wt_ocr_and_docker/ppocr/losses/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
import numpy as np
import cv2

from .rec_ctc_loss import CTCLoss
from .rec_sar_loss import SARLoss
from .basic_loss import DMLLoss
from .basic_loss import DistanceLoss
from .basic_loss import LossFromOutput
from .det_db_loss import DBLoss
from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss


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,
                 use_log=False,
                 key=None,
                 multi_head=False,
                 dis_head='ctc',
                 maps_name=None,
                 name="dml"):
        super().__init__(act=act, use_log=use_log)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.multi_head = multi_head
        self.dis_head = dis_head
        self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
        self.name = name
        self.maps_name = self._check_maps_name(maps_name)

    def _check_model_name_pairs(self, model_name_pairs):
        if not isinstance(model_name_pairs, list):
            return []
        elif isinstance(model_name_pairs[0], list) and isinstance(
                model_name_pairs[0][0], str):
            return model_name_pairs
        else:
            return [model_name_pairs]

    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] = outs[:, 0, :, :]
            elif k == "threshold_maps":
                new_outs[k] = outs[:, 1, :, :]
            elif k == "binary_maps":
                new_outs[k] = outs[:, 2, :, :]
            else:
                continue
        return new_outs

    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:
                if self.multi_head:
                    loss = super().forward(out1[self.dis_head],
                                           out2[self.dis_head])
                else:
                    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 _c, k in enumerate(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], self.maps_name, idx)] = loss[key]
                    else:
                        loss_dict["{}_{}_{}".format(self.name, self.maps_name[
                            _c], idx)] = loss

        loss_dict = _sum_loss(loss_dict)

        return loss_dict


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

    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]
            if self.multi_head:
                assert 'ctc' in out, 'multi head has multi out'
                loss = super().forward(out['ctc'], batch[:2] + batch[3:])
            else:
                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 DistillationSARLoss(SARLoss):
    def __init__(self,
                 model_name_list=[],
                 key=None,
                 multi_head=False,
                 name="loss_sar",
                 **kwargs):
        ignore_index = kwargs.get('ignore_index', 92)
        super().__init__(ignore_index=ignore_index)
        self.model_name_list = model_name_list
        self.key = key
        self.name = name
        self.multi_head = multi_head

    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]
            if self.multi_head:
                assert 'sar' in out, 'multi head has multi out'
                loss = super().forward(out['sar'], batch[:1] + batch[2:])
            else:
                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",
                 **kwargs):
        super().__init__()
        self.model_name_list = model_name_list
        self.name = name
        self.key = None

    def forward(self, predicts, 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=[],
                 key=None,
                 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
        self.key = key

    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_dict


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


class DistillationVQASerTokenLayoutLMLoss(VQASerTokenLayoutLMLoss):
    def __init__(self,
                 num_classes,
                 model_name_list=[],
                 key=None,
                 name="loss_ser"):
        super().__init__(num_classes=num_classes)
        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)
            loss_dict["{}_{}".format(self.name, model_name)] = loss["loss"]
        return loss_dict


class DistillationLossFromOutput(LossFromOutput):
    def __init__(self,
                 reduction="none",
                 model_name_list=[],
                 dist_key=None,
                 key="loss",
                 name="loss_re"):
        super().__init__(key=key, reduction=reduction)
        self.model_name_list = model_name_list
        self.name = name
        self.dist_key = dist_key

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


class DistillationSERDMLLoss(DMLLoss):
    """
    """

    def __init__(self,
                 act="softmax",
                 use_log=True,
                 num_classes=7,
                 model_name_pairs=[],
                 key=None,
                 name="loss_dml_ser"):
        super().__init__(act=act, use_log=use_log)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.name = name
        self.num_classes = num_classes
        self.model_name_pairs = model_name_pairs

    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]
            out1 = out1.reshape([-1, out1.shape[-1]])
            out2 = out2.reshape([-1, out2.shape[-1]])

            attention_mask = batch[2]
            if attention_mask is not None:
                active_output = attention_mask.reshape([-1, ]) == 1
                out1 = out1[active_output]
                out2 = out2[active_output]

            loss_dict["{}_{}".format(self.name, idx)] = super().forward(out1,
                                                                        out2)

        return loss_dict


class DistillationVQADistanceLoss(DistanceLoss):
    def __init__(self,
                 mode="l2",
                 model_name_pairs=[],
                 key=None,
                 index=None,
                 name="loss_distance",
                 **kargs):
        super().__init__(mode=mode, **kargs)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.index = index
        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]]
            attention_mask = batch[2]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]
                if self.index is not None:
                    out1 = out1[:, self.index, :, :]
                    out2 = out2[:, self.index, :, :]
                if attention_mask is not None:
                    max_len = attention_mask.shape[-1]
                    out1 = out1[:, :max_len]
                    out2 = out2[:, :max_len]
                out1 = out1.reshape([-1, out1.shape[-1]])
                out2 = out2.reshape([-1, out2.shape[-1]])
            if attention_mask is not None:
                active_output = attention_mask.reshape([-1, ]) == 1
                out1 = out1[active_output]
                out2 = out2[active_output]

            loss = super().forward(out1, out2)
            if isinstance(loss, dict):
                for key in loss:
                    loss_dict["{}_{}nohu_{}".format(self.name, key,
                                                    idx)] = loss[key]
            else:
                loss_dict["{}_{}_{}_{}".format(self.name, pair[0], pair[1],
                                               idx)] = loss
        return loss_dict