Note

You are reading the documentation for MMOCR 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMOCR 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the maintenance plan, changelog, code and documentation of MMOCR 1.0 for more details.

Source code for mmocr.models.textdet.losses.drrg_loss

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from mmdet.core import BitmapMasks
from torch import nn

from mmocr.models.builder import LOSSES
from mmocr.utils import check_argument


@LOSSES.register_module()
class DRRGLoss(nn.Module):
    """The class for implementing DRRG loss. This is partially adapted from
    https://github.com/GXYM/DRRG licensed under the MIT license.

    DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape Text
    Detection <https://arxiv.org/abs/1908.05900>`_.

    Args:
        ohem_ratio (float): The negative/positive ratio in ohem.
    """

    def __init__(self, ohem_ratio=3.0):
        super().__init__()
        self.ohem_ratio = ohem_ratio

    def balance_bce_loss(self, pred, gt, mask):
        """Balanced Binary-CrossEntropy Loss.

        Args:
            pred (Tensor): Shape of :math:`(1, H, W)`.
            gt (Tensor): Shape of :math:`(1, H, W)`.
            mask (Tensor): Shape of :math:`(1, H, W)`.

        Returns:
            Tensor: Balanced bce loss.
        """
        assert pred.shape == gt.shape == mask.shape
        assert torch.all(pred >= 0) and torch.all(pred <= 1)
        assert torch.all(gt >= 0) and torch.all(gt <= 1)
        positive = gt * mask
        negative = (1 - gt) * mask
        positive_count = int(positive.float().sum())
        gt = gt.float()
        if positive_count > 0:
            loss = F.binary_cross_entropy(pred, gt, reduction='none')
            positive_loss = torch.sum(loss * positive.float())
            negative_loss = loss * negative.float()
            negative_count = min(
                int(negative.float().sum()),
                int(positive_count * self.ohem_ratio))
        else:
            positive_loss = torch.tensor(0.0, device=pred.device)
            loss = F.binary_cross_entropy(pred, gt, reduction='none')
            negative_loss = loss * negative.float()
            negative_count = 100
        negative_loss, _ = torch.topk(negative_loss.view(-1), negative_count)

        balance_loss = (positive_loss + torch.sum(negative_loss)) / (
            float(positive_count + negative_count) + 1e-5)

        return balance_loss

    def gcn_loss(self, gcn_data):
        """CrossEntropy Loss from gcn module.

        Args:
            gcn_data (tuple(Tensor, Tensor)): The first is the
                prediction with shape :math:`(N, 2)` and the
                second is the gt label with shape :math:`(m, n)`
                where :math:`m * n = N`.

        Returns:
            Tensor: CrossEntropy loss.
        """
        gcn_pred, gt_labels = gcn_data
        gt_labels = gt_labels.view(-1).to(gcn_pred.device)
        loss = F.cross_entropy(gcn_pred, gt_labels)

        return loss

    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 check_argument.is_type_list(bitmasks, BitmapMasks)
        assert isinstance(target_sz, tuple)

        batch_size = len(bitmasks)
        num_masks = len(bitmasks[0])

        results = []

        for level_inx in range(num_masks):
            kernel = []
            for batch_inx in range(batch_size):
                mask = torch.from_numpy(bitmasks[batch_inx].masks[level_inx])
                # hxw
                mask_sz = mask.shape
                # left, right, top, bottom
                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)
            results.append(kernel)

        return results

    def forward(self, preds, downsample_ratio, gt_text_mask,
                gt_center_region_mask, gt_mask, gt_top_height_map,
                gt_bot_height_map, gt_sin_map, gt_cos_map):
        """Compute Drrg loss.

        Args:
            preds (tuple(Tensor)): The first is the prediction map
                with shape :math:`(N, C_{out}, H, W)`.
                The second is prediction from GCN module, with
                shape :math:`(N, 2)`.
                The third is ground-truth label with shape :math:`(N, 8)`.
            downsample_ratio (float): The downsample ratio.
            gt_text_mask (list[BitmapMasks]): Text mask.
            gt_center_region_mask (list[BitmapMasks]): Center region mask.
            gt_mask (list[BitmapMasks]): Effective mask.
            gt_top_height_map (list[BitmapMasks]): Top height map.
            gt_bot_height_map (list[BitmapMasks]): Bottom height map.
            gt_sin_map (list[BitmapMasks]): Sinusoid map.
            gt_cos_map (list[BitmapMasks]): Cosine map.

