def init_label_function(self, train_x):
# Allocate label function
self.y = TensorList([
x.new_zeros(self.params.sample_memory_size, 1, x.shape[2],
x.shape[3]) for x in train_x
])
# Output sigma factor
output_sigma_factor = self.fparams.attribute('output_sigma_factor')
self.sigma = (self.feature_sz / self.img_support_sz *
self.base_target_sz
).prod().sqrt() * output_sigma_factor * torch.ones(2)
# Center pos in normalized coords
target_center_norm = (self.pos - self.pos.round()) / (
self.target_scale * self.img_support_sz)
# Generate label functions
for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz,
self.kernel_size, train_x):
center_pos = sz * target_center_norm + 0.5 * torch.Tensor(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
for i, T in enumerate(self.transforms[:x.shape[0]]):
sample_center = center_pos + torch.Tensor(
T.shift) / self.img_support_sz * sz
y[i, 0,
...] = dcf.label_function_spatial(sz, sig, sample_center)
# Return only the ones to use for initial training
return TensorList(
[y[:x.shape[0], ...] for y, x in zip(self.y, train_x)])
def get_label_function(self, sample_pos, sample_scale):
# Generate label function
train_y = TensorList()
target_center_norm = (self.pos - sample_pos) / (sample_scale * self.img_support_sz)
for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size):
center = sz * target_center_norm + 0.5 * torch.Tensor([(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
train_y.append(dcf.label_function_spatial(sz, sig, center))
return train_y
