Python mmdet.core.mask_cross_entropy() Examples

The following are 6 code examples of mmdet.core.mask_cross_entropy(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mmdet.core , or try the search function .
Example #1
Source File: fcn_mask_head.py    From Grid-R-CNN with Apache License 2.0 5 votes vote down vote up 
def loss(self, mask_pred, mask_targets, labels):
        loss = dict()
        if self.class_agnostic:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
        loss['loss_mask'] = loss_mask
        return loss
Example #2
Source File: fcn_mask_head.py    From Reasoning-RCNN with Apache License 2.0 5 votes vote down vote up 
def loss(self, mask_pred, mask_targets, labels):
        loss = dict()
        if self.class_agnostic:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
        loss['loss_mask'] = loss_mask
        return loss
Example #3
Source File: fcn_mask_head.py    From kaggle-imaterialist with MIT License 5 votes vote down vote up 
def loss(self, mask_pred, mask_targets, labels):
        loss = dict()
        if self.class_agnostic:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
        loss['loss_mask'] = loss_mask
        return loss
Example #4
Source File: fcn_mask_head.py    From hrnet with MIT License 5 votes vote down vote up 
def loss(self, mask_pred, mask_targets, labels):
        loss = dict()
        if self.class_agnostic:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
        loss['loss_mask'] = loss_mask
        return loss
Example #5
Source File: fcn_mask_head.py    From AugFPN with Apache License 2.0 5 votes vote down vote up 
def loss(self, mask_pred, mask_targets, labels):
        loss = dict()
        if self.class_agnostic:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
        loss['loss_mask'] = loss_mask
        return loss
Example #6
Source File: fcn_mask_head.py    From AugFPN with Apache License 2.0 5 votes vote down vote up 
def loss_aux(self, mask_pred, mask_targets, labels, alpha=0.25):
        loss = dict()
        mask_pred_level0 = mask_pred[0::4,:]
        mask_pred_level1 = mask_pred[1::4,:]
        mask_pred_level2 = mask_pred[2::4,:]
        mask_pred_level3 = mask_pred[3::4,:]
   
        if self.class_agnostic:
            loss_mask_level0 = mask_cross_entropy(mask_pred_level0, mask_targets,
                                           torch.zeros_like(labels))
            loss_mask_level1 = mask_cross_entropy(mask_pred_level1, mask_targets,
                                           torch.zeros_like(labels))
            loss_mask_level2 = mask_cross_entropy(mask_pred_level2, mask_targets,
                                           torch.zeros_like(labels))
            loss_mask_level3 = mask_cross_entropy(mask_pred_level3, mask_targets,
                                           torch.zeros_like(labels))
        else:
            loss_mask_level0 = mask_cross_entropy(mask_pred_level0, mask_targets, labels)
            loss_mask_level1 = mask_cross_entropy(mask_pred_level1, mask_targets, labels)
            loss_mask_level2 = mask_cross_entropy(mask_pred_level2, mask_targets, labels)
            loss_mask_level3 = mask_cross_entropy(mask_pred_level3, mask_targets, labels)

        loss['loss_mask_level0'] = loss_mask_level0 * alpha
        loss['loss_mask_level1'] = loss_mask_level1 * alpha
        loss['loss_mask_level2'] = loss_mask_level2 * alpha
        loss['loss_mask_level3'] = loss_mask_level3 * alpha

        return loss