def __call__(self):
changed = np.zeros([self.height, self.width], np.bool)
for yx in tqdm.tqdm(self.loader):
batch_size = yx.size(0)
tensor = torch.zeros(batch_size, 3, self.height, self.width)
for i, _yx in enumerate(torch.unbind(yx)):
y, x = torch.unbind(_yx)
tensor[i, :, y, x] = 1
tensor = utils.ensure_device(tensor)
output = self.dnn(torch.autograd.Variable(tensor))
output = output[:, :, self.i, self.j]
cmp = output == self.output
cmp = torch.prod(cmp, -1).data
for _yx, c in zip(torch.unbind(yx), torch.unbind(cmp)):
y, x = torch.unbind(_yx)
changed[y, x] = c
return changed
def __call__(self):
changed = np.zeros([self.height, self.width], np.bool)
for yx in tqdm.tqdm(self.loader):
batch_size = yx.size(0)
tensor = torch.zeros(batch_size, 3, self.height, self.width)
for i, _yx in enumerate(torch.unbind(yx)):
y, x = torch.unbind(_yx)
tensor[i, :, y, x] = 1
tensor = utils.ensure_device(tensor)
output = self.dnn(torch.autograd.Variable(tensor, volatile=True))
output = output[:, :, self.i, self.j]
cmp = output == self.output
cmp = torch.prod(cmp, -1).data
for _yx, c in zip(torch.unbind(yx), torch.unbind(cmp)):
y, x = torch.unbind(_yx)
changed[y, x] = c
return changed
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_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