101 lines
3.7 KiB
Python
101 lines
3.7 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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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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from paddle.nn import functional as F
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class TableAttentionLoss(nn.Layer):
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def __init__(self, structure_weight=1.0, loc_weight=0.0, **kwargs):
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super(TableAttentionLoss, self).__init__()
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self.loss_func = nn.CrossEntropyLoss(weight=None, reduction="none")
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self.structure_weight = structure_weight
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self.loc_weight = loc_weight
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def forward(self, predicts, batch):
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structure_probs = predicts["structure_probs"]
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structure_targets = batch[1].astype("int64")
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structure_targets = structure_targets[:, 1:]
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structure_probs = paddle.reshape(
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structure_probs, [-1, structure_probs.shape[-1]]
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)
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structure_targets = paddle.reshape(structure_targets, [-1])
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structure_loss = self.loss_func(structure_probs, structure_targets)
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structure_loss = paddle.mean(structure_loss) * self.structure_weight
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loc_preds = predicts["loc_preds"]
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loc_targets = batch[2].astype("float32")
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loc_targets_mask = batch[3].astype("float32")
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loc_targets = loc_targets[:, 1:, :]
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loc_targets_mask = loc_targets_mask[:, 1:, :]
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loc_loss = (
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F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight
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)
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total_loss = structure_loss + loc_loss
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return {
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"loss": total_loss,
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"structure_loss": structure_loss,
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"loc_loss": loc_loss,
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}
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class SLALoss(nn.Layer):
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def __init__(self, structure_weight=1.0, loc_weight=0.0, loc_loss="mse", **kwargs):
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super(SLALoss, self).__init__()
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self.loss_func = nn.CrossEntropyLoss(weight=None, reduction="mean")
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self.structure_weight = structure_weight
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self.loc_weight = loc_weight
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self.loc_loss = loc_loss
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self.eps = 1e-12
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def forward(self, predicts, batch):
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structure_probs = predicts["structure_probs"]
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structure_targets = batch[1].astype("int64")
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max_len = batch[-2].max().astype("int32")
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structure_targets = structure_targets[:, 1 : max_len + 2]
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structure_loss = self.loss_func(structure_probs, structure_targets)
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structure_loss = paddle.mean(structure_loss) * self.structure_weight
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loc_preds = predicts["loc_preds"]
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loc_targets = batch[2].astype("float32")
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loc_targets_mask = batch[3].astype("float32")
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loc_targets = loc_targets[:, 1 : max_len + 2]
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loc_targets_mask = loc_targets_mask[:, 1 : max_len + 2]
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loc_loss = (
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F.smooth_l1_loss(
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loc_preds * loc_targets_mask,
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loc_targets * loc_targets_mask,
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reduction="sum",
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)
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* self.loc_weight
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)
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loc_loss = loc_loss / (loc_targets_mask.sum() + self.eps)
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total_loss = structure_loss + loc_loss
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return {
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"loss": total_loss,
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"structure_loss": structure_loss,
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"loc_loss": loc_loss,
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}
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