# --------------------------------------------------------
# RepVGG: Making VGG-style ConvNets Great Again (https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.pdf)
# Github source: https://github.com/DingXiaoH/RepVGG
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch.nn as nn
import numpy as np
import torch
import copy
from .se_block import SEBlock
import torch.utils.checkpoint as checkpoint

# --added
from typing import Any, Callable, Dict, List, Optional, Type, Union


def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
    result = nn.Sequential()
    result.add_module(
        "conv",
        nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            bias=False,
        ),
    )
    result.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
    return result


class RepVGGBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        padding_mode="zeros",
        deploy=False,
        use_se=False,
    ):
        super(RepVGGBlock, self).__init__()
        self.deploy = deploy
        self.groups = groups
        self.in_channels = in_channels

        assert kernel_size == 3
        assert padding == 1

        padding_11 = padding - kernel_size // 2

        self.nonlinearity = nn.ReLU()

        if use_se:
            #   Note that RepVGG-D2se uses SE before nonlinearity. But RepVGGplus models uses SE after nonlinearity.
            self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
        else:
            self.se = nn.Identity()

        if deploy:
            self.rbr_reparam = nn.Conv2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                groups=groups,
                bias=True,
                padding_mode=padding_mode,
            )

        else:
            self.rbr_identity = (
                nn.BatchNorm2d(num_features=in_channels)
                if out_channels == in_channels and stride == 1
                else None
            )
            self.rbr_dense = conv_bn(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                groups=groups,
            )
            self.rbr_1x1 = conv_bn(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1,
                stride=stride,
                padding=padding_11,
                groups=groups,
            )
            print("RepVGG Block, identity = ", self.rbr_identity)

    def forward(self, inputs):
        if hasattr(self, "rbr_reparam"):
            return self.nonlinearity(self.se(self.rbr_reparam(inputs)))

        if self.rbr_identity is None:
            id_out = 0
        else:
            id_out = self.rbr_identity(inputs)

        return self.nonlinearity(
            self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
        )

    #   Optional. This may improve the accuracy and facilitates quantization in some cases.
    #   1.  Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
    #   2.  Use like this.
    #       loss = criterion(....)
    #       for every RepVGGBlock blk:
    #           loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
    #       optimizer.zero_grad()
    #       loss.backward()
    def get_custom_L2(self):
        K3 = self.rbr_dense.conv.weight
        K1 = self.rbr_1x1.conv.weight
        t3 = (
            (
                self.rbr_dense.bn.weight
                / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())
            )
            .reshape(-1, 1, 1, 1)
            .detach()
        )
        t1 = (
            (
                self.rbr_1x1.bn.weight
                / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())
            )
            .reshape(-1, 1, 1, 1)
            .detach()
        )

        l2_loss_circle = (K3**2).sum() - (
            K3[:, :, 1:2, 1:2] ** 2
        ).sum()  # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
        eq_kernel = (
            K3[:, :, 1:2, 1:2] * t3 + K1 * t1
        )  # The equivalent resultant central point of 3x3 kernel.
        l2_loss_eq_kernel = (
            eq_kernel**2 / (t3**2 + t1**2)
        ).sum()  # Normalize for an L2 coefficient comparable to regular L2.
        return l2_loss_eq_kernel + l2_loss_circle

    #   This func derives the equivalent kernel and bias in a DIFFERENTIABLE way.
    #   You can get the equivalent kernel and bias at any time and do whatever you want,
    #   for example, apply some penalties or constraints during training, just like you do to the other models.
    #   May be useful for quantization or pruning.
    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
        return (
            kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid,
            bias3x3 + bias1x1 + biasid,
        )

    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])

    def _fuse_bn_tensor(self, branch):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, "id_tensor"):
                input_dim = self.in_channels // self.groups
                kernel_value = np.zeros(
                    (self.in_channels, input_dim, 3, 3), dtype=np.float32
                )
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, 1, 1] = 1
                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std

