running_scale_loss += scale_loss.item()
running_center_loss += center_loss.item()
running_rotation_loss += rotation_loss.item()
running_miou += miou.item()
epoch_loss = running_loss / len(data_set)
epoch_l1_loss = running_l1_loss / len(data_set)
epoch_scale_loss = running_scale_loss / len(data_set)
epoch_center_loss = running_center_loss / len(data_set)
epoch_rotation_loss = running_rotation_loss / len(data_set)
epoch_miou = running_miou / len(data_set)
print('Loss: {:.4f}'.format(float(epoch_loss)))
print('L1 Loss: {:.4f}'.format(float(epoch_l1_loss)))
print('Scale L1 Loss: {:.4f}'.format(float(epoch_scale_loss)))
print('Center L1 Loss: {:.4f}'.format(float(epoch_center_loss)))
print('M IOU: {:.4f}'.format(float(epoch_miou)))
if __name__ == '__main__':
model = MeshNet(cfg=cfg['MeshNet'], require_fea=True)
if use_gpu:
model.cuda()
model = nn.DataParallel(model)
model.load_state_dict(
torch.load(os.path.join(root_path, cfg['load_model'])))
model.eval()
test_model(model)
_, feas = model(centers, corners, normals, neighbor_index)
ft_all = feas.cpu().squeeze(0).numpy()
embed_dict[file_id] = ft_all
ids = sorted(list(embed_dict.keys()))
for i in ids:
embed_npy.append(embed_dict[i])
print(f'Number of embeddings in testset: {len(ids)}')
np.save(f'./results/test/embed_{args.fold}.npy', embed_npy)
if __name__ == '__main__':
if args.task == 'Shape':
num_classes = 8
else:
num_classes = 6
model = MeshNet(cfg=cfg['MeshNet'],
num_classes=num_classes,
require_fea=True)
model.cuda()
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.weight))
if not os.path.exists(f'./results/test'):
os.makedirs(f'./results/test')
model.eval()
inference(model)