| import torch | |
| import numpy as np | |
| from ..interact.s2m.s2m_network import deeplabv3plus_resnet50 as S2M | |
| from util.tensor_util import pad_divide_by, unpad | |
| class S2MController: | |
| """ | |
| A controller for Scribble-to-Mask (for user interaction, not for DAVIS) | |
| Takes the image, previous mask, and scribbles to produce a new mask | |
| ignore_class is usually 255 | |
| 0 is NOT the ignore class -- it is the label for the background | |
| """ | |
| def __init__(self, s2m_net:S2M, num_objects, ignore_class, device='cuda:0'): | |
| self.s2m_net = s2m_net | |
| self.num_objects = num_objects | |
| self.ignore_class = ignore_class | |
| self.device = device | |
| def interact(self, image, prev_mask, scr_mask): | |
| image = image.to(self.device, non_blocking=True) | |
| prev_mask = prev_mask.unsqueeze(0) | |
| h, w = image.shape[-2:] | |
| unaggre_mask = torch.zeros((self.num_objects, h, w), dtype=torch.float32, device=image.device) | |
| for ki in range(1, self.num_objects+1): | |
| p_srb = (scr_mask==ki).astype(np.uint8) | |
| n_srb = ((scr_mask!=ki) * (scr_mask!=self.ignore_class)).astype(np.uint8) | |
| Rs = torch.from_numpy(np.stack([p_srb, n_srb], 0)).unsqueeze(0).float().to(image.device) | |
| inputs = torch.cat([image, (prev_mask==ki).float().unsqueeze(0), Rs], 1) | |
| inputs, pads = pad_divide_by(inputs, 16) | |
| unaggre_mask[ki-1] = unpad(torch.sigmoid(self.s2m_net(inputs)), pads) | |
| return unaggre_mask |