# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from torch import Tensor

from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead


@MODELS.register_module()
class GARetinaHead(GuidedAnchorHead):
    """Guided-Anchor-based RetinaNet head."""

    def __init__(self,
                 num_classes: int,
                 in_channels: int,
                 stacked_convs: int = 4,
                 conv_cfg: OptConfigType = None,
                 norm_cfg: OptConfigType = None,
                 init_cfg: OptMultiConfig = None,
                 **kwargs) -> None:
        if init_cfg is None:
            init_cfg = dict(
                type='Normal',
                layer='Conv2d',
                std=0.01,
                override=[
                    dict(
                        type='Normal',
                        name='conv_loc',
                        std=0.01,
                        bias_prob=0.01),
                    dict(
                        type='Normal',
                        name='retina_cls',
                        std=0.01,
                        bias_prob=0.01)
                ])
        self.stacked_convs = stacked_convs
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        super().__init__(
            num_classes=num_classes,
            in_channels=in_channels,
            init_cfg=init_cfg,
            **kwargs)

    def _init_layers(self) -> None:
        """Initialize layers of the head."""
        self.relu = nn.ReLU(inplace=True)
        self.cls_convs = nn.ModuleList()
        self.reg_convs = nn.ModuleList()
        for i in range(self.stacked_convs):
            chn = self.in_channels if i == 0 else self.feat_channels
            self.cls_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))
            self.reg_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))

        self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
        num_anchors = self.square_anchor_generator.num_base_priors[0]
        self.conv_shape = nn.Conv2d(self.feat_channels, num_anchors * 2, 1)
        self.feature_adaption_cls = FeatureAdaption(
            self.feat_channels,
            self.feat_channels,
            kernel_size=3,
            deform_groups=self.deform_groups)
        self.feature_adaption_reg = FeatureAdaption(
            self.feat_channels,
            self.feat_channels,
            kernel_size=3,
            deform_groups=self.deform_groups)
        self.retina_cls = MaskedConv2d(
            self.feat_channels,
            self.num_base_priors * self.cls_out_channels,
            3,
            padding=1)
        self.retina_reg = MaskedConv2d(
            self.feat_channels, self.num_base_priors * 4, 3, padding=1)

    def forward_single(self, x: Tensor) -> Tuple[Tensor]:
        """Forward feature map of a single scale level."""
        cls_feat = x
        reg_feat = x
        for cls_conv in self.cls_convs:
            cls_feat = cls_conv(cls_feat)
        for reg_conv in self.reg_convs:
            reg_feat = reg_conv(reg_feat)

        loc_pred = self.conv_loc(cls_feat)
        shape_pred = self.conv_shape(reg_feat)

        cls_feat = self.feature_adaption_cls(cls_feat, shape_pred)
        reg_feat = self.feature_adaption_reg(reg_feat, shape_pred)

        if not self.training:
            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
        else:
            mask = None
        cls_score = self.retina_cls(cls_feat, mask)
        bbox_pred = self.retina_reg(reg_feat, mask)
        return cls_score, bbox_pred, shape_pred, loc_pred

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from torch import Tensor

from mmdet.core import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead


@MODELS.register_module()
class GARetinaHead(GuidedAnchorHead):
    """Guided-Anchor-based RetinaNet head."""

    def __init__(self,
                 num_classes: int,
                 in_channels: int,
                 stacked_convs: int = 4,
                 conv_cfg: OptConfigType = None,
                 norm_cfg: OptConfigType = None,
                 init_cfg: OptMultiConfig = None,
                 **kwargs) -> None:
        if init_cfg is None:
            init_cfg = dict(
                type='Normal',
                layer='Conv2d',
                std=0.01,
                override=[
                    dict(
                        type='Normal',
                        name='conv_loc',
                        std=0.01,
                        bias_prob=0.01),
                    dict(
                        type='Normal',
                        name='retina_cls',
                        std=0.01,
                        bias_prob=0.01)
                ])
        self.stacked_convs = stacked_convs
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        super().__init__(
            num_classes=num_classes,
            in_channels=in_channels,
            init_cfg=init_cfg,
            **kwargs)

    def _init_layers(self) -> None:
        """Initialize layers of the head."""
        self.relu = nn.ReLU(inplace=True)
        self.cls_convs = nn.ModuleList()
        self.reg_convs = nn.ModuleList()
        for i in range(self.stacked_convs):
            chn = self.in_channels if i == 0 else self.feat_channels
            self.cls_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))
            self.reg_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))

        self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
        num_anchors = self.square_anchor_generator.num_base_priors[0]
        self.conv_shape = nn.Conv2d(self.feat_channels, num_anchors * 2, 1)
        self.feature_adaption_cls = FeatureAdaption(
            self.feat_channels,
            self.feat_channels,
            kernel_size=3,
            deform_groups=self.deform_groups)
        self.feature_adaption_reg = FeatureAdaption(
            self.feat_channels,
            self.feat_channels,
            kernel_size=3,
            deform_groups=self.deform_groups)
        self.retina_cls = MaskedConv2d(
            self.feat_channels,
            self.num_base_priors * self.cls_out_channels,
            3,
            padding=1)
        self.retina_reg = MaskedConv2d(
            self.feat_channels, self.num_base_priors * 4, 3, padding=1)

    def forward_single(self, x: Tensor) -> Tuple[Tensor]:
        """Forward feature map of a single scale level."""
        cls_feat = x
        reg_feat = x
        for cls_conv in self.cls_convs:
            cls_feat = cls_conv(cls_feat)
        for reg_conv in self.reg_convs:
            reg_feat = reg_conv(reg_feat)

        loc_pred = self.conv_loc(cls_feat)
        shape_pred = self.conv_shape(reg_feat)

        cls_feat = self.feature_adaption_cls(cls_feat, shape_pred)
        reg_feat = self.feature_adaption_reg(reg_feat, shape_pred)

        if not self.training:
            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
        else:
            mask = None
        cls_score = self.retina_cls(cls_feat, mask)
        bbox_pred = self.retina_reg(reg_feat, mask)
        return cls_score, bbox_pred, shape_pred, loc_pred