    def switch_to_deploy(self):
        if hasattr(self, "rbr_reparam"):
            return
        kernel, bias = self.get_equivalent_kernel_bias()
        self.rbr_reparam = nn.Conv2d(
            in_channels=self.rbr_dense.conv.in_channels,
            out_channels=self.rbr_dense.conv.out_channels,
            kernel_size=self.rbr_dense.conv.kernel_size,
            stride=self.rbr_dense.conv.stride,
            padding=self.rbr_dense.conv.padding,
            dilation=self.rbr_dense.conv.dilation,
            groups=self.rbr_dense.conv.groups,
            bias=True,
        )
        self.rbr_reparam.weight.data = kernel
        self.rbr_reparam.bias.data = bias
        self.__delattr__("rbr_dense")
        self.__delattr__("rbr_1x1")
        if hasattr(self, "rbr_identity"):
            self.__delattr__("rbr_identity")
        if hasattr(self, "id_tensor"):
            self.__delattr__("id_tensor")
        self.deploy = True


class RepVGG(nn.Module):
    def __init__(
        self,
        num_blocks,
        num_classes=1000,
        width_multiplier=None,
        override_groups_map=None,
        deploy=False,
        use_se=False,
        use_act_checkpoint=False,
    ):
        super(RepVGG, self).__init__()
        assert len(width_multiplier) == 4
        self.deploy = deploy
        self.override_groups_map = override_groups_map or dict()
        assert 0 not in self.override_groups_map
        self.use_se = use_se
        self.use_act_checkpoint = use_act_checkpoint

        self.in_planes = min(64, int(64 * width_multiplier[0]))
        self.stage0 = RepVGGBlock(
            in_channels=3,
            out_channels=self.in_planes,
            kernel_size=3,
            stride=2,
            padding=1,
            deploy=self.deploy,
            use_se=self.use_se,
        )
        self.cur_layer_idx = 1
        self.stage1 = self._make_stage(
            int(64 * width_multiplier[0]), num_blocks[0], stride=2
        )
        self.stage2 = self._make_stage(
            int(128 * width_multiplier[1]), num_blocks[1], stride=2
        )
        self.stage3 = self._make_stage(
            int(256 * width_multiplier[2]), num_blocks[2], stride=2
        )
        self.stage4 = self._make_stage(
            int(512 * width_multiplier[3]), num_blocks[3], stride=2
        )
        self.gap = nn.AdaptiveAvgPool2d(output_size=1)
        self.linear = nn.Linear(int(512 * width_multiplier[3]), num_classes)

    def _make_stage(self, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        blocks = []
        for stride in strides:
            cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1)
            blocks.append(
                RepVGGBlock(
                    in_channels=self.in_planes,
                    out_channels=planes,
                    kernel_size=3,
                    stride=stride,
                    padding=1,
                    groups=cur_groups,
                    deploy=self.deploy,
                    use_se=self.use_se,
                )
            )
            self.in_planes = planes
            self.cur_layer_idx += 1
        return nn.ModuleList(blocks)

    def forward(self, x):
        out = self.stage0(x)
        for stage in (self.stage1, self.stage2, self.stage3, self.stage4):
            for block in stage:
                if self.use_act_checkpoint:
                    out = checkpoint.checkpoint(block, out)
                else:
                    out = block(out)
        out = self.gap(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
g2_map = {l: 2 for l in optional_groupwise_layers}
g4_map = {l: 4 for l in optional_groupwise_layers}


# ---------------------------------------------------
# - use_act_checkpoint
# use_act_checkpoint variable controls whether the model applies checkpointing to the
# computations within each block of each stage. This help reduce memory usage at the
# cost of longer computation times due to recomputation during backward pass.

# - deploy
# deploy variable controls whether RegVGG model needs to be transformed into deploy
# format.

# - pretrained
# pretrained RepVGG model is not supported at this stage, so it's set to be False
# ---------------------------------------------------


def get_repvgg_a0(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes

    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[2, 4, 14, 1],
        num_classes=num_classes,
        width_multiplier=[0.75, 0.75, 0.75, 2.5],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_a1(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes

    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[2, 4, 14, 1],
        num_classes=num_classes,
        width_multiplier=[1, 1, 1, 2.5],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_a2(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes

