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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 |