def debug(self, batch, output, iter_id):
opt = self.opt
wh = output['wh'] if opt.reg_bbox else None
reg = output['reg'] if opt.reg_offset else None
dets = ddd_decode(output['hm'], output['rot'], output['dep'],
output['dim'], wh=wh, reg=reg, K=opt.K)
# x, y, score, r1-r8, depth, dim1-dim3, cls
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
calib = batch['meta']['calib'].detach().numpy()
# x, y, score, rot, depth, dim1, dim2, dim3
# if opt.dataset == 'gta':
# dets[:, 12:15] /= 3
dets_pred = ddd_post_process(
dets.copy(), batch['meta']['c'].detach().numpy(),
batch['meta']['s'].detach().numpy(), calib, opt)
dets_gt = ddd_post_process(
batch['meta']['gt_det'].detach().numpy().copy(),
batch['meta']['c'].detach().numpy(),
batch['meta']['s'].detach().numpy(), calib, opt)
#for i in range(input.size(0)):
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 = ((img * self.opt.std + self.opt.mean) * 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, 'hm_pred')
debugger.add_blend_img(img, gt, 'hm_gt')
# decode
debugger.add_ct_detection(
img, dets[i], show_box=opt.reg_bbox, center_thresh=opt.center_thresh,
img_id='det_pred')
debugger.add_ct_detection(
img, batch['meta']['gt_det'][i].cpu().numpy().copy(),
show_box=opt.reg_bbox, img_id='det_gt')
debugger.add_3d_detection(
batch['meta']['image_path'][i], dets_pred[i], calib[i],
center_thresh=opt.center_thresh, img_id='add_pred')
debugger.add_3d_detection(
batch['meta']['image_path'][i], dets_gt[i], calib[i],
center_thresh=opt.center_thresh, img_id='add_gt')
# debugger.add_bird_view(
# dets_pred[i], center_thresh=opt.center_thresh, img_id='bird_pred')
# debugger.add_bird_view(dets_gt[i], img_id='bird_gt')
debugger.add_bird_views(
dets_pred[i], dets_gt[i],
center_thresh=opt.center_thresh, img_id='bird_pred_gt')
# debugger.add_blend_img(img, pred, 'out', white=True)
debugger.compose_vis_add(
batch['meta']['image_path'][i], dets_pred[i], calib[i],
opt.center_thresh, pred, 'bird_pred_gt', img_id='out')
# debugger.add_img(img, img_id='out')
if opt.debug ==4:
debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id))
else:
debugger.show_all_imgs(pause=True)
def save_result(self, output, batch, results):
opt = self.opt
wh = output['wh'] if opt.reg_bbox else None
reg = output['reg'] if opt.reg_offset else None
dets = ddd_decode(output['hm'],
output['rot'],
output['dep'],
output['dim'],
wh=wh,
reg=reg,
K=opt.K)
# x, y, score, r1-r8, depth, dim1-dim3, cls
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
calib = batch['meta']['calib'].detach().numpy()
# x, y, score, rot, depth, dim1, dim2, dim3
dets_pred = ddd_post_process(dets.copy(),
batch['meta']['c'].detach().numpy(),
batch['meta']['s'].detach().numpy(),
calib, opt)
img_id = batch['meta']['img_id'].detach().numpy()[0]
results[img_id] = dets_pred[0]
for j in range(1, opt.num_classes + 1):
keep_inds = (results[img_id][j][:, -1] > opt.center_thresh)
results[img_id][j] = results[img_id][j][keep_inds]
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images)[-1]
output['hm'] = output['hm'].sigmoid_()
# print(output['hm'][:,:,65:71,168:174])
# print("--------------------------------")
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
# print( output['dep'][:,:,68,171])
# print("---------------------------------------")
wh = output['wh'] if self.opt.reg_bbox else None
reg = output['reg'] if self.opt.reg_offset else None
# print( output['reg'][:,:,68,171])
# assert 0
torch.cuda.synchronize()
forward_time = time.time()
dets = ddd_decode(output['hm'],
output['rot'],
output['dep'],
output['dim'],
wh=wh,
reg=reg,
K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
# 字典
output = self.model(images)[-1]
# heatmap shape: batchsize x 3 x 96 x 320
output['hm'] = output['hm'].sigmoid_()
# dep shape: batchsize x 1 x 96 x 320
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
wh = output['wh'] if self.opt.reg_bbox else None
reg = output['reg'] if self.opt.reg_offset else None
torch.cuda.synchronize()
forward_time = time.time()
# rot shape: batchsize x 8 x 96 x 320
# dim shape: batchsize x 3 x 96 x 320
# dets shape: batchsize x K x 18（torch.Tensor）
dets = ddd_decode(output['hm'],
output['rot'],
output['dep'],
output['dim'],
wh=wh,
reg=reg,
K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images)[-1]
output['hm'] = output['hm'].sigmoid_()
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
wh = output['wh'] if self.opt.reg_bbox else None
reg = output['reg'] if self.opt.reg_offset else None
output['vertex_hm'] = output['vertex_hm'].sigmoid_()
torch.cuda.synchronize()
forward_time = time.time()
dets = ddd_decode(output['hm'],
output['rot'],
output['dep'],
output['dim'],
output['vertex_coordinate'],
output['vertex_hm'],
output['vertex_reg_offset'],
wh=wh,
reg=reg,
K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images)[-1]
print ("xxxxxxxxxxxxxoutputxxxxxxxx\n")
for n,t in output.items():
print (n," || ", t.size())
print ("\nxxxxxxxxxxxxxxxxxxxxxxxx\n")
output['hm'] = output['hm'].sigmoid_()
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
wh = output['wh'] if self.opt.reg_bbox else None
print ("reg offset",self.opt.reg_offset)
reg = output['reg'] if self.opt.reg_offset else None
torch.cuda.synchronize()
forward_time = time.time()
dets = ddd_decode(output['hm'], output['rot'], output['dep'],
output['dim'], wh=wh, reg=reg, K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images)[-1]
output['hm'] = output['hm'].sigmoid_()
#output['dep'] = output['dep'].sigmoid_()
output['rot'] = output['rot'].sigmoid_()
denominator = torch.rsqrt(output['rot'][:, 0:1, :, :]**2 +
output['rot'][:, 0:1, :, :]**2)
output['rot'] = output['rot'] * denominator
output['dim'] = output['dim'].sigmoid_() - 0.5
#TODO depth_tran,rotation_trans,
# print(output['hm'][:,:,65:71,168:174])
# print("--------------------------------")
#output['dep'] = np.exp( output['dep'])
# print( output['dep'][:,:,68,171])
# print("---------------------------------------")
wh = output['wh'] if self.opt.reg_bbox else None
reg = output['reg'] if self.opt.reg_offset else None
# print( output['reg'][:,:,68,171])
# assert 0
torch.cuda.synchronize()
forward_time = time.time()
#decocde_smoke
dets = ddd_decode(output['hm'],
output['rot'],
output['dep'],
output['dim'],
wh=wh,
reg=reg,
K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
