import torch
import torch.nn as nn
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152

from architectures.models.SSDNet import DepthWiseConv
from architectures.backbones.MobileNet import mobilenet_v2, ConvBNReLU


# taken from https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD


class DepthWiseConv_noBN(nn.Module):
    """
    depth wise followed by point wise convolution
    """

    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, padding=0):
        super().__init__()
        self.ds_conv = ConvBNReLU(in_planes, in_planes, kernel_size=kernel_size,
                                  stride=stride, groups=in_planes, padding=padding)
        self.pw_conv = nn.Conv2d(in_planes, out_planes, kernel_size=1)

    def forward(self, x):
        return self.pw_conv(self.ds_conv(x))


class ResNet(nn.Module):
    def __init__(self, backbone='resnet50', backbone_path=None):
        super().__init__()
        if backbone == 'resnet18':
            backbone = resnet18(pretrained=not backbone_path)
            self.out_channels = [256, 512, 512, 256, 256, 128]
        elif backbone == 'resnet34':
            backbone = resnet34(pretrained=not backbone_path)
            self.out_channels = [256, 512, 512, 256, 256, 256]
        elif backbone == 'resnet50':
            backbone = resnet50(pretrained=not backbone_path)
            self.out_channels = [1024, 512, 512, 256, 256, 256]
        elif backbone == 'resnet101':
            backbone = resnet101(pretrained=not backbone_path)
            self.out_channels = [1024, 512, 512, 256, 256, 256]
        else:  # backbone == 'resnet152':
            backbone = resnet152(pretrained=not backbone_path)
            self.out_channels = [1024, 512, 512, 256, 256, 256]
        if backbone_path:
            backbone.load_state_dict(torch.load(backbone_path))

        self.feature_extractor = nn.Sequential(*list(backbone.children())[:7])

        conv4_block1 = self.feature_extractor[-1][0]

        conv4_block1.conv1.stride = (1, 1)
        conv4_block1.conv2.stride = (1, 1)
        conv4_block1.downsample[0].stride = (1, 1)

    def forward(self, x):
        x = self.feature_extractor(x)
        return x


class SSD300(nn.Module):
    def __init__(self, backbone=ResNet('resnet50'), n_classes=81):
        super().__init__()

        # self.feature_extractor = backbone
        # self.out_channels = [1024, 512, 512, 256, 256, 256]

        self.feature_extractor = mobilenet_v2(pretrained=True, width_mult=1)
        self.out_channels = [576, 1280, 512, 256, 256, 128]

        self.label_num = n_classes  # number of COCO classes
        self._build_additional_features(self.out_channels[1:-1])
        self.num_defaults = [4, 6, 6, 6, 6, 6]
        self.loc = []
        self.conf = []

        for nd, oc in zip(self.num_defaults, self.out_channels):
            self.loc.append(DepthWiseConv_noBN(oc, nd * 4, kernel_size=3, padding=1))
            self.conf.append(DepthWiseConv_noBN(oc, nd * self.label_num, kernel_size=3, padding=1))

        self.loc = nn.ModuleList(self.loc)
        self.conf = nn.ModuleList(self.conf)
        self._init_weights()

    def _build_additional_features(self, input_size):
        self.additional_blocks = []
        for i, (input_size, output_size, channels) in enumerate(zip(input_size[:-1], input_size[1:], [256, 256, 128, 128, 128])):
            if i < 3:
                layer = nn.Sequential(
                    # nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
                    # nn.BatchNorm2d(channels),
                    # nn.ReLU(inplace=True),
                    DepthWiseConv(input_size, output_size, kernel_size=3,
                                  padding=1, stride=2),
                    # nn.BatchNorm2d(output_size),
                    # nn.ReLU(inplace=True),
                )
            else:
                layer = nn.Sequential(
                    # nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
                    # nn.BatchNorm2d(channels),
                    # nn.ReLU(inplace=True),
                    DepthWiseConv(input_size, output_size, kernel_size=3),
                    # nn.BatchNorm2d(output_size),
                    # nn.ReLU(inplace=True),
                )

            self.additional_blocks.append(layer)

        self.additional_blocks.append(DepthWiseConv(256, 128, kernel_size=2))

        self.additional_blocks = nn.ModuleList(self.additional_blocks)

    def _init_weights(self):
        layers = [*self.additional_blocks, *self.loc, *self.conf]
        for layer in layers:
            for param in layer.parameters():
                if param.dim() > 1:
                    nn.init.xavier_uniform_(param)

    # Shape the classifier to the view of bboxes
    def bbox_view(self, src, loc, conf):
        ret = []
        for s, l, c in zip(src, loc, conf):
            ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))

        locs, confs = list(zip(*ret))
        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
        return locs, confs

    def forward(self, x):
        if self.out_channels[0] == 576:
            inter_layer, x = self.feature_extractor(x)

            detection_feed = [inter_layer, x]
        else:
            x = self.feature_extractor(x)
            detection_feed = [x]

        for l in self.additional_blocks:
            x = l(x)
            detection_feed.append(x)

        # Feature Map 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4
        locs, confs = self.bbox_view(detection_feed, self.loc, self.conf)

        # For SSD 300, shall return nbatch x 8732 x {nlabels, nlocs} results
        return locs, confs