import argparse
import logging
import sys
from copy import deepcopy

sys.path.append("./")  # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)

from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import make_divisible, check_file, set_logging
from utils.torch_utils import (
    time_synchronized,
    fuse_conv_and_bn,
    model_info,
    scale_img,
    initialize_weights,
    select_device,
    copy_attr,
)
from utils.loss import SigmoidBin

try:
    import thop  # for FLOPS computation
except ImportError:
    thop = None


class Detect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer("anchors", a)  # shape(nl,na,2)
        self.register_buffer(
            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
        )  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(
            nn.Conv2d(x, self.no * self.na, 1) for x in ch
        )  # output conv

    def forward(self, x):
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = (
                x[i]
                .view(bs, self.na, self.no, ny, nx)
                .permute(0, 1, 3, 4, 2)
                .contiguous()
            )

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
                    i
                ]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class IDetect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(IDetect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer("anchors", a)  # shape(nl,na,2)
        self.register_buffer(
            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
        )  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(
            nn.Conv2d(x, self.no * self.na, 1) for x in ch
        )  # output conv

        self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)

    def forward(self, x):
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](self.ia[i](x[i]))  # conv
            x[i] = self.im[i](x[i])
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = (
                x[i]
                .view(bs, self.na, self.no, ny, nx)
                .permute(0, 1, 3, 4, 2)
                .contiguous()
            )

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
                    i
                ]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class IAuxDetect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(IAuxDetect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer("anchors", a)  # shape(nl,na,2)
        self.register_buffer(
            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
        )  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(
            nn.Conv2d(x, self.no * self.na, 1) for x in ch[: self.nl]
        )  # output conv
        self.m2 = nn.ModuleList(
            nn.Conv2d(x, self.no * self.na, 1) for x in ch[self.nl :]
        )  # output conv

        self.ia = nn.ModuleList(ImplicitA(x) for x in ch[: self.nl])
        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch[: self.nl])

    def forward(self, x):
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](self.ia[i](x[i]))  # conv
            x[i] = self.im[i](x[i])
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = (
                x[i]
                .view(bs, self.na, self.no, ny, nx)
                .permute(0, 1, 3, 4, 2)
                .contiguous()
            )

            x[i + self.nl] = self.m2[i](x[i + self.nl])
            x[i + self.nl] = (
                x[i + self.nl]
                .view(bs, self.na, self.no, ny, nx)
                .permute(0, 1, 3, 4, 2)
                .contiguous()
            )

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
                    i
                ]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x[: self.nl])

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class IBin(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=(), bin_count=21):  # detection layer
        super(IBin, self).__init__()
        self.nc = nc  # number of classes
        self.bin_count = bin_count

        self.w_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
        self.h_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
        # classes, x,y,obj
        self.no = (
            nc + 3 + self.w_bin_sigmoid.get_length() + self.h_bin_sigmoid.get_length()
        )  # w-bce, h-bce
        # + self.x_bin_sigmoid.get_length() + self.y_bin_sigmoid.get_length()

        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer("anchors", a)  # shape(nl,na,2)
        self.register_buffer(
            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
        )  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(
            nn.Conv2d(x, self.no * self.na, 1) for x in ch
        )  # output conv

        self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)

    def forward(self, x):

        # self.x_bin_sigmoid.use_fw_regression = True
        # self.y_bin_sigmoid.use_fw_regression = True
        self.w_bin_sigmoid.use_fw_regression = True
        self.h_bin_sigmoid.use_fw_regression = True

        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](self.ia[i](x[i]))  # conv
            x[i] = self.im[i](x[i])
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = (
                x[i]
                .view(bs, self.na, self.no, ny, nx)
                .permute(0, 1, 3, 4, 2)
                .contiguous()
            )

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
                    i
                ]  # xy
                # y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                # px = (self.x_bin_sigmoid.forward(y[..., 0:12]) + self.grid[i][..., 0]) * self.stride[i]
                # py = (self.y_bin_sigmoid.forward(y[..., 12:24]) + self.grid[i][..., 1]) * self.stride[i]

                pw = (
                    self.w_bin_sigmoid.forward(y[..., 2:24])
                    * self.anchor_grid[i][..., 0]
                )
                ph = (
                    self.h_bin_sigmoid.forward(y[..., 24:46])
                    * self.anchor_grid[i][..., 1]
                )

                # y[..., 0] = px
                # y[..., 1] = py
                y[..., 2] = pw
                y[..., 3] = ph

                y = torch.cat((y[..., 0:4], y[..., 46:]), dim=-1)

                z.append(y.view(bs, -1, y.shape[-1]))

        return x if self.training else (torch.cat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class Model(nn.Module):
    def __init__(
        self, cfg="yolor-csp-c.yaml", ch=3, nc=None, anchors=None
    ):  # model, input channels, number of classes
        super(Model, self).__init__()
        self.traced = False
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub

            self.yaml_file = Path(cfg).name
            with open(cfg) as f:
                self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict

