# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/facebookresearch/detr/blob/main/models/backbone.py
# ------------------------------------------------------------------------------------------------

from collections import OrderedDict

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

from detectron2.utils.comm import is_main_process


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):
        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))

    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 = 1e-5
        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, 
            num_channels: int, 
            return_layers: dict,
        ):
        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)

        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
        self.num_channels = num_channels

    def forward(self, x):
        xs = self.body(x)
        out = {}
        for name, x in xs.items():
            out[name] = x
        return out


class TorchvisionResNet(BackboneBase):
    """ResNet backbone with frozen BatchNorm."""
    def __init__(self, 
                 name: str,
                 train_backbone: bool,
                 return_layers: dict = {"layer4": "res5"},
                 dilation: bool = False,
                ):
        backbone = getattr(torchvision.models, name)(
            replace_stride_with_dilation=[False, False, dilation],
            pretrained=False, norm_layer=FrozenBatchNorm2d)
        num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
        super().__init__(backbone, train_backbone, num_channels, return_layers)