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Run_multi_cat.py
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import os
from os.path import join, isdir
from tracker_stage3 import *
import numpy as np
import torch
import argparse
import pickle
import random
import math
import warnings
warnings.simplefilter("ignore", UserWarning)
def genConfig(seq_path, set_type):
path, seqname = os.path.split(seq_path)
if 'RGBT' in set_type:
img_list_visible = sorted([seq_path + '/visible/' + p for p in os.listdir(seq_path + '/visible') if os.path.splitext(p)[1] in ['.jpg','.png','.bmp']])
img_list_infrared = sorted([seq_path + '/infrared/' + p for p in os.listdir(seq_path + '/infrared') if os.path.splitext(p)[1] in ['.jpg','.png','.bmp']])
gt = np.loadtxt(seq_path + '/init.txt', delimiter=',')
elif 'GTOT' in set_type:
img_list_visible = sorted([seq_path + '/v/' + p for p in os.listdir(seq_path + '/v') if os.path.splitext(p)[1] in ['.jpg','.png','.bmp']])
img_list_infrared = sorted([seq_path + '/i/' + p for p in os.listdir(seq_path + '/i') if os.path.splitext(p)[1] in ['.jpg','.png','.bmp']])
gt = np.loadtxt(seq_path + '/init.txt')
if gt.shape[1] == 8:
x_min = np.min(gt[:, [0, 2, 4, 6]], axis=1)[:, None]
y_min = np.min(gt[:, [1, 3, 5, 7]], axis=1)[:, None]
x_max = np.max(gt[:, [0, 2, 4, 6]], axis=1)[:, None]
y_max = np.max(gt[:, [1, 3, 5, 7]], axis=1)[:, None]
gt = np.concatenate((x_min, y_min, x_max - x_min, y_max - y_min), axis=1)
return img_list_visible,img_list_infrared,gt
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-set_type", default = 'RGBT234')
# parser.add_argument("-model_path", default = './models/GTOT_ALL852.pth')
parser.add_argument("-result_path", default = './results/')
parser.add_argument("-visual_log",default=False, action= 'store_true')
parser.add_argument("-visualize",default=False, action='store_true')
parser.add_argument("-adaptive_align",default=True, action='store_false')
parser.add_argument("-padding",default=1.2, type = float)
parser.add_argument("-jitter",default=True, action='store_false')
args = parser.parse_args()
##################################################################################
#########################Just modify opts in this script.#########################
######################Becuase of synchronization of options#######################
##################################################################################
## option setting
# opts['model_path']=args.model_path
# opts['result_path']='./results/'+args.set_type+'/'+args.model_path.split('/')[-1].split('.')[0]+'/'
opts['visual_log']=args.visual_log
opts['set_type']=args.set_type
opts['visualize'] = args.visualize
opts['adaptive_align'] = args.adaptive_align
opts['padding'] = args.padding
opts['jitter'] = args.jitter
##################################################################################
############################Do not modify opts anymore.###########################
######################Becuase of synchronization of options#######################
##################################################################################
if opts['set_type'] == 'RGBT234':
## for RGBT234
opts['lr_init'] = 0.0003
opts['lr_update'] = 0.0003
opts['lr_mult'] = {'fc6':10}
opts['maxiter_update'] = 15
opts['maxiter_init'] = 50
elif opts['set_type'] == 'GTOT':
## for GTOT
opts['lr_init'] = 0.00035
opts['lr_update'] = 0.0002
opts['lr_mult'] = {'fc6':11}
opts['maxiter_update'] = 10
opts['maxiter_init'] = 65
opts['trans_f_expand'] = 1.4
print opts
## path initialization
dataset_path = '/data/dataset/'
seq_home = dataset_path + opts['set_type']
seq_list = [f for f in os.listdir(seq_home) if isdir(join(seq_home,f))]
model_path = '/home/liulei/CAT++/GTOT_multi_cat/'
while True:
flag = False
model_list = os.listdir(model_path)
# import pdb
# pdb.set_trace()
model_list.sort(key=lambda x:int(x.split('i')[1].split('.')[0]))
model_list.reverse()
# gpuid = 3
for model in model_list:
torch.cuda.empty_cache()
# try:
# args.model_path = os.path.join(model_path, model)
# opts['model_path'] = '/home/user/liulei/cat/models/GTOT_multi/GTOT_multi1011.pth'
opts['model_path']=os.path.join(model_path, model)
# torch.cuda.set_device(args.gpuid)
opts['result_path'] = os.path.join(args.result_path, args.set_type) #+'/'+args.model_path.split('/')[-3]+
if not os.path.exists(opts['result_path']):
os.mkdir(opts['result_path'])
opts['result_path'] = os.path.join(opts['result_path'], opts['model_path'].split('/')[-3])
if not os.path.exists(opts['result_path']):
os.mkdir(opts['result_path'])
opts['result_path'] = os.path.join(opts['result_path'], opts['model_path'].split('/')[-2]+opts['model_path'].split('/')[-1].split('.')[0]+'ADRNet_nobb')
if not os.path.exists(opts['result_path']):
os.mkdir(opts['result_path'])
print(opts['model_path'])
else:
print(opts['model_path'], 'exists!')
