def _flip_output(self, output):
average_flips = ['hm', 'wh', 'dep', 'dim'] ##TODO consider tracking_wh
neg_average_flips = ['amodel_offset']
single_flips = [
'ltrb', 'nuscenes_att', 'velocity', 'ltrb_amodal', 'reg',
'hp_offset', 'rot', 'tracking', 'pre_hm'
] ## TODO consider iou
for head in output:
if head in average_flips:
output[head] = (output[head][0:1] +
flip_tensor(output[head][1:2])) / 2
if head in neg_average_flips:
flipped_tensor = flip_tensor(output[head][1:2])
flipped_tensor[:, 0::2] *= -1
output[head] = (output[head][0:1] + flipped_tensor) / 2
if head in single_flips:
output[head] = output[head][0:1]
if head == 'hps':
output['hps'] = (output['hps'][0:1] + flip_lr_off(
output['hps'][1:2], self.flip_idx)) / 2
if head == 'hm_hp':
output['hm_hp'] = (output['hm_hp'][0:1] + \
flip_lr(output['hm_hp'][1:2], self.flip_idx)) / 2
return output
def _flip_output(self, output):
average_flips = ["hm", "wh", "dep", "dim"]
neg_average_flips = ["amodel_offset"]
single_flips = [
"ltrb",
"nuscenes_att",
"velocity",
"ltrb_amodal",
"reg",
"hp_offset",
"rot",
"tracking",
"pre_hm",
]
for head in output:
if head in average_flips:
output[head] = (output[head][0:1] +
flip_tensor(output[head][1:2])) / 2
if head in neg_average_flips:
flipped_tensor = flip_tensor(output[head][1:2])
flipped_tensor[:, 0::2] *= -1
output[head] = (output[head][0:1] + flipped_tensor) / 2
if head in single_flips:
output[head] = output[head][0:1]
if head == "hps":
output["hps"] = (output["hps"][0:1] + flip_lr_off(
output["hps"][1:2], self.flip_idx)) / 2
if head == "hm_hp":
output["hm_hp"] = (output["hm_hp"][0:1] + flip_lr(
output["hm_hp"][1:2], self.flip_idx)) / 2
return output
