def score_threshold_test(self):
cfgs = []
json_out_heads = []
for score in np.linspace(0, 0.99, 100):
cfg = mmcv.Config.fromfile(self.cfg_path)
cfg.test_cfg['rcnn']['score_thr'] = score
json_out_head = 'score_thr={:.2f}'.format(score)
json_out_heads.append(json_out_head)
cfg.uid = 'score_thr={}'.format(score)
cfg.resume_from = os.path.join(cfg.work_dir, 'epoch_12.pth')
if not os.path.exists(cfg.resume_from):
cfg.resume_from = None
cfgs.append(cfg)
save_path = os.path.join(
cfgs[0].work_dir,
str(self.cfg_name) +
'_score_threshold_{}.txt'.format(self.data_mode))
if self.test_sleep_time >= 0:
batch_test(cfgs,
save_path,
self.test_sleep_time,
mode=self.data_mode,
json_out_heads=json_out_heads)
def find_best_constant_loss_weight(self, const_weights=None):
# To make Figure 5. The evaluation performance with increasing loss weight w.
cfgs = []
if const_weights is None:
const_weights = np.linspace(0, 2, 41)
for weight in const_weights:
cfg = mmcv.Config.fromfile(self.cfg_path)
cfg.model['dfn_balance']['init_weight'] = weight
cfg.uid = weight
cfg.cfg_name = 'const_weight={:.2f}'.format(cfg.uid)
cfg.work_dir = os.path.join(cfg.work_dir, cfg.cfg_name)
cfg.resume_from = os.path.join(cfg.work_dir, 'epoch_12.pth')
if not os.path.exists(cfg.resume_from):
cfg.resume_from = None
cfgs.append(cfg)
batch_train(cfgs, sleep_time=self.train_sleep_time)
save_path = os.path.join(
cfgs[0].work_dir,
str(self.cfg_name) + '_find_best_weight_test.txt')
if self.test_sleep_time >= 0:
batch_test(cfgs,
save_path,
self.test_sleep_time,
mode=self.data_mode)
def two_model_test(self, data_type="fabric"):
# watch train effects using different base cfg
first_model_cfg = [
'onecla/config/{}/size_224x224_epoch_12.py'.format(data_type),
'onecla/config/{}/size_224x224_epoch_52.py'.format(data_type),
'onecla/config/{}/size_1333x800_epoch_12.py'.format(data_type),
'onecla/config/{}/size_1333x800_epoch_52.py'.format(data_type),
]
first_model_path = [
'../work_dirs/{}/resnet50/size_224x224_epoch_12/epoch_000011.pth'.
format(data_type),
'../work_dirs/{}/resnet50/size_224x224_epoch_52/epoch_000051.pth'.
format(data_type),
'../work_dirs/{}/resnet50/size_1333x800_epoch_12/epoch_000011.pth'.
format(data_type),
'../work_dirs/{}/resnet50/size_1333x800_epoch_52/epoch_000051.pth'.
