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Source code for mmocr.models.textdet.losses.db_loss
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from torch import nn
from mmocr.models.builder import LOSSES
from mmocr.models.common.losses.dice_loss import DiceLoss
[docs]@LOSSES.register_module()
class DBLoss(nn.Module):
"""The class for implementing DBNet loss.
This is partially adapted from https://github.com/MhLiao/DB.
Args:
alpha (float): The binary loss coef.
beta (float): The threshold loss coef.
reduction (str): The way to reduce the loss.
negative_ratio (float): The ratio of positives to negatives.
eps (float): Epsilon in the threshold loss function.
bbce_loss (bool): Whether to use balanced bce for probability loss.
If False, dice loss will be used instead.
"""
def __init__(self,
alpha=1,
beta=1,
reduction='mean',
negative_ratio=3.0,
eps=1e-6,
bbce_loss=False):
super().__init__()
assert reduction in ['mean',
'sum'], " reduction must in ['mean','sum']"
self.alpha = alpha
self.beta = beta
self.reduction = reduction
self.negative_ratio = negative_ratio
self.eps = eps
self.bbce_loss = bbce_loss
self.dice_loss = DiceLoss(eps=eps)
[docs] def bitmasks2tensor(self, bitmasks, target_sz):
"""Convert Bitmasks to tensor.
Args:
bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is
for one img.
target_sz (tuple(int, int)): The target tensor of size
:math:`(H, W)`.
Returns:
list[Tensor]: The list of kernel tensors. Each element stands for
one kernel level.
"""
assert isinstance(bitmasks, list)
assert isinstance(target_sz, tuple)
batch_size = len(bitmasks)
num_levels = len(bitmasks[0])
result_tensors = []
for level_inx in range(num_levels):
kernel = []
for batch_inx in range(batch_size):
mask = torch.from_numpy(bitmasks[batch_inx].masks[level_inx])
mask_sz = mask.shape
pad = [
0, target_sz[1] - mask_sz[1], 0, target_sz[0] - mask_sz[0]
]
mask = F.pad(mask, pad, mode='constant', value=0)
kernel.append(mask)
kernel = torch.stack(kernel)
result_tensors.append(kernel)
return result_tensors
def balance_bce_loss(self, pred, gt, mask):
positive = (gt * mask)
negative = ((1 - gt) * mask)
positive_count = int(positive.float().sum())
negative_count = min(
int(negative.float().sum()),
int(positive_count * self.negative_ratio))
assert gt.max() <= 1 and gt.min() >= 0
assert pred.max() <= 1 and pred.min() >= 0
loss = F.binary_cross_entropy(pred, gt, reduction='none')
positive_loss = loss * positive.float()
negative_loss = loss * negative.float()
negative_loss, _ = torch.topk(negative_loss.view(-1), negative_count)
balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
positive_count + negative_count + self.eps)
return balance_loss
def l1_thr_loss(self, pred, gt, mask):
thr_loss = torch.abs((pred - gt) * mask).sum() / (
mask.sum() + self.eps)
return thr_loss
[docs] def forward(self, preds, downsample_ratio, gt_shrink, gt_shrink_mask,
gt_thr, gt_thr_mask):
"""Compute DBNet loss.
Args:
preds (Tensor): The output tensor with size :math:`(N, 3, H, W)`.
downsample_ratio (float): The downsample ratio for the
ground truths.
gt_shrink (list[BitmapMasks]): The mask list with each element
being the shrunk text mask for one img.
gt_shrink_mask (list[BitmapMasks]): The effective mask list with
each element being the shrunk effective mask for one img.
gt_thr (list[BitmapMasks]): The mask list with each element
being the threshold text mask for one img.
gt_thr_mask (list[BitmapMasks]): The effective mask list with
each element being the threshold effective mask for one img.
Returns:
dict: The dict for dbnet losses with "loss_prob", "loss_db" and
"loss_thresh".
"""
assert isinstance(downsample_ratio, float)
assert isinstance(gt_shrink, list)
assert isinstance(gt_shrink_mask, list)
assert isinstance(gt_thr, list)
assert isinstance(gt_thr_mask, list)
pred_prob = preds[:, 0, :, :]
pred_thr = preds[:, 1, :, :]
pred_db = preds[:, 2, :, :]
feature_sz = preds.size()
keys = ['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask']
gt = {}
for k in keys:
gt[k] = eval(k)
gt[k] = [item.rescale(downsample_ratio) for item in gt[k]]
gt[k] = self.bitmasks2tensor(gt[k], feature_sz[2:])
gt[k] = [item.to(preds.device) for item in gt[k]]
gt['gt_shrink'][0] = (gt['gt_shrink'][0] > 0).float()
if self.bbce_loss:
loss_prob = self.balance_bce_loss(pred_prob, gt['gt_shrink'][0],
gt['gt_shrink_mask'][0])
else:
loss_prob = self.dice_loss(pred_prob, gt['gt_shrink'][0],
gt['gt_shrink_mask'][0])
loss_db = self.dice_loss(pred_db, gt['gt_shrink'][0],
gt['gt_shrink_mask'][0])
loss_thr = self.l1_thr_loss(pred_thr, gt['gt_thr'][0],
gt['gt_thr_mask'][0])
results = dict(
loss_prob=self.alpha * loss_prob,
loss_db=loss_db,
loss_thr=self.beta * loss_thr)
return results