def process(self,
images,
pre_images=None,
pre_hms=None,
pre_inds=None,
return_time=False):
with torch.no_grad():
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
output = self.model(images, pre_images, pre_hms)[-1]
output = self._sigmoid_output(output)
output.update({'pre_inds': pre_inds})
if self.opt.flip_test:
output = self._flip_output(output)
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
forward_time = time.time()
dets = generic_decode(output, K=self.opt.K, opt=self.opt)
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, pre_images=None, pre_hms=None, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
if self.opt.trades:
self.inference_prehm.appendleft(pre_hms)
while len(self.inference_prehm) < (self.opt.clip_len - 1):
self.inference_preim.appendleft(pre_images)
self.inference_prehm.appendleft(pre_hms)
output, current_feat = self.model(images, pre_images, pre_hms, inference_feats=self.inference_feats, addtional_pre_imgs=self.inference_preim, addtional_pre_hms=self.inference_prehm)
output = output[-1]
output['tracking'] = output['tracking_offset']
self.inference_feats.appendleft(current_feat)
while len(self.inference_feats) < (self.opt.clip_len-1):
self.inference_feats.appendleft(current_feat)
else:
output, current_feat = self.model(images, pre_images, pre_hms)
output = output[-1]
output = self._sigmoid_output(output)
if self.opt.flip_test:
output = self._flip_output(output)
torch.cuda.synchronize()
forward_time = time.time()
dets, cur_det = generic_decode(output, K=self.opt.K, opt=self.opt)
torch.cuda.synchronize()
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, pre_images=None, pre_hms=None, pre_inds=None):
from model.decode import generic_decode
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images, pre_images, pre_hms)[-1]
output = self._sigmoid_output(output)
output.update({'pre_inds': pre_inds})
if self.opt.flip_test:
output = self._flip_output(output)
torch.cuda.synchronize()
dets = generic_decode(output, K=self.opt.K, opt=self.opt)
torch.cuda.synchronize()
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
return output, dets
def debug(self, batch, output, iter_id, dataset):
opt = self.opt
if "pre_hm" in batch:
output.update({"pre_hm": batch["pre_hm"]})
dets = generic_decode(output, K=opt.K, opt=opt)
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
dets_gt = batch["meta"]["gt_det"]
for i in range(1):
debugger = Debugger(opt=opt, dataset=dataset)
img = batch["image"][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * dataset.std + dataset.mean) * 255.0), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(
output["hm"][i].detach().cpu().numpy())
gt = debugger.gen_colormap(batch["hm"][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, "pred_hm")
debugger.add_blend_img(img, gt, "gt_hm")
if "pre_img" in batch:
pre_img = batch["pre_img"][i].detach().cpu().numpy().transpose(
1, 2, 0)
pre_img = np.clip(
((pre_img * dataset.std + dataset.mean) * 255), 0,
255).astype(np.uint8)
debugger.add_img(pre_img, "pre_img_pred")
debugger.add_img(pre_img, "pre_img_gt")
if "pre_hm" in batch:
pre_hm = debugger.gen_colormap(
batch["pre_hm"][i].detach().cpu().numpy())
debugger.add_blend_img(pre_img, pre_hm, "pre_hm")
debugger.add_img(img, img_id="out_pred")
if "ltrb_amodal" in opt.heads:
debugger.add_img(img, img_id="out_pred_amodal")
debugger.add_img(img, img_id="out_gt_amodal")
# Predictions
for k in range(len(dets["scores"][i])):
if dets["scores"][i, k] > opt.vis_thresh:
debugger.add_coco_bbox(
dets["bboxes"][i, k] * opt.down_ratio,
