def debug(self, batch, output, iter_id):
opt = self.opt
reg = output['reg'] if opt.reg_offset else None
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=opt.cat_spec_wh,
K=opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets[:, :, :4] *= opt.down_ratio
dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2])
dets_gt[:, :, :4] *= opt.down_ratio
for i in range(1):
debugger = Debugger(dataset=opt.dataset,
ipynb=(opt.debug == 3),
theme=opt.debugger_theme)
img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * opt.std + opt.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')
debugger.add_img(img, img_id='out_pred')
for k in range(len(dets[i])):
if dets[i, k, 4] > opt.center_thresh:
# add_coco_bbox(bbox, cat, conf=1, show_txt=True, img_id='default'):
debugger.add_coco_bbox(dets[i, k, :4],
dets[i, k, -1],
dets[i, k, 4],
img_id='out_pred')
debugger.add_img(img, img_id='out_gt')
for k in range(len(dets_gt[i])):
if dets_gt[i, k, 4] > opt.center_thresh:
# add_coco_bbox(bbox, cat, conf=1, show_txt=True, img_id='default'):
debugger.add_coco_bbox(dets_gt[i, k, :4],
dets_gt[i, k, -1],
dets_gt[i, k, 4],
img_id='out_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):
opt = self.opt
reg = output['reg'] if opt.reg_offset else None
hm_hp = output['hm_hp'] if opt.hm_hp else None
hp_offset = output['hp_offset'] if 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=opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets[:, :, :4] *= opt.input_res / opt.output_res
dets[:, :, 5:39] *= opt.input_res / opt.output_res
dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2])
dets_gt[:, :, :4] *= opt.input_res / opt.output_res
dets_gt[:, :, 5:39] *= opt.input_res / opt.output_res
for i in range(1):
debugger = Debugger(dataset=opt.dataset,
ipynb=(opt.debug == 3),
theme=opt.debugger_theme)
img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * opt.std + opt.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')
debugger.add_img(img, img_id='out_pred')
for k in range(len(dets[i])):
if dets[i, k, 4] > opt.center_thresh:
debugger.add_coco_bbox(dets[i, k, :4],
dets[i, k, -1],
dets[i, k, 4],
img_id='out_pred')
debugger.add_coco_hp(dets[i, k, 5:39], img_id='out_pred')
debugger.add_img(img, img_id='out_gt')
for k in range(len(dets_gt[i])):
if dets_gt[i, k, 4] > opt.center_thresh:
debugger.add_coco_bbox(dets_gt[i, k, :4],
dets_gt[i, k, -1],
dets_gt[i, k, 4],
img_id='out_gt')
debugger.add_coco_hp(dets_gt[i, k, 5:39], img_id='out_gt')
if opt.hm_hp:
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 opt.debug == 4:
debugger.save_all_imgs(opt.debug_dir,
prefix='{}'.format(iter_id))
else:
debugger.show_all_imgs(pause=True)