distillation_loss.py

#copyright (c) 2021 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.

import paddle
import paddle.nn as nn

from .rec_ctc_loss import CTCLoss
from .basic_loss import DMLLoss
from .basic_loss import DistanceLoss
from .det_db_loss import DBLoss
from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss


def _sum_loss(loss_dict):
    if "loss" in loss_dict.keys():
        return loss_dict
    else:
        loss_dict["loss"] = 0.
        for k, value in loss_dict.items():
            if k == "loss":
                continue
            else:
                loss_dict["loss"] += value
        return loss_dict


class DistillationDMLLoss(DMLLoss):
    """
    """

    def __init__(self,
                 model_name_pairs=[],
                 act=None,
                 key=None,
                 maps_name=None,
                 name="loss_dml"):
        super().__init__(act=act)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name
        self.maps_name = self.maps_name

    def _check_maps_name(self, maps_name):
        if maps_name is None:
            return None
        elif type(maps_name) == str:
            return [maps_name]
        elif type(maps_name) == list:
            return [maps_name]
        else:
            return None

    def _slice_out(self, outs):
        new_outs = {}
        for k in self.maps_name:
            if k == "thrink_maps":
                new_outs[k] = paddle.slice(outs, axes=1, starts=0, ends=1)
            elif k == "threshold_maps":
                new_outs[k] = paddle.slice(outs, axes=1, starts=1, ends=2)
            elif k == "binary_maps":
                new_outs[k] = paddle.slice(outs, axes=1, starts=2, ends=3)
            else:
                continue

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            out1 = predicts[pair[0]]
            out2 = predicts[pair[1]]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]

            if self.maps_name is None:
                loss = super().forward(out1, out2)
                if isinstance(loss, dict):
                    for key in loss:
                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
                                                       idx)] = loss[key]
                else:
                    loss_dict["{}_{}".format(self.name, idx)] = loss
            else:
                outs1 = self._slice_out(out1)
                outs2 = self._slice_out(out2)
                for k in outs1.keys():
                    loss = super().forward(outs1[k], outs2[k])
                    if isinstance(loss, dict):
                        for key in loss:
                            loss_dict["{}_{}_{}_{}_{}".format(key, pair[
                                0], pair[1], map_name, idx)] = loss[key]
                    else:
                        loss_dict["{}_{}_{}".format(self.name, map_name,
                                                    idx)] = loss

        loss_dict = _sum_loss(loss_dict)

        return loss_dict


class DistillationCTCLoss(CTCLoss):
    def __init__(self, model_name_list=[], key=None, name="loss_ctc"):
        super().__init__()
        self.model_name_list = model_name_list
        self.key = key
        self.name = name

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, model_name in enumerate(self.model_name_list):
            out = predicts[model_name]
            if self.key is not None:
                out = out[self.key]
            loss = super().forward(out, batch)
            if isinstance(loss, dict):
                for key in loss:
                    loss_dict["{}_{}_{}".format(self.name, model_name,
                                                idx)] = loss[key]
            else:
                loss_dict["{}_{}".format(self.name, model_name)] = loss
        return loss_dict


class DistillationDBLoss(DBLoss):
    def __init__(self,
                 model_name_list=[],
                 balance_loss=True,
                 main_loss_type='DiceLoss',
                 alpha=5,
                 beta=10,
                 ohem_ratio=3,
                 eps=1e-6,
                 name="db_loss",
                 **kwargs):
        super().__init__()
        self.model_name_list = model_name_list
        self.name = name
        self.key = None

    def forward(self, preicts, batch):
        loss_dict = {}
        for idx, model_name in enumerate(self.model_name_list):
            out = predicts[model_name]
            if self.key is not None:
                out = out[self.key]
            loss = super().forward(out, batch)

            if isinstance(loss, dict):
                for key in loss.keys():
                    if key == "loss":
                        continue
                    name = "{}_{}_{}".format(self.name, model_name, key)
                    loss_dict[name] = loss[key]
            else:
                loss_dict["{}_{}".format(self.name, model_name)] = loss

        loss_dict = _sum_loss(loss_dict)
        return loss_dict


class DistillationDilaDBLoss(DBLoss):
    def __init__(self,
                 model_name_pairs=[],
                 balance_loss=True,
                 main_loss_type='DiceLoss',
                 alpha=5,
                 beta=10,
                 ohem_ratio=3,
                 eps=1e-6,
                 name="dila_dbloss"):
        super().__init__()
        self.model_name_pairs = model_name_pairs
        self.name = name

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            stu_outs = predicts[pair[0]]
            tch_outs = predicts[pair[1]]
            if self.key is not None:
                stu_preds = stu_outs[self.key]
                tch_preds = tch_outs[self.key]

            stu_shrink_maps = stu_preds[:, 0, :, :]
            stu_binary_maps = stu_preds[:, 2, :, :]

            # dilation to teacher prediction
            dilation_w = np.array([[1, 1], [1, 1]])
            th_shrink_maps = tch_preds[:, 0, :, :]
            th_shrink_maps = th_shrink_maps.numpy() > 0.3  # thresh = 0.3 
            dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
            for i in range(th_shrink_maps.shape[0]):
                dilate_maps[i] = cv2.dilate(
                    th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
            th_shrink_maps = paddle.to_tensor(dilate_maps)

            label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
                1:]

            # calculate the shrink map loss
            bce_loss = self.alpha * self.bce_loss(
                stu_shrink_maps, th_shrink_maps, label_shrink_mask)
            loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
                                              label_shrink_mask)

            # k = f"{self.name}_{pair[0]}_{pair[1]}"
            k = "{}_{}_{}".format(self.name, pair[0], pair[1])
            loss_dict[k] = bce_loss + loss_binary_maps

        loss_dict = _sum_loss(loss_dict)
        return loss


class DistillationDistanceLoss(DistanceLoss):
    """
    """

    def __init__(self,
                 mode="l2",
                 model_name_pairs=[],
                 key=None,
                 name="loss_distance",
                 **kargs):
        super().__init__(mode=mode, **kargs)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name + "_l2"

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            out1 = predicts[pair[0]]
            out2 = predicts[pair[1]]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]
            loss = super().forward(out1, out2)
            if isinstance(loss, dict):
                for key in loss:
                    loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[
                        key]
            else:
                loss_dict["{}_{}_{}_{}".format(self.name, pair[0], pair[1],
                                               idx)] = loss
        return loss_dict