class MetricsCalculator():
def __init__(self,
name='CTCN',
mode='train',
score_thresh=0.001,
nms_thresh=0.8,
sigma_thresh=0.8,
soft_thresh=0.006,
gt_label_file='',
class_label_file='',
video_duration_file=''):
self.name = name
self.mode = mode # 'train', 'val', 'test'
self.score_thresh = score_thresh
self.nms_thresh = nms_thresh
self.sigma_thresh = sigma_thresh
self.soft_thresh = soft_thresh
self.class_label_file = class_label_file
self.video_duration_file = video_duration_file
if mode == 'test':
lines = open(gt_label_file).readlines()
self.gt_labels = [item.split(' ')[0] for item in lines]
self.box_coder = BoxCoder()
else:
self.gt_labels = None
self.box_coder = None
self.reset()
def reset(self):
logger.info('Resetting {} metrics...'.format(self.mode))
self.aggr_loss = 0.0
self.aggr_loc_loss = 0.0
self.aggr_cls_loss = 0.0
self.aggr_batch_size = 0
if self.mode == 'test':
self.class_label = get_class_label(self.class_label_file)
self.video_time_dict = get_video_time_dict(
self.video_duration_file)
self.res_detect = dict()
self.res_detect["version"] = "VERSION 1.3"
self.res_detect["external_data"] = {
"uesd": False,
"details": "none"
}
self.results_detect = dict()
self.box_decode_params = {
'score_thresh': self.score_thresh,
'nms_thresh': self.nms_thresh,
'sigma_thresh': self.sigma_thresh,
'soft_thresh': self.soft_thresh
}
self.out_file = 'res_decode_' + str(self.score_thresh) + '_' + \
str(self.nms_thresh) + '_' + str(self.sigma_thresh) + \
'_' + str(self.soft_thresh) + '.json'
def accumulate(self, loss, pred, label):
cur_batch_size = 1 # iteration counter
self.aggr_loss += np.mean(np.array(loss[0]))
self.aggr_loc_loss += np.mean(np.array(loss[1]))
self.aggr_cls_loss += np.mean(np.array(loss[2]))
self.aggr_batch_size += cur_batch_size
if self.mode == 'test':
box_preds, label_preds, score_preds = self.box_coder.decode(
pred[0].squeeze(), pred[1].squeeze(), **self.box_decode_params)
fid = label.squeeze()
fname = self.gt_labels[fid]
logger.info("id {}, file {}, num of box preds {}:".format(
fid, fname, len(box_preds)))
self.results_detect[fname] = []
for j in range(len(label_preds)):
self.results_detect[fname].append({
"score":
score_preds[j],
"label":
self.class_label[label_preds[j]].strip(),
"segment": [
max(
0, self.video_time_dict[fname] * box_preds[j][0] /
512.0),
min(
self.video_time_dict[fname],
self.video_time_dict[fname] * box_preds[j][1] /
512.0)
]
})
def finalize_metrics(self):
self.avg_loss = self.aggr_loss / self.aggr_batch_size
self.avg_loc_loss = self.aggr_loc_loss / self.aggr_batch_size
self.avg_cls_loss = self.aggr_cls_loss / self.aggr_batch_size
if self.mode == 'test':
self.res_detect['results'] = self.results_detect
with open(self.out_file, 'w') as f:
json.dump(self.res_detect, f)
logger.info('results has been saved into file: {}'.format(
self.out_file))
def get_computed_metrics(self):
json_stats = {}
json_stats['avg_loss'] = self.avg_loss
json_stats['avg_loc_loss'] = self.avg_loc_loss
json_stats['avg_cls_loss'] = self.avg_cls_loss
return json_stats
class MetricsCalculator():
def __init__(self,
name='CTCN',
mode='train',
score_thresh=0.001,
nms_thresh=0.8,
sigma_thresh=0.8,
soft_thresh=0.006,
gt_label_file='',
class_label_file='',
video_duration_file=''):
self.name = name
self.mode = mode # 'train', 'valid', 'test', 'infer'
self.score_thresh = score_thresh
self.nms_thresh = nms_thresh
self.sigma_thresh = sigma_thresh
self.soft_thresh = soft_thresh
self.class_label_file = class_label_file
self.video_duration_file = video_duration_file
if mode == 'test' or mode == 'infer':
lines = open(gt_label_file).readlines()
self.gt_labels = [item.split(' ')[0] for item in lines]
self.box_coder = BoxCoder()
else:
self.gt_labels = None
self.box_coder = None
self.reset()
def reset(self):
logger.info('Resetting {} metrics...'.format(self.mode))
