
- summary_list.append(strsummary)
- save_log.save_evaluation(summary_list, mota_list, total_iter)
-
-if __name__ == '__main__':
- if len(sys.argv) == 2:
- cfg_file = sys.argv[1]
- train(cfg_file)
- else:
- print('Error: Please indicate config file path')
\ No newline at end of file
+## DVAE-UMOT
+## Copyright Inria
+## Year 2022
+## Contact : xiaoyu.lin@inria.fr
+
+## DVAE-UMOT is free software: you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or
+## (at your option) any later version.
+
+## DVAE-UMOT is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+## GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program, DVAE-UMOT. If not, see <http://www.gnu.org/licenses/> and the LICENSE file.
+
+# DVAE-UMOT has code derived from
+# (1) ArTIST, https://github.com/fatemeh-slh/ArTIST.
+# (2) DVAE, https://github.com/XiaoyuBIE1994/DVAE, distributed under MIT License 2020 INRIA.
+
+import sys
+import os
+import torch
+import shutil
+import numpy as np
+from configparser import ConfigParser
+from data.data_loader import create_dataloader
+from save_model import SaveLog
+from models.vem_KF import VEM_KF_MODEL
+from utils import tracking_evaluation_onebatch_KF
+import motmetrics as mm
+
+from utils import get_basic_info
+
+
+def train(cfg_file):
+ # Read the config file
+ if not os.path.isfile(cfg_file):
+ raise ValueError('Invalid config file path')
+ cfg = ConfigParser()
+ cfg.read(cfg_file)
+
+ # Create save log directory
+ save_log = SaveLog(cfg)
+ save_dir = save_log.save_dir
+
+ # Save config file
+ save_cfg_path = os.path.join(save_dir, 'config.ini')
+ shutil.copy(cfg_file, save_cfg_path)
+
+ # Print basic information
+ use_cuda = cfg.getboolean('Training', 'use_cuda')
+ device = 'cuda' if torch.cuda.is_available() and use_cuda else 'cpu'
+
+ basic_info = get_basic_info(device)
+ save_log.print_info(basic_info)
+ for info in basic_info:
+ print('%s' % info)
+
+ # Create and initialize model
+ vem_model = VEM_KF_MODEL(cfg, device, save_log)
+
+ # Load data
+ vem_data_loader, vem_data_size = create_dataloader(cfg, data_type='mot')
+
+ # Print data information
+ data_info = []
+ data_info.append('========== DATA INFO ==========')
+ data_info.append('Training data: %s' % vem_data_size)
+ save_log.print_info(data_info)
+ for info in data_info:
+ print('%s' % info)
+
+ # Start training
+ print('Start training...')
+ total_iter = int(cfg.get('VEM', 'N_iter'))
+ save_frequency = int(cfg.get('Training', 'save_frequency'))
+ normalize_range = np.array([int(i) for i in cfg.get('DataFrame', 'normalize_range').split(',')], dtype='float64').reshape(-1,4)
+ acc_list = [[] for i in range(total_iter)]
+ for idx, data in enumerate(vem_data_loader):
+ print('batch {}\n'.format(idx))
+ data_obs = data['det'].to(device)
+ data_gt = data['gt'].to('cpu')
+ Eta_iter, x_mean_vem_iter, x_var_vem_iter, Lambda_iter\
+ = vem_model.model_training(data_obs, data_gt, idx, save_frequency)
+
+ acc_list = tracking_evaluation_onebatch_KF(data_gt, normalize_range, acc_list, Eta_iter, x_mean_vem_iter)
+
+ summary_list = []
+ mota_list = [[] for i in range(total_iter)]
+ for iter_number in range(total_iter):
+ mh = mm.metrics.create()
+ name = ['sample_{}'.format(i) for i in range(vem_data_size)]
+ summary = mh.compute_many(acc_list[iter_number], metrics=mm.metrics.motchallenge_metrics, names=name, generate_overall=True)
+ mota_list[iter_number].append(summary.loc['OVERALL']['mota'])
+ strsummary = mm.io.render_summary(
+ summary,
+ formatters=mh.formatters,
+ namemap=mm.io.motchallenge_metric_names
+ )
+ summary_list.append(strsummary)
+ save_log.save_evaluation(summary_list, mota_list, total_iter)
+
+if __name__ == '__main__':
+ if len(sys.argv) == 2:
+ cfg_file = sys.argv[1]
+ train(cfg_file)
+ else:
+ print('Error: Please indicate config file path')
--
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