277 lines
9.1 KiB
Python
277 lines
9.1 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/shengtao96/CentripetalText/tree/main/models/loss
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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import numpy as np
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def ohem_single(score, gt_text, training_mask):
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# online hard example mining
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pos_num = int(paddle.sum(gt_text > 0.5)) - int(
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paddle.sum((gt_text > 0.5) & (training_mask <= 0.5)))
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if pos_num == 0:
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# selected_mask = gt_text.copy() * 0 # may be not good
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selected_mask = training_mask
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selected_mask = paddle.cast(
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selected_mask.reshape(
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(1, selected_mask.shape[0], selected_mask.shape[1])), "float32")
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return selected_mask
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neg_num = int(paddle.sum((gt_text <= 0.5) & (training_mask > 0.5)))
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neg_num = int(min(pos_num * 3, neg_num))
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if neg_num == 0:
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selected_mask = training_mask
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selected_mask = paddle.cast(
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selected_mask.reshape(
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(1, selected_mask.shape[0], selected_mask.shape[1])), "float32")
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return selected_mask
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# hard example
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neg_score = score[(gt_text <= 0.5) & (training_mask > 0.5)]
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neg_score_sorted = paddle.sort(-neg_score)
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threshold = -neg_score_sorted[neg_num - 1]
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selected_mask = ((score >= threshold) |
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(gt_text > 0.5)) & (training_mask > 0.5)
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selected_mask = paddle.cast(
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selected_mask.reshape(
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(1, selected_mask.shape[0], selected_mask.shape[1])), "float32")
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return selected_mask
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def ohem_batch(scores, gt_texts, training_masks):
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selected_masks = []
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for i in range(scores.shape[0]):
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selected_masks.append(
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ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[
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i, :, :]))
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selected_masks = paddle.cast(paddle.concat(selected_masks, 0), "float32")
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return selected_masks
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def iou_single(a, b, mask, n_class):
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EPS = 1e-6
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valid = mask == 1
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a = a[valid]
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b = b[valid]
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miou = []
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# iou of each class
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for i in range(n_class):
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inter = paddle.cast(((a == i) & (b == i)), "float32")
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union = paddle.cast(((a == i) | (b == i)), "float32")
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miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS))
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miou = sum(miou) / len(miou)
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return miou
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def iou(a, b, mask, n_class=2, reduce=True):
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batch_size = a.shape[0]
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a = a.reshape((batch_size, -1))
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b = b.reshape((batch_size, -1))
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mask = mask.reshape((batch_size, -1))
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iou = paddle.zeros((batch_size, ), dtype="float32")
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for i in range(batch_size):
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iou[i] = iou_single(a[i], b[i], mask[i], n_class)
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if reduce:
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iou = paddle.mean(iou)
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return iou
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class DiceLoss(nn.Layer):
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def __init__(self, loss_weight=1.0):
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super(DiceLoss, self).__init__()
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self.loss_weight = loss_weight
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def forward(self, input, target, mask, reduce=True):
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batch_size = input.shape[0]
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input = F.sigmoid(input) # scale to 0-1
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input = input.reshape((batch_size, -1))
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target = paddle.cast(target.reshape((batch_size, -1)), "float32")
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mask = paddle.cast(mask.reshape((batch_size, -1)), "float32")
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input = input * mask
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target = target * mask
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a = paddle.sum(input * target, axis=1)
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b = paddle.sum(input * input, axis=1) + 0.001
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c = paddle.sum(target * target, axis=1) + 0.001
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d = (2 * a) / (b + c)
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loss = 1 - d
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loss = self.loss_weight * loss
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if reduce:
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loss = paddle.mean(loss)
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return loss
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class SmoothL1Loss(nn.Layer):
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def __init__(self, beta=1.0, loss_weight=1.0):
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super(SmoothL1Loss, self).__init__()
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self.beta = beta
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self.loss_weight = loss_weight
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np_coord = np.zeros(shape=[640, 640, 2], dtype=np.int64)
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for i in range(640):
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for j in range(640):
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np_coord[i, j, 0] = j
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np_coord[i, j, 1] = i
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np_coord = np_coord.reshape((-1, 2))
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self.coord = self.create_parameter(
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shape=[640 * 640, 2],
