def prepare_training(self, output, batch):
init = snake_gcn_utils.prepare_training(output, batch)
output.update({
'i_it_4py': init['i_it_4py'],
'i_it_py': init['i_it_py']
})
output.update({
'i_gt_4py': init['i_gt_4py'],
'i_gt_py': init['i_gt_py']
})
return init
def prepare_training(self, output, batch):
init = snake_gcn_utils.prepare_training(output, batch)
if cfg.poly_cls_branch:
spec_num = 80
init['i_gt_py'] = torch.cat(
(init['i_gt_py'][:spec_num], init['neg_i_gt_py'][:spec_num]),
dim=0)
init['c_gt_py'] = torch.cat(
(init['c_gt_py'][:spec_num], init['neg_c_gt_py'][:spec_num]),
dim=0)
init['i_it_py'] = torch.cat(
(init['i_it_py'][:spec_num], init['neg_i_it_py'][:spec_num]),
dim=0)
init['c_it_py'] = torch.cat(
(init['c_it_py'][:spec_num], init['neg_c_it_py'][:spec_num]),
dim=0)
init['py_ind'] = torch.cat(
(init['py_ind'][:spec_num], init['py_ind'][:spec_num]), dim=0)
poly_num = len(init['i_gt_py'])
poly_cls_labels = torch.ones(poly_num)
poly_cls_labels[int(poly_num / 2):] = 0
output.update({'poly_cls_labels': poly_cls_labels.cuda()})
if 0:
input_imgs = batch['inp'].detach().cpu().numpy()
i_gt_py = init['i_gt_py'].detach().cpu().numpy()
i_it_py = init['i_it_py'].detach().cpu().numpy()
neg_i_gt_py = init['neg_i_gt_py'].detach().cpu().numpy()
neg_i_it_py = init['neg_i_it_py'].detach().cpu().numpy()
py_inds = init['py_ind'].detach().cpu().numpy()
np.save('input_imgs.npy', input_imgs)
np.save('i_gt_py.npy', i_gt_py)
np.save('i_it_py.npy', i_it_py)
np.save('neg_i_gt_py.npy', neg_i_gt_py)
np.save('neg_i_it_py.npy', neg_i_it_py)
np.save('py_inds.npy', py_inds)
print('saving training data done!')
exit()
output.update({
'i_it_4py': init['i_it_4py'],
'i_it_py': init['i_it_py']
})
output.update({
'i_gt_4py': init['i_gt_4py'],
'i_gt_py': init['i_gt_py']
})
return init