# ------------------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# ------------------------------------------------------------------------------------

"""
Backbone modules.
"""

from typing import Dict, List

import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter

from util.misc import NestedTensor

from .position_encoding import build_position_encoding


class FrozenBatchNorm2d(torch.nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
    without which any other models than torchvision.models.resnet[18,34,50,101]
    produce nans.
    """

    def __init__(self, n, eps=1e-5):
        super(FrozenBatchNorm2d, self).__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))
        self.eps = eps

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super(FrozenBatchNorm2d, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def forward(self, x):
        # move reshapes to the beginning
        # to make it fuser-friendly
        w = self.weight.reshape(1, -1, 1, 1)
        b = self.bias.reshape(1, -1, 1, 1)
        rv = self.running_var.reshape(1, -1, 1, 1)
        rm = self.running_mean.reshape(1, -1, 1, 1)
        eps = self.eps
        scale = w * (rv + eps).rsqrt()
        bias = b - rm * scale
        return x * scale + bias


class BackboneBase(nn.Module):
    def __init__(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool):
        super().__init__()
        for name, parameter in backbone.named_parameters():
            if not train_backbone or "layer2" not in name and "layer3" not in name and "layer4" not in name:
                parameter.requires_grad_(False)
        if return_interm_layers:
            return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
            self.strides = [8, 16, 32]
            self.num_channels = [512, 1024, 2048]
        else:
            return_layers = {"layer4": "0"}
            self.strides = [32]
            self.num_channels = [2048]
        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)

    def forward(self, tensor_list: NestedTensor):
        xs = self.body(tensor_list.tensors)
        out: Dict[str, NestedTensor] = {}
        for name, x in xs.items():
            m = tensor_list.mask
            assert m is not None
            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
            out[name] = NestedTensor(x, mask)
        return out


class Backbone(BackboneBase):
    """ResNet backbone with frozen BatchNorm."""

    def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool, input_channels=1):
        norm_layer = FrozenBatchNorm2d
        backbone = getattr(torchvision.models, name)(
            replace_stride_with_dilation=[False, False, dilation], pretrained=True, norm_layer=norm_layer
        )
        # modify the first layer to compatible with single channel input
        backbone.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
        assert name not in ("resnet18", "resnet34"), "number of channels are hard coded"
        super().__init__(backbone, train_backbone, return_interm_layers)
        if dilation:
            self.strides[-1] = self.strides[-1] // 2


class Joiner(nn.Sequential):
    def __init__(self, backbone, position_embedding):
        super().__init__(backbone, position_embedding)
        self.strides = backbone.strides
        self.num_channels = backbone.num_channels

    def forward(self, tensor_list: NestedTensor):
        xs = self[0](tensor_list)
        out: List[NestedTensor] = []
        pos = []
        for name, x in sorted(xs.items()):
            out.append(x)

        # position encoding
        for x in out:
            pos.append(self[1](x).to(x.tensors.dtype))

        return out, pos


def build_backbone(args):
    position_embedding = build_position_encoding(args)
    train_backbone = args.lr_backbone > 0
    return_interm_layers = args.num_feature_levels > 1
    backbone = Backbone(
        args.backbone, train_backbone, return_interm_layers, args.dilation, input_channels=args.input_channels
    )
    model = Joiner(backbone, position_embedding)
    return model