def main():
args = parse_args()
print('Called with args:')
print(args)
if args.dataset == "pascal_voc":
args.imdbval_name = "voc_2007_test"
set_cfgs = [
'ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
elif args.dataset == "pascal_voc_0712":
args.imdbval_name = "voc_0712_test"
set_cfgs = [
'ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
elif args.dataset == "coco":
args.imdbval_name = "coco_2014_minival"
set_cfgs = [
'ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
elif args.dataset == "imagenet":
args.imdbval_name = "imagenet_val"
set_cfgs = [
'ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
elif args.dataset == "vg":
args.imdbval_name = "vg_150-50-50_minival"
set_cfgs = [
'ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
elif 'mot_2017' in args.dataset or 'mot19_cvpr' in args.dataset:
set_cfgs = [
'ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'
]
# elif args.dataset == "mot_2017_train":
# # args.imdb_name = "mot_2017_train"
# args.imdbval_name = "mot_2017_train"
# set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
# elif args.dataset == "mot_2017_small_train":
# # args.imdb_name = "mot_2017_small_train"
# args.imdbval_name = "mot_2017_small_train"
# set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
# elif args.dataset == "mot_2017_small_val":
# # args.imdb_name = "mot_2017_small_train"
# args.imdbval_name = "mot_2017_small_val"
# set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
# elif args.dataset == "mot_2017_all":
# # args.imdb_name = "mot_2017_small_train"
# args.imdbval_name = "mot_2017_all"
# set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
else:
raise NotImplementedError
input_dir = os.path.join(args.load_dir, args.net, args.dataset,
args.exp_name)
if not os.path.exists(input_dir):
raise Exception(
'There is no input directory for loading network from ' +
input_dir)
load_name = os.path.join(input_dir,
f"fpn_{args.checksession}_{args.checkepoch}.pth")
cfg_file = os.path.join(input_dir, 'config.yaml')
cfg_from_file(cfg_file)
cfg_from_list(set_cfgs)
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# data
# cfg.TRAIN.USE_FLIPPED = False
imdb, roidb, ratio_list, ratio_index = combined_roidb(
args.imdbval_name, False)
imdb.competition_mode(on=True)
print('{:d} roidb entries'.format(len(roidb)))
output_dir = get_output_dir(imdb, args.exp_name)
dataset = roibatchLoader(roidb,
ratio_list,
ratio_index,
1,
imdb.num_classes,
training=False,
normalize=False)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True)
# network
print("load checkpoint %s" % (load_name))
if args.net == 'res101':
FPN = FPNResNet(imdb.classes, 101, pretrained=False)
elif args.net == 'res50':
FPN = FPNResNet(imdb.classes, 50, pretrained=False)
elif args.net == 'res152':
FPN = FPNResNet(imdb.classes, 152, pretrained=False)
else:
print("Network is not defined.")
pdb.set_trace()
FPN.create_architecture()
FPN.load_state_dict(torch.load(load_name)['model'])
print('load model successfully!')
if args.cuda:
cfg.CUDA = True
FPN.cuda()
elif torch.cuda.is_available():
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
start = time.time()
_t = {'im_detect': time.time(), 'misc': time.time()}
det_file = os.path.join(output_dir, 'detections.pkl')
all_boxes = validate(FPN,
dataloader,
imdb,
vis=args.vis,
cuda=args.cuda,
soft_nms=args.soft_nms,
score_thresh=args.score_thresh)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
end = time.time()
print("test time: %0.4fs" % (end - start))
with open(os.path.join(output_dir, 'config.yaml'), 'w') as outfile:
yaml.dump(cfg, outfile, default_flow_style=False)
end = time.time()
# print(end - start)
#
# evaluation
#
#
# train
#
all_boxes = validate(FPN,
dataloader_train,
imdb_train,
vis=False,
cuda=args.cuda)
# evaluate without print output
sys.stdout = open(os.devnull, 'w')
aps = imdb.evaluate_detections(all_boxes, output_dir)
sys.stdout = sys.__stdout__
# print because of flushing in imdd_eval.evaluate_detections
_print("")
_print(f'[TRAIN]: Mean AP = {np.mean(aps):.4f}')
if args.use_tfboard:
write_scalars(writer, [np.mean(aps)], ['train'],
epoch,
tag='mean_ap')