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dtype="int32", # NOTE: not support "int64" before paddle 2.3.1
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default_initializer=nn.initializer.Assign(value=np_coord))
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self.coord.stop_gradient = True
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def forward_single(self, input, target, mask, beta=1.0, eps=1e-6):
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batch_size = input.shape[0]
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diff = paddle.abs(input - target) * mask.unsqueeze(1)
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loss = paddle.where(diff < beta, 0.5 * diff * diff / beta,
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diff - 0.5 * beta)
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loss = paddle.cast(loss.reshape((batch_size, -1)), "float32")
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mask = paddle.cast(mask.reshape((batch_size, -1)), "float32")
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loss = paddle.sum(loss, axis=-1)
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loss = loss / (mask.sum(axis=-1) + eps)
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return loss
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def select_single(self, distance, gt_instance, gt_kernel_instance,
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training_mask):
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with paddle.no_grad():
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# paddle 2.3.1, paddle.slice not support:
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# distance[:, self.coord[:, 1], self.coord[:, 0]]
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select_distance_list = []
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for i in range(2):
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tmp1 = distance[i, :]
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tmp2 = tmp1[self.coord[:, 1], self.coord[:, 0]]
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select_distance_list.append(tmp2.unsqueeze(0))
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select_distance = paddle.concat(select_distance_list, axis=0)
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off_points = paddle.cast(
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self.coord, "float32") + 10 * select_distance.transpose((1, 0))
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off_points = paddle.cast(off_points, "int64")
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off_points = paddle.clip(off_points, 0, distance.shape[-1] - 1)
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selected_mask = (
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gt_instance[self.coord[:, 1], self.coord[:, 0]] !=
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gt_kernel_instance[off_points[:, 1], off_points[:, 0]])
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selected_mask = paddle.cast(
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selected_mask.reshape((1, -1, distance.shape[-1])), "int64")
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selected_training_mask = selected_mask * training_mask
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return selected_training_mask
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def forward(self,
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distances,
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gt_instances,
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gt_kernel_instances,
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training_masks,
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gt_distances,
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reduce=True):
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selected_training_masks = []
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for i in range(distances.shape[0]):
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selected_training_masks.append(
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self.select_single(distances[i, :, :, :], gt_instances[i, :, :],
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gt_kernel_instances[i, :, :], training_masks[
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i, :, :]))
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selected_training_masks = paddle.cast(
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paddle.concat(selected_training_masks, 0), "float32")
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loss = self.forward_single(distances, gt_distances,
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selected_training_masks, self.beta)
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loss = self.loss_weight * loss
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with paddle.no_grad():
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batch_size = distances.shape[0]
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false_num = selected_training_masks.reshape((batch_size, -1))
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false_num = false_num.sum(axis=-1)
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total_num = paddle.cast(
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training_masks.reshape((batch_size, -1)), "float32")
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total_num = total_num.sum(axis=-1)
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iou_text = (total_num - false_num) / (total_num + 1e-6)
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if reduce:
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loss = paddle.mean(loss)
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return loss, iou_text
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class CTLoss(nn.Layer):
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def __init__(self):
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super(CTLoss, self).__init__()
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self.kernel_loss = DiceLoss()
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self.loc_loss = SmoothL1Loss(beta=0.1, loss_weight=0.05)
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def forward(self, preds, batch):
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imgs = batch[0]
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out = preds['maps']
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gt_kernels, training_masks, gt_instances, gt_kernel_instances, training_mask_distances, gt_distances = batch[
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1:]
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kernels = out[:, 0, :, :]
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distances = out[:, 1:, :, :]
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# kernel loss
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selected_masks = ohem_batch(kernels, gt_kernels, training_masks)
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loss_kernel = self.kernel_loss(
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kernels, gt_kernels, selected_masks, reduce=False)
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iou_kernel = iou(paddle.cast((kernels > 0), "int64"),
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gt_kernels,
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training_masks,
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reduce=False)
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losses = dict(loss_kernels=loss_kernel, )
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# loc loss
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loss_loc, iou_text = self.loc_loss(
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distances,
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gt_instances,
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gt_kernel_instances,
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training_mask_distances,
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gt_distances,
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reduce=False)
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losses.update(dict(loss_loc=loss_loc, ))
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loss_all = loss_kernel + loss_loc
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losses = {'loss': loss_all}
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return losses
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