Source code for mmedit.models.mattors.indexnet

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import auto_fp16

from ..builder import build_loss
from ..registry import MODELS
from .base_mattor import BaseMattor
from .utils import get_unknown_tensor

[docs]@MODELS.register_module() class IndexNet(BaseMattor): """IndexNet matting model. This implementation follows: Indices Matter: Learning to Index for Deep Image Matting Args: backbone (dict): Config of backbone. train_cfg (dict): Config of training. In 'train_cfg', 'train_backbone' should be specified. test_cfg (dict): Config of testing. pretrained (str): path of pretrained model. loss_alpha (dict): Config of the alpha prediction loss. Default: None. loss_comp (dict): Config of the composition loss. Default: None. """ def __init__(self, backbone, train_cfg=None, test_cfg=None, pretrained=None, loss_alpha=None, loss_comp=None): super().__init__(backbone, None, train_cfg, test_cfg, pretrained) self.loss_alpha = ( build_loss(loss_alpha) if loss_alpha is not None else None) self.loss_comp = ( build_loss(loss_comp) if loss_comp is not None else None) # support fp16 self.fp16_enabled = False def forward_dummy(self, inputs): return self.backbone(inputs)
[docs] @auto_fp16(apply_to=('merged', 'trimap')) def forward_train(self, merged, trimap, meta, alpha, ori_merged, fg, bg): """Forward function for training IndexNet model. Args: merged (Tensor): Input images tensor with shape (N, C, H, W). Typically these should be mean centered and std scaled. trimap (Tensor): Tensor of trimap with shape (N, 1, H, W). meta (list[dict]): Meta data about the current data batch. alpha (Tensor): Tensor of alpha with shape (N, 1, H, W). ori_merged (Tensor): Tensor of origin merged images (not normalized) with shape (N, C, H, W). fg (Tensor): Tensor of foreground with shape (N, C, H, W). bg (Tensor): Tensor of background with shape (N, C, H, W). Returns: dict: Contains the loss items and batch information. """ pred_alpha = self.backbone(, trimap), 1)) losses = dict() weight = get_unknown_tensor(trimap, meta) if self.loss_alpha is not None: losses['loss_alpha'] = self.loss_alpha(pred_alpha, alpha, weight) if self.loss_comp is not None: losses['loss_comp'] = self.loss_comp(pred_alpha, fg, bg, ori_merged, weight) return {'losses': losses, 'num_samples': merged.size(0)}
[docs] def forward_test(self, merged, trimap, meta, save_image=False, save_path=None, iteration=None): """Defines the computation performed at every test call. Args: merged (Tensor): Image to predict alpha matte. trimap (Tensor): Trimap of the input image. meta (list[dict]): Meta data about the current data batch. Currently only batch_size 1 is supported. It may contain information needed to calculate metrics (``ori_alpha`` and ``ori_trimap``) or save predicted alpha matte (``merged_path``). save_image (bool, optional): Whether save predicted alpha matte. Defaults to False. save_path (str, optional): The directory to save predicted alpha matte. Defaults to None. iteration (int, optional): If given as None, the saved alpha matte will have the same file name with ``merged_path`` in meta dict. If given as an int, the saved alpha matte would named with postfix ``_{iteration}.png``. Defaults to None. Returns: dict: Contains the predicted alpha and evaluation result. """ pred_alpha = self.backbone(, trimap), 1)) pred_alpha = pred_alpha.cpu().numpy().squeeze() pred_alpha = self.restore_shape(pred_alpha, meta) eval_result = self.evaluate(pred_alpha, meta) if save_image: self.save_image(pred_alpha, meta, save_path, iteration) return {'pred_alpha': pred_alpha, 'eval_result': eval_result}
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