continue
# except:
# continue
# import pynvml
# pynvml.nvmlInit()
# while True:
# handle = pynvml.nvmlDeviceGetHandleByIndex(gpuid)
# meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
# used = meminfo.used / 1024 /1024
# if used<7000:
# torch.cuda.set_device(gpuid)
# break
# else:
# time.sleep(60)
iou_list=[]
fps_list=dict()
bb_result = dict()
result = dict()
iou_list_nobb=[]
bb_result_nobb = dict()
for num,seq in enumerate(seq_list):
torch.cuda.empty_cache()
np.random.seed(123)
torch.manual_seed(456)
torch.cuda.manual_seed(789)
# seed = 3407
# os.environ['PYTHONHASHSEED'] = str(seed)
# torch.manual_seed(seed)
# torch.cuda.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
# np.random.seed(seed)
# random.seed(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# torch.backends.cudnn.enabled = False
seq_path = seq_home + '/' + seq
img_list_v,img_list_i,gt=genConfig(seq_path,opts['set_type'])
iou_result, iou_result_bb, result, result_bb, fps = run_mdnet(img_list_v, img_list_i, gt[0], gt, seq = seq, display=opts['visualize'])
# iou_result, result_bb, fps, result_nobb = run_mdnet(img_list_v, img_list_i, gt[0], gt, seq = seq, display=opts['visualize'])
enable_frameNum = 0.
for iidx in range(len(iou_result)):
if (math.isnan(iou_result[iidx])==False):
enable_frameNum += 1.
else:
## gt is not alowed
iou_result[iidx] = 0.
iou_result_bb[iidx] = 0.
iou_list.append(iou_result_bb.sum()/enable_frameNum)
iou_list_nobb.append(iou_result.sum()/enable_frameNum)
bb_result[seq] = result_bb
fps_list[seq]=fps
bb_result_nobb[seq] = result
print '{} {} : nobb:{}, bb:{}, total mIoUnobb:{}, total mIoUbb:{}, fps:{}'.format(num,seq,iou_result.mean(), iou_result_bb.mean(), sum(iou_list_nobb)/len(iou_list_nobb), sum(iou_list)/len(iou_list),sum(fps_list.values())/len(fps_list))
save_path_bb = os.path.join(opts['result_path'], 'bb'+seq+'.txt')
save_path_nobb = os.path.join(opts['result_path'], 'nobb'+seq+'.txt')
# np.savetxt(save_path_bb,result_bb)
# np.savetxt(save_path_nobb,result)
for i in range(len(result_bb)):
with open(save_path_bb, 'a') as f:
res='{} {} {} {} {} {} {} {}'.format(result_bb[i][0],result_bb[i][1],result_bb[i][0]+result_bb[i][2],result_bb[i][1],result_bb[i][0]+result_bb[i][2],result_bb[i][1]+result_bb[i][3],result_bb[i][0],result_bb[i][1]+result_bb[i][3])
f.write(res)
f.write('\n')
with open(save_path_nobb, 'a') as f:
res='{} {} {} {} {} {} {} {}'.format(result[i][0],result[i][1],result[i][0]+result[i][2],result[i][1],result[i][0]+result[i][2],result[i][1]+result[i][3],result[i][0],result[i][1]+result[i][3])
f.write(res)
f.write('\n')
print('overall')
flag = True
break
if not flag:
break