        # Define model
        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override yaml value
        if anchors:
            logger.info(f"Overriding model.yaml anchors with anchors={anchors}")
            self.yaml["anchors"] = round(anchors)  # override yaml value
        self.model, self.save = parse_model(
            deepcopy(self.yaml), ch=[ch]
        )  # model, savelist
        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            s = 256  # 2x min stride
            m.stride = torch.tensor(
                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
            )  # forward
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()  # only run once
            # print('Strides: %s' % m.stride.tolist())
        if isinstance(m, IDetect):
            s = 256  # 2x min stride
            m.stride = torch.tensor(
                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
            )  # forward
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()  # only run once
            # print('Strides: %s' % m.stride.tolist())
        if isinstance(m, IAuxDetect):
            s = 256  # 2x min stride
            m.stride = torch.tensor(
                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]]
            )  # forward
            # print(m.stride)
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_aux_biases()  # only run once
            # print('Strides: %s' % m.stride.tolist())
        if isinstance(m, IBin):
            s = 256  # 2x min stride
            m.stride = torch.tensor(
                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
            )  # forward
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases_bin()  # only run once
            # print('Strides: %s' % m.stride.tolist())

        # Init weights, biases
        initialize_weights(self)
        self.info()
        logger.info("")

    def forward(self, x, augment=False, profile=False):
        if augment:
            img_size = x.shape[-2:]  # height, width
            s = [1, 0.83, 0.67]  # scales
            f = [None, 3, None]  # flips (2-ud, 3-lr)
            y = []  # outputs
            for si, fi in zip(s, f):
                xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
                yi = self.forward_once(xi)[0]  # forward
                # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                yi[..., :4] /= si  # de-scale
                if fi == 2:
                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
                elif fi == 3:
                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
                y.append(yi)
            return torch.cat(y, 1), None  # augmented inference, train
        else:
            return self.forward_once(x, profile)  # single-scale inference, train

    def forward_once(self, x, profile=False):
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = (
                    y[m.f]
                    if isinstance(m.f, int)
                    else [x if j == -1 else y[j] for j in m.f]
                )  # from earlier layers

            if not hasattr(self, "traced"):
                self.traced = False

            if self.traced:
                if (
                    isinstance(m, Detect)
                    or isinstance(m, IDetect)
                    or isinstance(m, IAuxDetect)
                ):
                    break

            if profile:
                c = isinstance(m, (Detect, IDetect, IAuxDetect, IBin))
                o = (
                    thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0]
                    / 1e9
                    * 2
                    if thop
                    else 0
                )  # FLOPS
                for _ in range(10):
                    m(x.copy() if c else x)
                t = time_synchronized()
                for _ in range(10):
                    m(x.copy() if c else x)
                dt.append((time_synchronized() - t) * 100)
                print("%10.1f%10.0f%10.1fms %-40s" % (o, m.np, dt[-1], m.type))

            x = m(x)  # run

            y.append(x if m.i in self.save else None)  # save output

        if profile:
            print("%.1fms total" % sum(dt))
        return x

    def _initialize_biases(
        self, cf=None
    ):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(
                8 / (640 / s) ** 2
            )  # obj (8 objects per 640 image)
            b.data[:, 5:] += (
                math.log(0.6 / (m.nc - 0.99))
                if cf is None
                else torch.log(cf / cf.sum())
            )  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _initialize_aux_biases(
        self, cf=None
    ):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, mi2, s in zip(m.m, m.m2, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(
                8 / (640 / s) ** 2
            )  # obj (8 objects per 640 image)
            b.data[:, 5:] += (
                math.log(0.6 / (m.nc - 0.99))
                if cf is None
                else torch.log(cf / cf.sum())
            )  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
            b2 = mi2.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b2.data[:, 4] += math.log(
                8 / (640 / s) ** 2
            )  # obj (8 objects per 640 image)
            b2.data[:, 5:] += (
                math.log(0.6 / (m.nc - 0.99))
                if cf is None
                else torch.log(cf / cf.sum())
            )  # cls
            mi2.bias = torch.nn.Parameter(b2.view(-1), requires_grad=True)

    def _initialize_biases_bin(
        self, cf=None
    ):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Bin() module
        bc = m.bin_count
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            old = b[:, (0, 1, 2, bc + 3)].data
            obj_idx = 2 * bc + 4
            b[:, :obj_idx].data += math.log(0.6 / (bc + 1 - 0.99))
            b[:, obj_idx].data += math.log(
                8 / (640 / s) ** 2
            )  # obj (8 objects per 640 image)
            b[:, (obj_idx + 1) :].data += (
                math.log(0.6 / (m.nc - 0.99))
                if cf is None
                else torch.log(cf / cf.sum())
            )  # cls
            b[:, (0, 1, 2, bc + 3)].data = old
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            print(
                ("%6g Conv2d.bias:" + "%10.3g" * 6)
                % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())
            )