dets["clses"][i, k],
dets["scores"][i, k],
img_id="out_pred",
)
if "ltrb_amodal" in opt.heads:
debugger.add_coco_bbox(
dets["bboxes_amodal"][i, k] * opt.down_ratio,
dets["clses"][i, k],
dets["scores"][i, k],
img_id="out_pred_amodal",
)
if "hps" in opt.heads and int(dets["clses"][i, k]) == 0:
debugger.add_coco_hp(dets["hps"][i, k] *
opt.down_ratio,
img_id="out_pred")
if "tracking" in opt.heads:
debugger.add_arrow(
dets["cts"][i][k] * opt.down_ratio,
dets["tracking"][i][k] * opt.down_ratio,
img_id="out_pred",
)
debugger.add_arrow(
dets["cts"][i][k] * opt.down_ratio,
dets["tracking"][i][k] * opt.down_ratio,
img_id="pre_img_pred",
)
# Ground truth
debugger.add_img(img, img_id="out_gt")
for k in range(len(dets_gt["scores"][i])):
if dets_gt["scores"][i][k] > opt.vis_thresh:
debugger.add_coco_bbox(
dets_gt["bboxes"][i][k] * opt.down_ratio,
dets_gt["clses"][i][k],
dets_gt["scores"][i][k],
img_id="out_gt",
)
if "ltrb_amodal" in opt.heads:
debugger.add_coco_bbox(
dets_gt["bboxes_amodal"][i, k] * opt.down_ratio,
dets_gt["clses"][i, k],
dets_gt["scores"][i, k],
img_id="out_gt_amodal",
)
if "hps" in opt.heads and (int(dets["clses"][i, k]) == 0):
debugger.add_coco_hp(dets_gt["hps"][i][k] *
opt.down_ratio,
img_id="out_gt")
if "tracking" in opt.heads:
debugger.add_arrow(
dets_gt["cts"][i][k] * opt.down_ratio,
dets_gt["tracking"][i][k] * opt.down_ratio,
img_id="out_gt",
)
debugger.add_arrow(
dets_gt["cts"][i][k] * opt.down_ratio,
dets_gt["tracking"][i][k] * opt.down_ratio,
img_id="pre_img_gt",
)
if "hm_hp" in opt.heads:
pred = debugger.gen_colormap_hp(
output["hm_hp"][i].detach().cpu().numpy())
gt = debugger.gen_colormap_hp(
batch["hm_hp"][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, "pred_hmhp")
debugger.add_blend_img(img, gt, "gt_hmhp")
if "rot" in opt.heads and "dim" in opt.heads and "dep" in opt.heads:
dets_gt = {k: dets_gt[k].cpu().numpy() for k in dets_gt}
calib = (batch["meta"]["calib"].detach().numpy()
if "calib" in batch["meta"] else None)
det_pred = generic_post_process(
opt,
dets,
batch["meta"]["c"].cpu().numpy(),
batch["meta"]["s"].cpu().numpy(),
output["hm"].shape[2],
output["hm"].shape[3],
self.opt.num_classes,
calib,
)
det_gt = generic_post_process(
opt,
dets_gt,
batch["meta"]["c"].cpu().numpy(),
batch["meta"]["s"].cpu().numpy(),
output["hm"].shape[2],
output["hm"].shape[3],
self.opt.num_classes,
calib,
)
debugger.add_3d_detection(
batch["meta"]["img_path"][i],
batch["meta"]["flipped"][i],
det_pred[i],
calib[i],
vis_thresh=opt.vis_thresh,
img_id="add_pred",
)
debugger.add_3d_detection(
batch["meta"]["img_path"][i],
batch["meta"]["flipped"][i],
det_gt[i],
calib[i],
vis_thresh=opt.vis_thresh,
img_id="add_gt",
)
debugger.add_bird_views(
det_pred[i],
det_gt[i],
vis_thresh=opt.vis_thresh,
img_id="bird_pred_gt",
)
if opt.debug == 4:
debugger.save_all_imgs(opt.debug_dir,
prefix="{}".format(iter_id))
else:
debugger.show_all_imgs(pause=True)
def debug(self, batch, output, iter_id, dataset):
opt = self.opt
if 'pre_hm' in batch:
output.update({'pre_hm': batch['pre_hm']})
dets = generic_decode(output, K=opt.K, opt=opt)
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
dets_gt = batch['meta']['gt_det']
for i in range(1):
debugger = Debugger(opt=opt, dataset=dataset)
img = batch['image'][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * dataset.std + dataset.mean) * 255.), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(
output['hm'][i].detach().cpu().numpy())
gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hm')
debugger.add_blend_img(img, gt, 'gt_hm')
if 'pre_img' in batch:
