import numpy as np
import torch.nn as nn
from mmcv.cnn import normal_init

from ..registry import HEADS
from ..utils import ConvModule, bias_init_with_prob
from .anchor_head import AnchorHead


@HEADS.register_module
class RetinaSepBNHead(AnchorHead):
    """"RetinaHead with separate BN.

    In RetinaHead, conv/norm layers are shared across different FPN levels,
    while in RetinaSepBNHead, conv layers are shared across different FPN
    levels, but BN layers are separated.
    """

    def __init__(self,
                 num_classes,
                 num_ins,
                 in_channels,
                 stacked_convs=4,
                 octave_base_scale=4,
                 scales_per_octave=3,
                 conv_cfg=None,
                 norm_cfg=None,
                 **kwargs):
        self.stacked_convs = stacked_convs
        self.octave_base_scale = octave_base_scale
        self.scales_per_octave = scales_per_octave
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.num_ins = num_ins
        octave_scales = np.array(
            [2**(i / scales_per_octave) for i in range(scales_per_octave)])
        anchor_scales = octave_scales * octave_base_scale
        super(RetinaSepBNHead, self).__init__(
            num_classes, in_channels, anchor_scales=anchor_scales, **kwargs)

    def _init_layers(self):
        self.relu = nn.ReLU(inplace=True)
        self.cls_convs = nn.ModuleList()
        self.reg_convs = nn.ModuleList()
        for i in range(self.num_ins):
            cls_convs = nn.ModuleList()
            reg_convs = nn.ModuleList()
            for i in range(self.stacked_convs):
                chn = self.in_channels if i == 0 else self.feat_channels
                cls_convs.append(
                    ConvModule(
                        chn,
                        self.feat_channels,
                        3,
                        stride=1,
                        padding=1,
                        conv_cfg=self.conv_cfg,
                        norm_cfg=self.norm_cfg))
                reg_convs.append(
                    ConvModule(
                        chn,
                        self.feat_channels,
                        3,
                        stride=1,
                        padding=1,
                        conv_cfg=self.conv_cfg,
                        norm_cfg=self.norm_cfg))
            self.cls_convs.append(cls_convs)
            self.reg_convs.append(reg_convs)
        for i in range(self.stacked_convs):
            for j in range(1, self.num_ins):
                self.cls_convs[j][i].conv = self.cls_convs[0][i].conv
                self.reg_convs[j][i].conv = self.reg_convs[0][i].conv
        self.retina_cls = nn.Conv2d(
            self.feat_channels,
            self.num_anchors * self.cls_out_channels,
            3,
            padding=1)
        self.retina_reg = nn.Conv2d(
            self.feat_channels, self.num_anchors * 4, 3, padding=1)

    def init_weights(self):
        for m in self.cls_convs[0]:
            normal_init(m.conv, std=0.01)
        for m in self.reg_convs[0]:
            normal_init(m.conv, std=0.01)
        bias_cls = bias_init_with_prob(0.01)
        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
        normal_init(self.retina_reg, std=0.01)

    def forward(self, feats):
        cls_scores = []
        bbox_preds = []
        for i, x in enumerate(feats):
            cls_feat = feats[i]
            reg_feat = feats[i]
            for cls_conv in self.cls_convs[i]:
                cls_feat = cls_conv(cls_feat)
            for reg_conv in self.reg_convs[i]:
                reg_feat = reg_conv(reg_feat)
            cls_score = self.retina_cls(cls_feat)
            bbox_pred = self.retina_reg(reg_feat)
            cls_scores.append(cls_score)
            bbox_preds.append(bbox_pred)
        return cls_scores, bbox_preds