    # def _print_weights(self):
    #     for m in self.model.modules():
    #         if type(m) is Bottleneck:
    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        print("Fusing layers... ")
        for m in self.model.modules():
            if isinstance(m, RepConv):
                # print(f" fuse_repvgg_block")
                m.fuse_repvgg_block()
            elif isinstance(m, RepConv_OREPA):
                # print(f" switch_to_deploy")
                m.switch_to_deploy()
            elif type(m) is Conv and hasattr(m, "bn"):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.fuseforward  # update forward
        self.info()
        return self

    def nms(self, mode=True):  # add or remove NMS module
        present = type(self.model[-1]) is NMS  # last layer is NMS
        if mode and not present:
            print("Adding NMS... ")
            m = NMS()  # module
            m.f = -1  # from
            m.i = self.model[-1].i + 1  # index
            self.model.add_module(name="%s" % m.i, module=m)  # add
            self.eval()
        elif not mode and present:
            print("Removing NMS... ")
            self.model = self.model[:-1]  # remove
        return self

    def autoshape(self):  # add autoShape module
        print("Adding autoShape... ")
        m = autoShape(self)  # wrap model
        copy_attr(
            m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=()
        )  # copy attributes
        return m

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)


def parse_model(d, ch):  # model_dict, input_channels(3)
    logger.info(
        "\n%3s%18s%3s%10s  %-40s%-30s"
        % ("", "from", "n", "params", "module", "arguments")
    )
    anchors, nc, gd, gw = (
        d["anchors"],
        d["nc"],
        d["depth_multiple"],
        d["width_multiple"],
    )
    na = (
        (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors
    )  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(
        d["backbone"] + d["head"]
    ):  # from, number, module, args
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except:
                pass

        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in [
            nn.Conv2d,
            Conv,
            RobustConv,
            RobustConv2,
            DWConv,
            GhostConv,
            RepConv,
            RepConv_OREPA,
            DownC,
            SPP,
            SPPF,
            SPPCSPC,
            GhostSPPCSPC,
            MixConv2d,
            Focus,
            Stem,
            GhostStem,
            CrossConv,
            Bottleneck,
            BottleneckCSPA,
            BottleneckCSPB,
            BottleneckCSPC,
            RepBottleneck,
            RepBottleneckCSPA,
            RepBottleneckCSPB,
            RepBottleneckCSPC,
            Res,
            ResCSPA,
            ResCSPB,
            ResCSPC,
            RepRes,
            RepResCSPA,
            RepResCSPB,
            RepResCSPC,
            ResX,
            ResXCSPA,
            ResXCSPB,
            ResXCSPC,
            RepResX,
            RepResXCSPA,
            RepResXCSPB,
            RepResXCSPC,
            Ghost,
            GhostCSPA,
            GhostCSPB,
            GhostCSPC,
            SwinTransformerBlock,
            STCSPA,
            STCSPB,
            STCSPC,
            SwinTransformer2Block,
            ST2CSPA,
            ST2CSPB,
            ST2CSPC,
        ]:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in [
                DownC,
                SPPCSPC,
                GhostSPPCSPC,
                BottleneckCSPA,
                BottleneckCSPB,
                BottleneckCSPC,
                RepBottleneckCSPA,
                RepBottleneckCSPB,
                RepBottleneckCSPC,
                ResCSPA,
                ResCSPB,
                ResCSPC,
                RepResCSPA,
                RepResCSPB,
                RepResCSPC,
                ResXCSPA,
                ResXCSPB,
                ResXCSPC,
                RepResXCSPA,
                RepResXCSPB,
                RepResXCSPC,
                GhostCSPA,
                GhostCSPB,
                GhostCSPC,
                STCSPA,
                STCSPB,
                STCSPC,
                ST2CSPA,
                ST2CSPB,
                ST2CSPC,
            ]:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum([ch[x] for x in f])
        elif m is Chuncat:
            c2 = sum([ch[x] for x in f])
        elif m is Shortcut:
            c2 = ch[f[0]]
        elif m is Foldcut:
            c2 = ch[f] // 2
        elif m in [Detect, IDetect, IAuxDetect, IBin]:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is ReOrg:
            c2 = ch[f] * 4
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            c2 = ch[f]

        m_ = (
            nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)
        )  # module
        t = str(m)[8:-2].replace("__main__.", "")  # module type
        np = sum([x.numel() for x in m_.parameters()])  # number params
        m_.i, m_.f, m_.type, m_.np = (
            i,
            f,
            t,
            np,
        )  # attach index, 'from' index, type, number params
        logger.info("%3s%18s%3s%10.0f  %-40s%-30s" % (i, f, n, np, t, args))  # print
        save.extend(
            x % i for x in ([f] if isinstance(f, int) else f) if x != -1
        )  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--cfg", type=str, default="yolor-csp-c.yaml", help="model.yaml"
    )
    parser.add_argument(
        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
    )
    parser.add_argument("--profile", action="store_true", help="profile model speed")
    opt = parser.parse_args()
    opt.cfg = check_file(opt.cfg)  # check file
    set_logging()
    device = select_device(opt.device)

    # Create model
    model = Model(opt.cfg).to(device)
    model.train()

    if opt.profile:
        img = torch.rand(1, 3, 640, 640).to(device)
        y = model(img, profile=True)