def setup_folder(args, cfg, phase='train'):
# setup_folder config
cfg_path = osp.join(cfg.GENERAL.CFG_ROOT, args.job_group,
args.cfg_name + '.yml')
merge_cfg_from_file(cfg_path)
cfg.GENERAL.JOB_GROUP = args.job_group
# setup_folder weights folder
snapshot_dir = osp.join(cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.JOB_GROUP,
args.cfg_name)
warn = False if cfg.GENERAL.JOB_GROUP == 'tests' or 'debug' else True
if phase == 'train':
create_if_not_exist(
[cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.HISTORY_ROOT], warn=warn)
create_if_not_exist([
osp.join(cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.JOB_GROUP),
osp.join(cfg.GENERAL.HISTORY_ROOT, cfg.GENERAL.JOB_GROUP)
],
warn=warn)
create_if_not_exist(snapshot_dir, warn=warn)
# setup_folder logger
# TODO image logger another writer
log_dir = osp.join(
osp.join(cfg.LOG.ROOT_DIR,
cfg.GENERAL.JOB_GROUP + '_' + args.cfg_name))
tb_writer = TBWriter(
log_dir, {
'phase': phase,
'show_pr_curve': cfg.LOG.SHOW_PR_CURVE,
'show_test_image': cfg.LOG.SHOW_TEST_IMAGE
})
setup_cuda(cfg, args.cuda, args.devices)
return tb_writer, cfg_path, snapshot_dir, log_dir
def test_vis():
from lib.utils.config import cfg
# TODO make this an API
dataset = VOCDetection(cfg.DATASET.DATASET_DIR, [('2007', 'test')],
SSDAugmentation((300, 500), dataset_mean),
VOCAnnotationTransform())
from lib.utils.visualize_utils import TBWriter
tb_writer = TBWriter(log_dir, {'vis_list': [3, 4, 5, 6, 8]})
# import random
# img_idx = random.randint(0, len(data_loader.dataset)-1)
# image, target = dataset.pull_item(img_index, tb_writer)
for img_idx in range(len(dataset)):
if img_idx > 5:
break
tb_writer.cfg['steps'] = img_idx
image, target, extra = dataset.pull_item(img_idx, tb_writer)
print(image.shape)
def test_vis():
dataset = COCODetection(
COCO_ROOT, ('valminusminival2014', ),
SSDAugmentation((300, 300), dataset_mean, use_base=True),
COCOAnnotationTransform())
from lib.utils.visualize_utils import TBWriter
tb_writer = TBWriter(log_dir, {'epoch': 50, 'vis_list': [3, 4, 5, 6, 8]})
# import random
# img_idx = random.randint(0, len(dataset)-1)
# image, target = dataset.pull_item(img_idx, tb_writer)
for img_idx in range(len(dataset)):
if img_idx > 5:
break
tb_writer.cfg['steps'] = img_idx
image, target = dataset.pull_item(img_idx, tb_writer)
print(image.shape)
def setup_folder(args, cfg, phase='train'):
# setup_folder config
time_stamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
cfg_path = args.cfg_name #osp.join(cfg.GENERAL.CFG_ROOT, args.job_group, args.cfg_name+'.yml')
args.cfg_name = os.path.basename(args.cfg_name)[:-4]
merge_cfg_from_file(cfg_path)
cfg.GENERAL.JOB_GROUP = args.job_group
# setup_folder weights folder
snapshot_dir = osp.join(cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.JOB_GROUP,
args.cfg_name)
warn = False if cfg.GENERAL.JOB_GROUP == 'tests' or 'debug' else True
if phase == 'train':
create_if_not_exist(
[cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.HISTORY_ROOT], warn=warn)
create_if_not_exist([
osp.join(cfg.GENERAL.WEIGHTS_ROOT, cfg.GENERAL.JOB_GROUP),
osp.join(cfg.GENERAL.HISTORY_ROOT, cfg.GENERAL.JOB_GROUP)
],
warn=warn)
create_if_not_exist(snapshot_dir, warn=warn)
log_dir = osp.join(
osp.join(
cfg.LOG.ROOT_DIR, cfg.GENERAL.JOB_GROUP + '_' + args.cfg_name +
'_' + time_stamp))
elif phase == 'eval':
log_dir = osp.join(
osp.join(cfg.LOG.ROOT_DIR,
cfg.GENERAL.JOB_GROUP + '_' + args.cfg_name))
# setup_folder logger
# TODO image logger another writer
tb_writer = None
if args.save_log:
tb_writer = TBWriter(
log_dir, {
'phase': phase,
'show_pr_curve': cfg.LOG.SHOW_PR_CURVE,
'show_test_image': cfg.LOG.SHOW_TEST_IMAGE,
'show_pr_scalar': cfg.LOG.SHOW_PR_SCALAR,
'show_maxconf_box': cfg.LOG.SHOW_MAXCONF_BOX
})
setup_cuda(cfg, args.cuda, args.devices, args.net_gpus, args.loss_gpu)
return tb_writer, cfg_path, snapshot_dir, log_dir
def test_vis():
cfg_name = 'test_data_voc'
cfg_path = osp.join(cfg.GENERAL.CFG_ROOT, 'tests', cfg_name+'.yml')
merge_cfg_from_file(cfg_path)
val_loader = dataset_factory(phase='train', cfg=cfg)
dataset = val_loader.dataset
log_dir = osp.join(osp.join(cfg.LOG.ROOT_DIR, 'tests' + '_' + cfg_name))
# tb_writer = TBWriter(log_dir, {'aug_vis_list': [3, 4, 5, 6, 8]})
tb_writer = TBWriter(log_dir, {'aug_vis_list': [4, 5, 8]})
# tb_writer = None
for img_idx in range(len(dataset)):
# if img_idx >= 100:
# break
tb_writer.cfg['aug'] = True
tb_writer.cfg['steps'] = img_idx
tb_writer.cfg['img_id'] = img_idx
tb_writer.cfg['thick'] = 1
image, target, extra = dataset.pull_item(img_idx, tb_writer)
print(image.shape)
tb_writer.writer.file_writer.flush()