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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/losses/drrg_loss.py
"""
import paddle
import paddle.nn.functional as F
from paddle import nn
class DRRGLoss(nn.Layer):
def __init__(self, ohem_ratio=3.0):
super().__init__()
self.ohem_ratio = ohem_ratio
self.downsample_ratio = 1.0
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 paddle.all(pred >= 0) and paddle.all(pred <= 1)
assert paddle.all(gt >= 0) and paddle.all(gt <= 1)
positive = gt * mask
negative = (1 - gt) * mask
positive_count = int(positive.sum())
if positive_count > 0:
loss = F.binary_cross_entropy(pred, gt, reduction='none')
positive_loss = paddle.sum(loss * positive)
negative_loss = loss * negative
negative_count = min(
int(negative.sum()), int(positive_count * self.ohem_ratio))
else:
positive_loss = paddle.to_tensor(0.0)
loss = F.binary_cross_entropy(pred, gt, reduction='none')
negative_loss = loss * negative
negative_count = 100
negative_loss, _ = paddle.topk(
negative_loss.reshape([-1]), negative_count)
balance_loss = (positive_loss + paddle.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.reshape([-1])
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.
"""
batch_size = len(bitmasks)
results = []
kernel = []
for batch_inx in range(batch_size):
mask = bitmasks[batch_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 = paddle.stack(kernel)
results.append(kernel)
return results
def forward(self, preds, labels):
"""Compute Drrg loss.
"""
assert isinstance(preds, tuple)
gt_text_mask, gt_center_region_mask, gt_mask, gt_top_height_map, gt_bot_height_map, gt_sin_map, gt_cos_map = labels[
1:8]
downsample_ratio = self.downsample_ratio
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.shape
# bitmask 2 tensor
mapping = {
'gt_text_mask': paddle.cast(gt_text_mask, 'float32'),
'gt_center_region_mask':
paddle.cast(gt_center_region_mask, 'float32'),
'gt_mask': paddle.cast(gt_mask, 'float32'),
'gt_top_height_map': paddle.cast(gt_top_height_map, 'float32'),
'gt_bot_height_map': paddle.cast(gt_bot_height_map, 'float32'),
'gt_sin_map': paddle.cast(gt_sin_map, 'float32'),
'gt_cos_map': paddle.cast(gt_cos_map, 'float32')
}
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 for item in gt[key]]
scale = paddle.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(
F.sigmoid(pred_text_region), gt['gt_text_mask'][0],
gt['gt_mask'][0])
text_mask = (gt['gt_text_mask'][0] * gt['gt_mask'][0])
negative_text_mask = ((1 - gt['gt_text_mask'][0]) * gt['gt_mask'][0])
loss_center_map = F.binary_cross_entropy(
F.sigmoid(pred_center_region),
gt['gt_center_region_mask'][0],
reduction='none')
if int(text_mask.sum()) > 0:
loss_center_positive = paddle.sum(loss_center_map *
text_mask) / paddle.sum(text_mask)
else:
loss_center_positive = paddle.to_tensor(0.0)
loss_center_negative = paddle.sum(
loss_center_map *
negative_text_mask) / paddle.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])
if int(center_mask.sum()) > 0:
map_sz = pred_top_height_map.shape
ones = paddle.ones(map_sz, dtype='float32')
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 = paddle.sum(
(paddle.log(gt_height + 1) *
(loss_top + loss_bot)) * center_mask) / paddle.sum(center_mask)
loss_sin = paddle.sum(
F.smooth_l1_loss(
pred_sin_map, gt['gt_sin_map'][0],
reduction='none') * center_mask) / paddle.sum(center_mask)
loss_cos = paddle.sum(
F.smooth_l1_loss(
pred_cos_map, gt['gt_cos_map'][0],
reduction='none') * center_mask) / paddle.sum(center_mask)
else:
loss_height = paddle.to_tensor(0.0)
loss_sin = paddle.to_tensor(0.0)
loss_cos = paddle.to_tensor(0.0)
loss_gcn = self.gcn_loss(gcn_data)
loss = loss_text + loss_center + loss_height + loss_sin + loss_cos + loss_gcn
results = dict(
loss=loss,
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
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