def filter_cls_data(yx_min, yx_max, mask):
if mask.numel() > 0:
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
else: # all bboxes are difficult
yx_min = utils.ensure_device(torch.zeros(0, 2))
yx_max = utils.ensure_device(torch.zeros(0, 2))
return yx_min, yx_max
def filter_cls_data(yx_min, yx_max, mask):
if mask.numel() > 0:
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
else: # all bboxes are difficult
yx_min = utils.ensure_device(torch.zeros(0, 2))
yx_max = utils.ensure_device(torch.zeros(0, 2))
return yx_min, yx_max
def filter_visible(self, yx_min, yx_max, iou, prob, cls):
try:
score = iou
mask = score > self.config.getfloat('detect', 'threshold')
except configparser.NoOptionError:
score = prob
mask = score > self.config.getfloat('detect', 'threshold_cls')
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
cls, score = (t[mask].view(-1) for t in (cls, score))
return yx_min, yx_max, cls, score
def filter_cls_pred(yx_min, yx_max, score, mask):
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
score = score[mask]
return yx_min, yx_max, score
def filter_valid(yx_min, yx_max, cls, difficult):
mask = torch.prod(yx_min < yx_max, -1) & (difficult < 1)
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
cls, = (t[mask] for t in (cls, ))
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
return yx_min, yx_max, cls
def filter_cls_pred(yx_min, yx_max, score, mask):
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
score = score[mask]
return yx_min, yx_max, score
def filter_valid(yx_min, yx_max, cls, difficult):
mask = torch.prod(yx_min < yx_max, -1) & (difficult < 1)
_mask = torch.unsqueeze(mask, -1).repeat(1, 2) # PyTorch's bug
cls, = (t[mask] for t in (cls,))
yx_min, yx_max = (t[_mask].view(-1, 2) for t in (yx_min, yx_max))
return yx_min, yx_max, cls
