def matching(data_yx_min, data_yx_max, yx_min, yx_max, threshold):
if data_yx_min.numel() > 0:
matrix = utils.iou.torch.iou_matrix(yx_min, yx_max, data_yx_min, data_yx_max)
iou, index = torch.max(matrix, -1)
positive = iou > threshold
tp = _matching(positive.cpu().numpy(), index.cpu().numpy())
else:
tp = np.zeros([yx_min.size(0)], np.bool)
return tp
def matching(data_yx_min, data_yx_max, yx_min, yx_max, threshold):
if data_yx_min.numel() > 0:
matrix = utils.iou.torch.iou_matrix(yx_min, yx_max, data_yx_min,
data_yx_max)
iou, index = torch.max(matrix, -1)
positive = iou > threshold
tp = _matching(positive.cpu().numpy(), index.cpu().numpy())
else:
tp = np.zeros([yx_min.size(0)], np.bool)
return tp
def conv_logits(pred):
if 'logits' in pred:
logits = pred['logits'].contiguous()
prob, cls = torch.max(F.softmax(logits, -1), -1)
else:
size = pred['iou'].size()
prob = torch.autograd.Variable(utils.ensure_device(torch.ones(*size)))
cls = torch.autograd.Variable(
utils.ensure_device(torch.zeros(*size).int()))
return prob, cls