# encoding: utf-8
"""
@author:  liaoxingyu
@contact: sherlockliao01@gmail.com
"""

import torch
from torch import nn

from fastreid.layers import *
from fastreid.modeling.heads.build import REID_HEADS_REGISTRY
from fastreid.utils.weight_init import weights_init_kaiming, weights_init_classifier


@REID_HEADS_REGISTRY.register()
class AttrHead(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        # fmt: off
        feat_dim      = cfg.MODEL.BACKBONE.FEAT_DIM
        num_classes   = cfg.MODEL.HEADS.NUM_CLASSES
        pool_type     = cfg.MODEL.HEADS.POOL_LAYER
        cls_type      = cfg.MODEL.HEADS.CLS_LAYER
        with_bnneck   = cfg.MODEL.HEADS.WITH_BNNECK

        if pool_type == 'fastavgpool':   self.pool_layer = FastGlobalAvgPool2d()
        elif pool_type == 'avgpool':     self.pool_layer = nn.AdaptiveAvgPool2d(1)
        elif pool_type == 'maxpool':     self.pool_layer = nn.AdaptiveMaxPool2d(1)
        elif pool_type == 'gempoolP':    self.pool_layer = GeneralizedMeanPoolingP()
        elif pool_type == 'gempool':     self.pool_layer = GeneralizedMeanPooling()
        elif pool_type == "avgmaxpool":  self.pool_layer = AdaptiveAvgMaxPool2d()
        elif pool_type == 'clipavgpool': self.pool_layer = ClipGlobalAvgPool2d()
        elif pool_type == "identity":    self.pool_layer = nn.Identity()
        elif pool_type == "flatten":     self.pool_layer = Flatten()
        else:                            raise KeyError(f"{pool_type} is not supported!")

        # Classification layer
        if cls_type == 'linear':          self.classifier = nn.Linear(feat_dim, num_classes, bias=False)
        elif cls_type == 'arcSoftmax':    self.classifier = ArcSoftmax(cfg, feat_dim, num_classes)
        elif cls_type == 'circleSoftmax': self.classifier = CircleSoftmax(cfg, feat_dim, num_classes)
        elif cls_type == 'cosSoftmax':     self.classifier = CosSoftmax(cfg, feat_dim, num_classes)
        else:                             raise KeyError(f"{cls_type} is not supported!")
        # fmt: on

        bottleneck = []
        if with_bnneck:
            bottleneck.append(nn.BatchNorm1d(num_classes))

        self.bottleneck = nn.Sequential(*bottleneck)

        self.bottleneck.apply(weights_init_kaiming)
        self.classifier.apply(weights_init_classifier)

    def forward(self, features, targets=None):
        """
        See :class:`ReIDHeads.forward`.
        """
        global_feat = self.pool_layer(features)
        global_feat = global_feat[..., 0, 0]

        classifier_name = self.classifier.__class__.__name__
        # fmt: off
        if classifier_name == 'Linear': cls_outputs = self.classifier(global_feat)
        else:                           cls_outputs = self.classifier(global_feat, targets)
        # fmt: on

        cls_outputs = self.bottleneck(cls_outputs)

        if not self.training:
            cls_outputs = torch.sigmoid(cls_outputs)
            return cls_outputs
        else:
            return {
                "cls_outputs": cls_outputs,
            }