        Returns:
            dict:  A loss dict with ``loss_text``, ``loss_center``,
            ``loss_height``, ``loss_sin``, ``loss_cos``, and ``loss_gcn``.
        """
        assert isinstance(preds, tuple)
        assert isinstance(downsample_ratio, float)
        assert check_argument.is_type_list(gt_text_mask, BitmapMasks)
        assert check_argument.is_type_list(gt_center_region_mask, BitmapMasks)
        assert check_argument.is_type_list(gt_mask, BitmapMasks)
        assert check_argument.is_type_list(gt_top_height_map, BitmapMasks)
        assert check_argument.is_type_list(gt_bot_height_map, BitmapMasks)
        assert check_argument.is_type_list(gt_sin_map, BitmapMasks)
        assert check_argument.is_type_list(gt_cos_map, BitmapMasks)

        pred_maps, gcn_data = preds
        pred_text_region = pred_maps[:, 0, :, :]
        pred_center_region = pred_maps[:, 1, :, :]
        pred_sin_map = pred_maps[:, 2, :, :]
        pred_cos_map = pred_maps[:, 3, :, :]
        pred_top_height_map = pred_maps[:, 4, :, :]
        pred_bot_height_map = pred_maps[:, 5, :, :]
        feature_sz = pred_maps.size()
        device = pred_maps.device

        # bitmask 2 tensor
        mapping = {
            'gt_text_mask': gt_text_mask,
            'gt_center_region_mask': gt_center_region_mask,
            'gt_mask': gt_mask,
            'gt_top_height_map': gt_top_height_map,
            'gt_bot_height_map': gt_bot_height_map,
            'gt_sin_map': gt_sin_map,
            'gt_cos_map': gt_cos_map
        }
        gt = {}
        for key, value in mapping.items():
            gt[key] = value
            if abs(downsample_ratio - 1.0) < 1e-2:
                gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
            else:
                gt[key] = [item.rescale(downsample_ratio) for item in gt[key]]
                gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
                if key in ['gt_top_height_map', 'gt_bot_height_map']:
                    gt[key] = [item * downsample_ratio for item in gt[key]]
            gt[key] = [item.to(device) for item in gt[key]]

        scale = torch.sqrt(1.0 / (pred_sin_map**2 + pred_cos_map**2 + 1e-8))
        pred_sin_map = pred_sin_map * scale
        pred_cos_map = pred_cos_map * scale

        loss_text = self.balance_bce_loss(
            torch.sigmoid(pred_text_region), gt['gt_text_mask'][0],
            gt['gt_mask'][0])

        text_mask = (gt['gt_text_mask'][0] * gt['gt_mask'][0]).float()
        negative_text_mask = ((1 - gt['gt_text_mask'][0]) *
                              gt['gt_mask'][0]).float()
        loss_center_map = F.binary_cross_entropy(
            torch.sigmoid(pred_center_region),
            gt['gt_center_region_mask'][0].float(),
            reduction='none')
        if int(text_mask.sum()) > 0:
            loss_center_positive = torch.sum(
                loss_center_map * text_mask) / torch.sum(text_mask)
        else:
            loss_center_positive = torch.tensor(0.0, device=device)
        loss_center_negative = torch.sum(
            loss_center_map *
            negative_text_mask) / torch.sum(negative_text_mask)
        loss_center = loss_center_positive + 0.5 * loss_center_negative

        center_mask = (gt['gt_center_region_mask'][0] *
                       gt['gt_mask'][0]).float()
        if int(center_mask.sum()) > 0:
            map_sz = pred_top_height_map.size()
            ones = torch.ones(map_sz, dtype=torch.float, device=device)
            loss_top = F.smooth_l1_loss(
                pred_top_height_map / (gt['gt_top_height_map'][0] + 1e-2),
                ones,
                reduction='none')
            loss_bot = F.smooth_l1_loss(
                pred_bot_height_map / (gt['gt_bot_height_map'][0] + 1e-2),
                ones,
                reduction='none')
            gt_height = (
                gt['gt_top_height_map'][0] + gt['gt_bot_height_map'][0])
            loss_height = torch.sum(
                (torch.log(gt_height + 1) *
                 (loss_top + loss_bot)) * center_mask) / torch.sum(center_mask)

            loss_sin = torch.sum(
                F.smooth_l1_loss(
                    pred_sin_map, gt['gt_sin_map'][0], reduction='none') *
                center_mask) / torch.sum(center_mask)
            loss_cos = torch.sum(
                F.smooth_l1_loss(
                    pred_cos_map, gt['gt_cos_map'][0], reduction='none') *
                center_mask) / torch.sum(center_mask)
        else:
            loss_height = torch.tensor(0.0, device=device)
            loss_sin = torch.tensor(0.0, device=device)
            loss_cos = torch.tensor(0.0, device=device)

        loss_gcn = self.gcn_loss(gcn_data)

        results = dict(
            loss_text=loss_text,
            loss_center=loss_center,
            loss_height=loss_height,
            loss_sin=loss_sin,
            loss_cos=loss_cos,
            loss_gcn=loss_gcn)

        return results