format(data_type),
]
first_code_py = 'onecla/infer.py'
cfgs, json_out_heads = [], []
for i, v in enumerate(first_model_cfg):
cfg = mmcv.Config.fromfile(self.cfg_path)
cfg.first_model_cfg = first_model_cfg[i]
cfg.first_model_path = first_model_path[i]
cfg.first_code_py = first_code_py
basename = os.path.basename(v)
basename = basename[:basename.rfind('.py')]
json_out_head = basename
json_out_heads.append(json_out_head)
cfg.uid = os.path.basename(basename)
cfg.resume_from = os.path.join(cfg.work_dir, 'latest.pth')
if not os.path.exists(cfg.resume_from):
cfg.resume_from = None
cfgs.append(cfg)
save_path = os.path.join(
cfgs[0].work_dir,
str(self.cfg_name) + '_two_model_{}.txt'.format(self.data_mode))
if self.test_sleep_time >= 0:
batch_test(cfgs,
save_path,
self.test_sleep_time,
mode=self.data_mode,
json_out_heads=json_out_heads)
def common_train(self, **kwargs):
cfg = mmcv.Config.fromfile(self.cfg_path)
if kwargs is not None and len(kwargs) != 0:
for k, v in dict(kwargs['test_cfg']).items():
cfg.test_cfg[k] = v
cfg.first_model_cfg = None
cfg.cfg_name = str(self.cfg_name)
cfg.uid = str(self.cfg_name) if 'uid' not in kwargs else kwargs['uid']
cfg.resume_from = os.path.join(cfg.work_dir, 'epoch_12.pth')
if not os.path.exists(cfg.resume_from):
cfg.resume_from = None
cfgs = [cfg]
batch_train(cfgs, sleep_time=self.train_sleep_time)
save_path = os.path.join(
cfg.work_dir,
str(self.cfg_name) + '_{}.txt'.format(self.data_mode))
if self.test_sleep_time >= 0:
batch_test(cfgs,
save_path,
self.test_sleep_time,
mode=self.data_mode)
def normal_proportion_test(self):
def cls_dataset(coco):
if isinstance(coco, str):
coco = COCO(coco)
normal_ids, defect_ids = [], []
for image in coco.dataset['images']:
img_id = image['id']
ann_ids = coco.getAnnIds(img_id)
anns = coco.loadAnns(ann_ids)
cnt = 0
for i, ann in enumerate(anns):
if ann['category_id'] != 0:
cnt += 1
if cnt == 0:
normal_ids.append(img_id)
else:
defect_ids.append(img_id)
return normal_ids, defect_ids
def make_proportion_json(ann_file,
proportion,
save_name,
random_state=666):
from extends.cocoutils.coco_split import get_coco_by_imgids
import pandas as pd
coco = COCO(ann_file)
normal_ids, defect_ids = cls_dataset(coco)
prop = len(normal_ids) / (len(normal_ids) + len(defect_ids))
normal_id_df = pd.DataFrame({'id': normal_ids})
defect_id_df = pd.DataFrame({'id': defect_ids})
if proportion <= prop:
# increase normal images
a = proportion * len(defect_ids) / (1 - proportion)
normal_id_df = normal_id_df.sample(n=int(a),
random_state=random_state)
defect_ids.extend(list(normal_id_df['id']))
dataset = get_coco_by_imgids(coco, defect_ids)
else:
# decrease defective images
b = len(normal_ids) / proportion - len(normal_ids)
defect_id_df = defect_id_df.sample(n=int(b),
random_state=random_state)
normal_ids.extend(list(defect_id_df['id']))
dataset = get_coco_by_imgids(coco, normal_ids)
with open(save_name, 'w') as fp:
json.dump(dataset, fp)
return len(normal_id_df), len(defect_id_df)
cfgs, json_out_heads = [], []
for proportion in np.linspace(0., 1., 101):
cfg = mmcv.Config.fromfile(self.cfg_path)
ann_file = os.path.join(
cfg.work_dir,
'proportion/normal_proportion={:.2f}_test.json'.format(
proportion))
prop_cnt = [None, None]
if True or not os.path.exists(ann_file):
ann_file = ann_file.replace('\\', '/')
save_dir = ann_file[:ann_file.rfind('/')]
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# try to keep as many images as possible in keeping proportion
prop_cnt = make_proportion_json(cfg.data['test']['ann_file'],
proportion, ann_file)
cfg.data['test']['ann_file'] = ann_file
json_out_head = 'proportion={:.2f}'.format(proportion)
json_out_heads.append(json_out_head)
cfg.uid = 'proportion={}, normal_cnt={}, defect_cnt={}'.format(
proportion, prop_cnt[0], prop_cnt[1])
cfg.resume_from = os.path.join(cfg.work_dir, 'epoch_12.pth')
if not os.path.exists(cfg.resume_from):
cfg.resume_from = None
cfgs.append(cfg)
save_path = os.path.join(
cfgs[0].work_dir,
str(self.cfg_name) +
'_normal_proportion_{}.txt'.format(self.data_mode))
if self.test_sleep_time >= 0:
batch_test(cfgs,
save_path,
self.test_sleep_time,
mode=self.data_mode,
json_out_heads=json_out_heads)