def init_memory(self, train_x: TensorList, masks):
""" Initialize the sample memory used to update the target model """
assert masks.dim() == 4
# Initialize first-frame spatial training samples
self.num_init_samples = train_x.size(0)
init_sample_weights = TensorList(
[x.new_ones(1) / x.shape[0] for x in train_x])
# Sample counters and weights for spatial
self.num_stored_samples = self.num_init_samples.copy()
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights = TensorList(
[x.new_zeros(self.params.sample_memory_size) for x in train_x])
for sw, init_sw, num in zip(self.sample_weights, init_sample_weights,
self.num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList([
x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2],
x.shape[3]) for x in train_x
])
self.target_masks = masks.new_zeros(self.params.sample_memory_size,
masks.shape[-3], masks.shape[-2],
masks.shape[-1])
self.target_masks[:masks.shape[0], :, :, :] = masks
for ts, x in zip(self.training_samples, train_x):
ts[:x.shape[0], ...] = x
def init_memory(self, train_x_rgb: TensorList, train_x_d: TensorList):
# Initialize first-frame spatial training samples
self.num_init_samples = train_x_rgb.size(0)
init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x_rgb])
# Sample counters and weights for spatial
self.num_stored_samples = self.num_init_samples.copy()
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights_rgb = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x_rgb])
self.sample_weights_d = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x_d])
for sw_rgb, sw_d, init_sw, num in zip(self.sample_weights_rgb, self.sample_weights_d, init_sample_weights, self.num_init_samples):
sw_rgb[:num] = init_sw
sw_d[:num] = init_sw
# Initialize memory
self.training_samples_rgb = TensorList(
[x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x_rgb])
self.training_samples_d = TensorList(
[x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x_d])
for ts_rgb, ts_d, x_rgb, x_d in zip(self.training_samples_rgb, self.training_samples_d, train_x_rgb, train_x_d):
ts_rgb[:x_rgb.shape[0],...] = x_rgb
ts_d[:x_d.shape[0],...] = x_d
def init_memory(self, train_x_72_cls: TensorList,
train_x_18_cls: TensorList, train_x_72_reg: TensorList):
# Initialize first-frame spatial training samples
self.num_init_samples = train_x_72_cls.size(0)
init_sample_weights = TensorList([x.new_ones(1) / x.shape[0] for x in train_x_72_cls])
# Sample counters and weights for spatial
# self.num_stored_samples = self.num_init_samples.copy()
# self.previous_replace_ind = [None] * len(self.num_stored_samples)
# self.sample_weights = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x_72_cls])
# for sw, init_sw, num in zip(self.sample_weights, init_sample_weights, self.num_init_samples):
# sw[:num] = init_sw
# print(self.sample_weights)
self.num_stored_samples_cls_72 = self.num_init_samples.copy()
self.num_stored_samples_cls_18 = self.num_init_samples.copy()
self.num_stored_samples_reg_72 = self.num_init_samples.copy()
self.previous_replace_ind_cls_72 = [None] * len(self.num_stored_samples_cls_72)
self.previous_replace_ind_cls_18 = [None] * len(self.num_stored_samples_cls_18)
self.previous_replace_ind_reg_72 = [None] * len(self.num_stored_samples_reg_72)
self.sample_weights_cls_72 = TensorList([x.new_zeros(self.params.sample_memory_size) for x in train_x_72_cls])
for sw, init_sw, num in zip(self.sample_weights_cls_72, init_sample_weights, self.num_init_samples):
sw[:num] = init_sw
self.sample_weights_cls_18 = self.sample_weights_cls_72.clone()
self.sample_weights_reg_72 = self.sample_weights_cls_72.clone()
# initialize classification features memory
self.training_samples_cls_72 = TensorList(
[x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x_72_cls])
for ts, x in zip(self.training_samples_cls_72, train_x_72_cls):
ts[:x.shape[0], ...] = x
self.training_samples_cls_18 = TensorList(
[x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x_18_cls])
for ts, x in zip(self.training_samples_cls_18, train_x_18_cls):
ts[:x.shape[0], ...] = x
self.training_samples_reg_72 = TensorList(
[x.new_zeros(self.params.sample_memory_size, x.shape[1], x.shape[2], x.shape[3]) for x in train_x_72_reg])
for ts, x in zip(self.training_samples_reg_72, train_x_72_reg):
ts[:x.shape[0], ...] = x