pre_img = batch['pre_img'][i].detach().cpu().numpy().transpose(
1, 2, 0)
pre_img = np.clip(
((pre_img * dataset.std + dataset.mean) * 255), 0,
255).astype(np.uint8)
debugger.add_img(pre_img, 'pre_img_pred')
debugger.add_img(pre_img, 'pre_img_gt')
if 'pre_hm' in batch:
pre_hm = debugger.gen_colormap(
batch['pre_hm'][i].detach().cpu().numpy())
debugger.add_blend_img(pre_img, pre_hm, 'pre_hm')
debugger.add_img(img, img_id='out_pred')
if 'ltrb_amodal' in opt.heads:
debugger.add_img(img, img_id='out_pred_amodal')
debugger.add_img(img, img_id='out_gt_amodal')
# Predictions
for k in range(len(dets['scores'][i])):
if dets['scores'][i, k] > opt.vis_thresh:
debugger.add_coco_bbox(dets['bboxes'][i, k] *
opt.down_ratio,
dets['clses'][i, k],
dets['scores'][i, k],
img_id='out_pred')
if 'ltrb_amodal' in opt.heads:
debugger.add_coco_bbox(dets['bboxes_amodal'][i, k] *
opt.down_ratio,
dets['clses'][i, k],
dets['scores'][i, k],
img_id='out_pred_amodal')
if 'hps' in opt.heads and int(dets['clses'][i, k]) == 0:
debugger.add_coco_hp(dets['hps'][i, k] *
opt.down_ratio,
img_id='out_pred')
if 'tracking' in opt.heads:
debugger.add_arrow(dets['cts'][i][k] * opt.down_ratio,
dets['tracking'][i][k] *
opt.down_ratio,
img_id='out_pred')
debugger.add_arrow(dets['cts'][i][k] * opt.down_ratio,
dets['tracking'][i][k] *
opt.down_ratio,
img_id='pre_img_pred')
# Ground truth
debugger.add_img(img, img_id='out_gt')
for k in range(len(dets_gt['scores'][i])):
if dets_gt['scores'][i][k] > opt.vis_thresh:
debugger.add_coco_bbox(dets_gt['bboxes'][i][k] *
opt.down_ratio,
dets_gt['clses'][i][k],
dets_gt['scores'][i][k],
img_id='out_gt')
if 'ltrb_amodal' in opt.heads:
debugger.add_coco_bbox(dets_gt['bboxes_amodal'][i, k] *
opt.down_ratio,
dets_gt['clses'][i, k],
dets_gt['scores'][i, k],
img_id='out_gt_amodal')
if 'hps' in opt.heads and \
(int(dets['clses'][i, k]) == 0):
debugger.add_coco_hp(dets_gt['hps'][i][k] *
opt.down_ratio,
img_id='out_gt')
if 'tracking' in opt.heads:
debugger.add_arrow(
dets_gt['cts'][i][k] * opt.down_ratio,
dets_gt['tracking'][i][k] * opt.down_ratio,
img_id='out_gt')
debugger.add_arrow(
dets_gt['cts'][i][k] * opt.down_ratio,
dets_gt['tracking'][i][k] * opt.down_ratio,
img_id='pre_img_gt')
if 'hm_hp' in opt.heads:
pred = debugger.gen_colormap_hp(
output['hm_hp'][i].detach().cpu().numpy())
gt = debugger.gen_colormap_hp(
batch['hm_hp'][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hmhp')
debugger.add_blend_img(img, gt, 'gt_hmhp')
if 'rot' in opt.heads and 'dim' in opt.heads and 'dep' in opt.heads:
dets_gt = {k: dets_gt[k].cpu().numpy() for k in dets_gt}
calib = batch['meta']['calib'].detach().numpy() \
if 'calib' in batch['meta'] else None
det_pred = generic_post_process(
opt, dets, batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(), output['hm'].shape[2],
output['hm'].shape[3], self.opt.num_classes, calib)
det_gt = generic_post_process(opt, dets_gt,
batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2],
output['hm'].shape[3],
self.opt.num_classes, calib)
debugger.add_3d_detection(batch['meta']['img_path'][i],
batch['meta']['flipped'][i],
det_pred[i],
calib[i],
vis_thresh=opt.vis_thresh,
img_id='add_pred')
debugger.add_3d_detection(batch['meta']['img_path'][i],
batch['meta']['flipped'][i],
det_gt[i],
calib[i],
vis_thresh=opt.vis_thresh,
img_id='add_gt')
debugger.add_bird_views(det_pred[i],
det_gt[i],
vis_thresh=opt.vis_thresh,
img_id='bird_pred_gt')
if opt.debug == 4:
debugger.save_all_imgs(opt.debug_dir,
prefix='{}'.format(iter_id))
else:
debugger.show_all_imgs(pause=True)
