Source code for mmedit.models.mattors.dim

# 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 DIM(BaseMattor): """Deep Image Matting model. .. note:: For ``(self.train_cfg.train_backbone, self.train_cfg.train_refiner)``: * ``(True, False)`` corresponds to the encoder-decoder stage in \ the paper. * ``(False, True)`` corresponds to the refinement stage in the \ paper. * ``(True, True)`` corresponds to the fine-tune stage in the paper. Args: backbone (dict): Config of backbone. refiner (dict): Config of refiner. train_cfg (dict): Config of training. In ``train_cfg``, ``train_backbone`` should be specified. If the model has a refiner, ``train_refiner`` should be specified. test_cfg (dict): Config of testing. In ``test_cfg``, If the model has a refiner, ``train_refiner`` should be specified. 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. loss_refine (dict): Config of the loss of the refiner. Default: None. """ def __init__(self, backbone, refiner=None, train_cfg=None, test_cfg=None, pretrained=None, loss_alpha=None, loss_comp=None, loss_refine=None): super().__init__(backbone, refiner, train_cfg, test_cfg, pretrained) if all(v is None for v in (loss_alpha, loss_comp, loss_refine)): raise ValueError('Please specify one loss for DIM.') if loss_alpha is not None: self.loss_alpha = build_loss(loss_alpha) if loss_comp is not None: self.loss_comp = build_loss(loss_comp) if loss_refine is not None: self.loss_refine = build_loss(loss_refine) # support fp16 self.fp16_enabled = False @auto_fp16() def _forward(self, x, refine): raw_alpha = self.backbone(x) pred_alpha = raw_alpha.sigmoid() if refine: refine_input =[:, :3, :, :], pred_alpha), 1) pred_refine = self.refiner(refine_input, raw_alpha) else: # As ONNX does not support NoneType for output, # we choose to use zero tensor to represent None pred_refine = torch.zeros([]) return pred_alpha, pred_refine def forward_dummy(self, inputs): return self._forward(inputs, self.with_refiner)
[docs] def forward_train(self, merged, trimap, meta, alpha, ori_merged, fg, bg): """Defines the computation performed at every training call. Args: merged (Tensor): of shape (N, C, H, W) encoding input images. Typically these should be mean centered and std scaled. trimap (Tensor): of shape (N, 1, H, W). Tensor of trimap read by opencv. meta (list[dict]): Meta data about the current data batch. alpha (Tensor): of shape (N, 1, H, W). Tensor of alpha read by opencv. ori_merged (Tensor): of shape (N, C, H, W). Tensor of origin merged image read by opencv (not normalized). fg (Tensor): of shape (N, C, H, W). Tensor of fg read by opencv. bg (Tensor): of shape (N, C, H, W). Tensor of bg read by opencv. Returns: dict: Contains the loss items and batch information. """ pred_alpha, pred_refine = self._forward(, trimap), 1), self.train_cfg.train_refiner) weight = get_unknown_tensor(trimap, meta) losses = dict() if self.train_cfg.train_backbone: 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) if self.train_cfg.train_refiner: losses['loss_refine'] = self.loss_refine(pred_refine, alpha, 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, pred_refine = self._forward(, trimap), 1), self.test_cfg.refine) if self.test_cfg.refine: pred_alpha = pred_refine pred_alpha = pred_alpha.detach().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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