self.aggr_loss = 0.0
self.aggr_loc_loss = 0.0
self.aggr_cls_loss = 0.0
self.aggr_batch_size = 0
if self.mode == 'test' or 'infer':
self.class_label = get_class_label(self.class_label_file)
self.video_time_dict = get_video_time_dict(
self.video_duration_file)
self.res_detect = dict()
self.res_detect["version"] = "VERSION 1.3"
self.res_detect["external_data"] = {
"uesd": False,
"details": "none"
}
self.results_detect = dict()
self.box_decode_params = {
'score_thresh': self.score_thresh,
'nms_thresh': self.nms_thresh,
'sigma_thresh': self.sigma_thresh,
'soft_thresh': self.soft_thresh
}
self.out_file = self.name + '_' + self.mode + \
'_res_decode_' + str(self.score_thresh) + '_' + \
str(self.nms_thresh) + '_' + str(self.sigma_thresh) + \
'_' + str(self.soft_thresh) + '.json'
def accumulate(self, fetch_list):
cur_batch_size = 1 # iteration counter
total_loss = fetch_list[0]
loc_loss = fetch_list[1]
cls_loss = fetch_list[2]
self.aggr_loss += np.mean(np.array(total_loss))
self.aggr_loc_loss += np.mean(np.array(loc_loss))
self.aggr_cls_loss += np.mean(np.array(cls_loss))
self.aggr_batch_size += cur_batch_size
if self.mode == 'test':
loc_pred = np.array(fetch_list[3])
cls_pred = np.array(fetch_list[4])
label = np.array(fetch_list[5])
box_preds, label_preds, score_preds = self.box_coder.decode(
loc_pred.squeeze(), cls_pred.squeeze(),
**self.box_decode_params)
fid = label.squeeze()
fname = self.gt_labels[fid]
logger.info("id {}, file {}, num of box preds {}:".format(
fid, fname, len(box_preds)))
self.results_detect[fname] = []
for j in range(len(label_preds)):
self.results_detect[fname].append({
"score":
score_preds[j],
"label":
self.class_label[label_preds[j]].strip(),
"segment": [
max(
0, self.video_time_dict[fname] * box_preds[j][0] /
512.0),
min(
self.video_time_dict[fname],
self.video_time_dict[fname] * box_preds[j][1] /
512.0)
]
})
def accumulate_infer_results(self, fetch_list):
fname = fetch_list[2][0]
print('fname', fetch_list[2])
loc_pred = np.array(fetch_list[0])
cls_pred = np.array(fetch_list[1])
assert len(loc_pred) == 1, "please set batchsize to be 1 when infer"
box_preds, label_preds, score_preds = self.box_coder.decode(
loc_pred.squeeze(), cls_pred.squeeze(), **self.box_decode_params)
self.results_detect[fname] = []
log_info = 'name: {} \n'.format(fname)
for j in range(len(label_preds)):
score = score_preds[j]
label = self.class_label[label_preds[j]].strip()
segment_start = max(
0, self.video_time_dict[fname] * box_preds[j][0] / 512.0)
segment_end = min(
self.video_time_dict[fname],
self.video_time_dict[fname] * box_preds[j][1] / 512.0)
self.results_detect[fname].append({
"score":
score,
"label":
label,
"segment": [segment_start, segment_end]
})
log_info += 'score: {}, \tlabel: {}, \tsegment: [{}, {}] \n'.format(
score, label, segment_start, segment_end)
logger.info(log_info)
def finalize_metrics(self, savedir):
self.avg_loss = self.aggr_loss / self.aggr_batch_size
self.avg_loc_loss = self.aggr_loc_loss / self.aggr_batch_size
self.avg_cls_loss = self.aggr_cls_loss / self.aggr_batch_size
filepath = os.path.join(savedir, self.out_file)
if self.mode == 'test':
self.res_detect['results'] = self.results_detect
with open(filepath, 'w') as f:
json.dump(self.res_detect, f)
logger.info(
'results has been saved into file: {}'.format(filepath))
def finalize_infer_metrics(self, savedir):
self.res_detect['results'] = self.results_detect
filepath = os.path.join(savedir, self.out_file)
with open(filepath, 'w') as f:
json.dump(self.res_detect, f)
logger.info('results has been saved into file: {}'.format(filepath))
def get_computed_metrics(self):
json_stats = {}
json_stats['avg_loss'] = self.avg_loss
json_stats['avg_loc_loss'] = self.avg_loc_loss
json_stats['avg_cls_loss'] = self.avg_cls_loss
return json_stats