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hub / github.com/xingyizhou/CenterNet / debug

Method debug

src/lib/trains/exdet.py:53–86  ·  view source on GitHub ↗
(self, batch, output, iter_id)

Source from the content-addressed store, hash-verified

51 return loss_states, loss
52
53 def debug(self, batch, output, iter_id):
54 opt = self.opt
55 detections = self.decode(output['hm_t'], output['hm_l'],
56 output['hm_b'], output['hm_r'],
57 output['hm_c']).detach().cpu().numpy()
58 detections[:, :, :4] *= opt.input_res / opt.output_res
59 for i in range(1):
60 debugger = Debugger(
61 dataset=opt.dataset, ipynb=(opt.debug==3), theme=opt.debugger_theme)
62 pred_hm = np.zeros((opt.input_res, opt.input_res, 3), dtype=np.uint8)
63 gt_hm = np.zeros((opt.input_res, opt.input_res, 3), dtype=np.uint8)
64 img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0)
65 img = ((img * self.opt.std + self.opt.mean) * 255.).astype(np.uint8)
66 for p in self.parts:
67 tag = 'hm_{}'.format(p)
68 pred = debugger.gen_colormap(output[tag][i].detach().cpu().numpy())
69 gt = debugger.gen_colormap(batch[tag][i].detach().cpu().numpy())
70 if p != 'c':
71 pred_hm = np.maximum(pred_hm, pred)
72 gt_hm = np.maximum(gt_hm, gt)
73 if p == 'c' or opt.debug > 2:
74 debugger.add_blend_img(img, pred, 'pred_{}'.format(p))
75 debugger.add_blend_img(img, gt, 'gt_{}'.format(p))
76 debugger.add_blend_img(img, pred_hm, 'pred')
77 debugger.add_blend_img(img, gt_hm, 'gt')
78 debugger.add_img(img, img_id='out')
79 for k in range(len(detections[i])):
80 if detections[i, k, 4] > 0.1:
81 debugger.add_coco_bbox(detections[i, k, :4], detections[i, k, -1],
82 detections[i, k, 4], img_id='out')
83 if opt.debug == 4:
84 debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id))
85 else:
86 debugger.show_all_imgs(pause=True)

Callers

nothing calls this directly

Calls 7

gen_colormapMethod · 0.95
add_blend_imgMethod · 0.95
add_imgMethod · 0.95
add_coco_bboxMethod · 0.95
save_all_imgsMethod · 0.95
show_all_imgsMethod · 0.95
DebuggerClass · 0.90

Tested by

no test coverage detected