    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[2, 4, 14, 1],
        num_classes=num_classes,
        width_multiplier=[1.5, 1.5, 1.5, 2.75],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b0(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[1, 1, 1, 2.5],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b1(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2, 2, 2, 4],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b1g2(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2, 2, 2, 4],
        override_groups_map=g2_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b1g4(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2, 2, 2, 4],
        override_groups_map=g4_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b2(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2.5, 2.5, 2.5, 5],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b2g2(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2.5, 2.5, 2.5, 5],
        override_groups_map=g2_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b2g4(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[2.5, 2.5, 2.5, 5],
        override_groups_map=g4_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b3(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[3, 3, 3, 5],
        override_groups_map=None,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b3g2(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[3, 3, 3, 5],
        override_groups_map=g2_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_b3g4(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[4, 6, 16, 1],
        num_classes=num_classes,
        width_multiplier=[3, 3, 3, 5],
        override_groups_map=g4_map,
        deploy=deploy,
        use_act_checkpoint=use_act_checkpoint,
    )


def get_repvgg_d2se(
    info: Dict,
    pretrained: bool = False,
    deploy: bool = False,
    use_act_checkpoint: bool = False,
    **kwargs: Any,
) -> RepVGG:
    num_classes = info.num_classes
    if pretrained:
        raise ValueError("Pretrained is currently not supported for RepVGG")

    return RepVGG(
        num_blocks=[8, 14, 24, 1],
        num_classes=num_classes,
        width_multiplier=[2.5, 2.5, 2.5, 5],
        override_groups_map=None,
        deploy=deploy,
        use_se=True,
        use_act_checkpoint=use_act_checkpoint,
    )


# def create_RepVGG_A0(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
#                   width_multiplier=[0.75, 0.75, 0.75, 2.5], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_A1(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
#                   width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_A2(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
#                   width_multiplier=[1.5, 1.5, 1.5, 2.75], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B0(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B1(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2, 2, 2, 4], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B1g2(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2, 2, 2, 4], override_groups_map=g2_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B1g4(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2, 2, 2, 4], override_groups_map=g4_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)


# def create_RepVGG_B2(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B2g2(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g2_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B2g4(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)


# def create_RepVGG_B3(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[3, 3, 3, 5], override_groups_map=None, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B3g2(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[3, 3, 3, 5], override_groups_map=g2_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_B3g4(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
#                   width_multiplier=[3, 3, 3, 5], override_groups_map=g4_map, deploy=deploy, use_act_checkpoint=use_act_checkpoint)

# def create_RepVGG_D2se(deploy=False, use_act_checkpoint=False):
#     return RepVGG(num_blocks=[8, 14, 24, 1], num_classes=1000,
#                   width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None, deploy=deploy, use_se=True, use_act_checkpoint=use_act_checkpoint)


func_dict = {
    "RepVGG-A0": get_repvgg_a0,
    "RepVGG-A1": get_repvgg_a1,
    "RepVGG-A2": get_repvgg_a2,
    "RepVGG-B0": get_repvgg_b0,
    "RepVGG-B1": get_repvgg_b1,
    "RepVGG-B1g2": get_repvgg_b1g2,
    "RepVGG-B1g4": get_repvgg_b1g4,
    "RepVGG-B2": get_repvgg_b2,
    "RepVGG-B2g2": get_repvgg_b2g2,
    "RepVGG-B2g4": get_repvgg_b2g4,
    "RepVGG-B3": get_repvgg_b3,
    "RepVGG-B3g2": get_repvgg_b3g2,
    "RepVGG-B3g4": get_repvgg_b3g4,
    "RepVGG-D2se": get_repvgg_d2se,  #   Updated at April 25, 2021. This is not reported in the CVPR paper.
}


def get_RepVGG_func_by_name(name):
    return func_dict[name]


#   Use this for converting a RepVGG model or a bigger model with RepVGG as its component
#   Use like this
#   model = create_RepVGG_A0(deploy=False)
#   train model or load weights
#   repvgg_model_convert(model, save_path='repvgg_deploy.pth')
#   If you want to preserve the original model, call with do_copy=True

#   ====================== for using RepVGG as the backbone of a bigger model, e.g., PSPNet, the pseudo code will be like
#   train_backbone = create_RepVGG_B2(deploy=False)
#   train_backbone.load_state_dict(torch.load('RepVGG-B2-train.pth'))
#   train_pspnet = build_pspnet(backbone=train_backbone)
#   segmentation_train(train_pspnet)
#   deploy_pspnet = repvgg_model_convert(train_pspnet)
#   segmentation_test(deploy_pspnet)
#   =====================   example_pspnet.py shows an example


def repvgg_model_convert(model: torch.nn.Module, save_path=None, do_copy=True):
    if do_copy:
        model = copy.deepcopy(model)
    for module in model.modules():
        if hasattr(module, "switch_to_deploy"):
            module.switch_to_deploy()
    if save_path is not None:
        torch.save(model.state_dict(), save_path)
    return model
