import torch
from torch import nn
import timm

from hybridnets.model import BiFPN, Regressor, Classifier, BiFPNDecoder
from utils.utils import Anchors
from hybridnets.model import SegmentationHead

from encoders import get_encoder

class HybridNetsBackbone(nn.Module):
    def __init__(self, num_classes=80, compound_coef=0, seg_classes=1, backbone_name=None, **kwargs):
        super(HybridNetsBackbone, self).__init__()
        self.compound_coef = compound_coef

        self.seg_classes = seg_classes

        self.backbone_compound_coef = [0, 1, 2, 3, 4, 5, 6, 6, 7]
        self.fpn_num_filters = [64, 88, 112, 160, 224, 288, 384, 384, 384]
        self.fpn_cell_repeats = [3, 4, 5, 6, 7, 7, 8, 8, 8]
        self.input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536, 1536]
        self.box_class_repeats = [3, 3, 3, 4, 4, 4, 5, 5, 5]
        self.pyramid_levels = [5, 5, 5, 5, 5, 5, 5, 5, 6]
        self.anchor_scale = [1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,]
        self.aspect_ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])
        self.num_scales = len(kwargs.get('scales', [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]))
        conv_channel_coef = {
            # the channels of P3/P4/P5.
            0: [40, 112, 320],
            1: [40, 112, 320],
            2: [48, 120, 352],
            3: [48, 136, 384],
            4: [56, 160, 448],
            5: [64, 176, 512],
            6: [72, 200, 576],
            7: [72, 200, 576],
            8: [80, 224, 640],
        }

        num_anchors = len(self.aspect_ratios) * self.num_scales

        self.bifpn = nn.Sequential(
            *[BiFPN(self.fpn_num_filters[self.compound_coef],
                    conv_channel_coef[compound_coef],
                    True if _ == 0 else False,
                    attention=True if compound_coef < 6 else False,
                    use_p8=compound_coef > 7)
              for _ in range(self.fpn_cell_repeats[compound_coef])])

        self.num_classes = num_classes
        self.regressor = Regressor(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors,
                                   num_layers=self.box_class_repeats[self.compound_coef],
                                   pyramid_levels=self.pyramid_levels[self.compound_coef])

        '''Modified by Dat Vu'''
        # self.decoder = DecoderModule()
        self.bifpndecoder = BiFPNDecoder(pyramid_channels=self.fpn_num_filters[self.compound_coef])

        self.segmentation_head = SegmentationHead(
            in_channels=64,
            out_channels=self.seg_classes+1 if self.seg_classes > 1 else self.seg_classes,
            activation='softmax2d' if self.seg_classes > 1 else 'sigmoid',
            kernel_size=1,
            upsampling=4,
        )

        self.classifier = Classifier(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors,
                                     num_classes=num_classes,
                                     num_layers=self.box_class_repeats[self.compound_coef],
                                     pyramid_levels=self.pyramid_levels[self.compound_coef])

        self.anchors = Anchors(anchor_scale=self.anchor_scale[compound_coef],
                               pyramid_levels=(torch.arange(self.pyramid_levels[self.compound_coef]) + 3).tolist(),
                               **kwargs)

        if backbone_name:
            # Use timm to create another backbone that you prefer
            # https://github.com/rwightman/pytorch-image-models
            self.encoder = timm.create_model(backbone_name, pretrained=True, features_only=True, out_indices=(2,3,4))  # P3,P4,P5
        else:
            # EfficientNet_Pytorch
            self.encoder = get_encoder(
                'efficientnet-b' + str(self.backbone_compound_coef[compound_coef]),
                in_channels=3,
                depth=5,
                weights='imagenet',
            )
    
        self.initialize_decoder(self.bifpndecoder)
        self.initialize_head(self.segmentation_head)
        self.initialize_decoder(self.bifpn)

    def freeze_bn(self):
        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d):
                m.eval()

    def forward(self, inputs):
        max_size = inputs.shape[-1]

        # p1, p2, p3, p4, p5 = self.backbone_net(inputs)
        p2, p3, p4, p5 = self.encoder(inputs)[-4:]  # self.backbone_net(inputs)

        features = (p3, p4, p5)

        features = self.bifpn(features)
        
        p3,p4,p5,p6,p7 = features
        
        outputs = self.bifpndecoder((p2,p3,p4,p5,p6,p7))

        segmentation = self.segmentation_head(outputs)
        
        regression = self.regressor(features)
        classification = self.classifier(features)
        anchors = self.anchors(inputs, inputs.dtype)

        return features, regression, classification, anchors, segmentation
    
    def initialize_decoder(self, module):
        for m in module.modules():

            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)


    def initialize_head(self, module):
        for m in module.modules():
            if isinstance(m, (nn.Linear, nn.Conv2d)):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)