def post_process(self, dets, meta, scale=1):
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets = multi_pose_post_process(dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'])
for j in range(1, self.num_classes + 1):
dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 56)
dets[0][j][:, :4] /= scale
dets[0][j][:, 5:39] /= scale
return dets[0]
def post_process(self, dets, meta, scale=1):
# print('-------->>> post_process')
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets = multi_pose_post_process(dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'])
for j in range(1, self.num_classes + 1):
dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 39)
# import pdb; pdb.set_trace()
dets[0][j][:, :4] /= scale
dets[0][j][:, 5:] /= scale
return dets[0]
def post_process(self, dets, meta, scale=1): # 1*100*40
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2]) # dets:dets: (1,100,40)
dets = multi_pose_post_process( # [{1:1*100*39}]
dets.copy(), [meta['c']], [meta['s']], meta['out_height'],
meta['out_width'])
for j in range(1, self.num_classes + 1):
dets[0][j] = np.array(dets[0][j],
dtype=np.float32).reshape(-1, 39) # 100*39
# import pdb; pdb.set_trace()
dets[0][j][:, :4] /= scale #
dets[0][j][:, 5:] /= scale #
return dets[0]
def save_result(self, output, batch, results):
reg = output['reg'] if self.opt.reg_offset else None
hm_hp = output['hm_hp'] if self.opt.hm_hp else None
hp_offset = output['hp_offset'] if self.opt.reg_hp_offset else None
dets = multi_pose_decode(
output['hm'], output['wh'], output['hps'],
reg=reg, hm_hp=hm_hp, hp_offset=hp_offset, K=self.opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets_out = multi_pose_post_process(
dets.copy(), batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2], output['hm'].shape[3])
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
def save_result(self, output, batch, results):
reg = output[3] if self.cfg.LOSS.REG_OFFSET else None
hm_hp = output[4] if self.cfg.LOSS.HM_HP else None
hp_offset = output[5] if self.cfg.LOSS.REG_HP_OFFSET else None
dets = multi_pose_decode(
output[0], output[1], output[2],
reg=reg, hm_hp=hm_hp, hp_offset=hp_offset, K=self.cfg.TEST.TOPK)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets_out = multi_pose_post_process(
dets.copy(), batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output[0].shape[2], output[0].shape[3])
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
def post_process(self, dets, meta, scale=1):
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets = multi_pose_post_process(
dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'],self.opt.down_ratio)
for j in range(1, self.num_classes + 1):
l = 39
if self.opt.mdn:
l += 42 if self.opt.flip_test or self.opt.flip_test_max else 40
dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, l)
# import pdb; pdb.set_trace()
dets[0][j][:, :4] /= scale
dets[0][j][:, 5:39] /= scale
if self.opt.mdn:
if self.opt.flip_test or self.opt.flip_test_max:
dets[0][j][:, 43:81] /= scale
else:
dets[0][j][:, 41:79] /= scale
return dets[0]
def save_result(self, output, batch, results):
opt = self.opt
if opt.task == 'ctdet':
reg = output['reg'] if self.opt.reg_offset else None
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=self.opt.cat_spec_wh,
K=self.opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets_out = ctdet_post_process(dets.copy(),
batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2],
output['hm'].shape[3],
output['hm'].shape[1])
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
elif opt.task == 'multi_pose':
reg = output['reg'] if self.opt.reg_offset else None
hm_hp = output['hm_hp'] if self.opt.hm_hp else None
hp_offset = output['hp_offset'] if self.opt.reg_hp_offset else None
dets = multi_pose_decode(output['hm'],
output['wh'],
output['hps'],
reg=reg,
hm_hp=hm_hp,
hp_offset=hp_offset,
K=self.opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets_out = multi_pose_post_process(
dets.copy(), batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(), output['hm'].shape[2],
output['hm'].shape[3])
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
else:
assert 0, 'task not defined!'
