def print_list(self):
try:
rows = self.tbl_row_query(self.tbl_name, "PartitionKey eq 'net'")
#print(dir(row))
from colorclass import Color, Windows
from terminaltables import AsciiTable
Windows.enable(auto_colors=True, reset_atexit=True)
table_data = [[Color('{autocyan}Hostname'),
Color('{autocyan}Last Reply'),
Color('{autocyan}IP'),
Color('{autocyan}OS'),
Color('{autocyan}OS Release'),
Color('{autocyan}OS Version'),
Color('{autocyan}System'),
Color('{autocyan}Processor'),
]
]
max_wrap=10
for row in rows:
#d = self.entity2dict(row)
d = row.__dict__
time = alxlib.time_help.format_from_timestamp(d['Timestamp'])
li = [d['hostname'], time, d['ip'], d['os'], d['osrelease'],
self.wrap_text(d['osversion'], max_wrap), d["system"],
self.wrap_text(d["processor"], max_wrap), ]
table_data.append(li)
table = AsciiTable(table_data)
table.table_data = table_data
print(table.table)
except Exception as e:
logging.warning("Error print_list")
print(e)
def val(model, optimizer, dataloader, epoch, opt, val_logger, visualizer=None):
labels = []
sample_matrics = []
for i, (images, targets) in enumerate(dataloader):
labels += targets[:, 1].tolist()
targets[:, 2:] = xywh2xyxy(targets[:, 2:])
targets[:, 2:] *= opt.image_size
outputs = model.forward(images)
outputs = non_max_suppression(outputs, opt.conf_thres, opt.nms_thres)
sample_matrics += get_batch_statistics(outputs,
targets,
iou_threshold=0.5)
if visualizer is not None:
vis.plot_current_visuals(images, outputs)
true_positives, pred_scores, pred_labels = [
np.concatenate(x, 0) for x in list(zip(*sample_matrics))
]
precision, recall, AP, f1, ap_class = ap_per_class(true_positives,
pred_scores,
pred_labels, labels)
# logging
metric_table_data = [['Metrics', 'Value'], ['precision',
precision.mean()],
['recall', recall.mean()], ['f1', f1.mean()],
['mAP', AP.mean()]]
metric_table = AsciiTable(metric_table_data,
title='[Epoch {:d}/{:d}'.format(
epoch, opt.num_epochs))
print('{}\n\n\n'.format(metric_table.table))
class_names = load_classe_names(opt.classname_path)
for i, c in enumerate(ap_class):
metric_table_data += [['AP-{}'.format(class_names[c]), AP[i]]]
metric_table.table_data = metric_table_data
val_logger.write('{}\n\n\n'.format(metric_table.table))
def val(model, optimizer, dataloader, epoch, opt, val_logger, best_mAP=0):
labels = []
sample_matrics = []
if opt.gpu:
model = model.to(opt.device)
model.eval()
for i, (images, targets) in enumerate(dataloader):
labels += targets[:, 1].tolist()
targets[:, 2:] = xywh2xyxy(targets[:, 2:])
targets[:, 2:] *= opt.image_size
batches_done = len(dataloader) * epoch + i
if opt.gpu:
images = Variable(images.to(opt.device))
detections = model.forward(images)
detections = non_max_suppression(detections, opt.conf_thresh,
opt.nms_thresh)
sample_matrics += get_batch_statistics(detections,
targets.cpu(),
iou_threshold=0.5)
true_positives, pred_scores, pred_labels = [
np.concatenate(x, 0) for x in list(zip(*sample_matrics))
]
precision, recall, AP, f1, ap_class = ap_per_class(true_positives,
pred_scores,
pred_labels, labels)
# logging
metric_table_data = [['Metrics', 'Value'], ['precision',
precision.mean()],
['recall', recall.mean()], ['f1', f1.mean()],
['mAP', AP.mean()]]
metric_table = AsciiTable(metric_table_data,
title='[Epoch {:d}/{:d}'.format(
epoch, opt.num_epochs))
class_names = load_classe_names(opt.classname_path)
for i, c in enumerate(ap_class):
metric_table_data += [['AP-{}'.format(class_names[c]), AP[i]]]
metric_table.table_data = metric_table_data
val_logger.print_and_write('{}\n'.format(metric_table.table))
if best_mAP < AP.mean():
save_file_path = os.path.join(opt.checkpoint_path, 'best.pth')
states = {
'epoch': epoch + 1,
'model': opt.model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_mAP': best_mAP,
}
torch.save(states, save_file_path)
best_mAP = AP.mean()
print("current best mAP:" + str(best_mAP))
return best_mAP