def process(self, images, return_time=False):
with torch.no_grad():
output = self.model(images)[-1]
hm_obj = output['hm'].sigmoid_()
hm_human = output['hm_human'].sigmoid_()
hm_rel = output['hm_rel'].sigmoid_()
wh = output['wh']
reg = output['reg'] if self.opt.reg_offset else None
sub_offset = output['sub_offset']
obj_offset = output['obj_offset']
torch.cuda.synchronize()
forward_time = time.time()
dets_obj, dets_sub, rel = hoidet_decode(
hm_human,
hm_obj,
wh,
hm_rel,
sub_offset,
obj_offset,
reg=reg,
corremat=self.corre_mat,
is_sub_verb=self.opt.use_verb_sub)
if return_time:
return output, dets_obj, dets_sub, rel, forward_time, images.size(
)[2], images.size()[3]
else:
return output, dets_obj, dets_sub, rel, images.size(
)[2], images.size()[3]
def process(self, images, return_time=False):
with torch.no_grad():
output = self.model(images)[-1]
hm_obj = output['hm'].sigmoid_()
if self.opt.test_mul:
hm_rel = output['hm_rel_refine1'].sigmoid_() * output[
'hm_rel_refine'].sigmoid_() * output['hm_rel'].sigmoid_()
elif self.opt.test_add:
hm_rel = (output['hm_rel_refine_1'] * 0.1 +
output['hm_rel'] * 0.2 +
output['hm_rel_refine']).sigmoid_()
elif self.opt.test_refine:
hm_rel = output['hm_rel_refine'].sigmoid_()
else:
hm_rel = output['hm_rel'].sigmoid_()
wh = output['wh']
reg = output['reg'] if self.opt.reg_offset else None
sub_offset = output['sub_offset']
obj_offset = output['obj_offset']
torch.cuda.synchronize()
forward_time = time.time()
if self.opt.hm_rel_dcn25_i_casc_match or self.opt.hm_rel_dcn25_i_match:
dets_obj, dets_sub, rel = hoidet_decode_match(
hm_obj,
wh,
hm_rel,
sub_offset,
obj_offset,
reg=reg,
corremat=self.corre_mat,
is_sub_verb=self.opt.use_verb_sub,
K_rel=self.opt.test_top_K_rel)
else:
dets_obj, dets_sub, rel = hoidet_decode(
hm_obj,
wh,
hm_rel,
sub_offset,
obj_offset,
reg=reg,
corremat=self.corre_mat,
is_sub_verb=self.opt.use_verb_sub,
K_rel=self.opt.test_top_K_rel)
if return_time:
return output, dets_obj, dets_sub, rel, forward_time, images.size(
)[2], images.size()[3]
else:
return output, dets_obj, dets_sub, rel, images.size(
)[2], images.size()[3]