import argparse

import logging

import sys

from copy import deepcopy

sys.path.append('./')  # to run '$ python *.py' files in subdirectories

logger = logging.getLogger(__name__)

import torch

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

    end2end = False

    include_nms = False 

    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()

                if not torch.onnx.is_in_onnx_export():

                    y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy

                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                else:

                    xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy

                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                    y = torch.cat((xy, wh, y[..., 4:]), -1)

                z.append(y.view(bs, -1, self.no))

        if self.training:

            out = x

        elif self.end2end:

            out = torch.cat(z, 1)

        elif self.include_nms:

            z = self.convert(z)

            out = (z, )

        else:

            out = (torch.cat(z, 1), x)

        return out

    @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()

    def convert(self, z):

        z = torch.cat(z, 1)

        box = z[:, :, :4]

        conf = z[:, :, 4:5]

        score = z[:, :, 5:]

        score *= conf

        convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],

                                           dtype=torch.float32,

                                           device=z.device)

        box @= convert_matrix                          

        return (box, score)

class IDetect(nn.Module):

    stride = None  # strides computed during build

    export = False  # onnx export

    end2end = False

    include_nms = False 

    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. - 0.5 + 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)

    def fuseforward(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. - 0.5 + 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))

        if self.training:

            out = x

        elif self.end2end:

            out = torch.cat(z, 1)

        elif self.include_nms:

            z = self.convert(z)

            out = (z, )

        else:

            out = (torch.cat(z, 1), x)

        return out

    def fuse(self):

        print("IDetect.fuse")

        # fuse ImplicitA and Convolution

        for i in range(len(self.m)):

            c1,c2,_,_ = self.m[i].weight.shape

            c1_,c2_, _,_ = self.ia[i].implicit.shape

            self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1)

        # fuse ImplicitM and Convolution

        for i in range(len(self.m)):

            c1,c2, _,_ = self.im[i].implicit.shape

            self.m[i].bias *= self.im[i].implicit.reshape(c2)

            self.m[i].weight *= self.im[i].implicit.transpose(0,1)

    @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()

    def convert(self, z):

        z = torch.cat(z, 1)

        box = z[:, :, :4]

        conf = z[:, :, 4:5]

        score = z[:, :, 5:]

        score *= conf

        convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],

                                           dtype=torch.float32,

                                           device=z.device)

        box @= convert_matrix                          

        return (box, score)

class IKeypoint(nn.Module):

    stride = None  # strides computed during build

    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), nkpt=17, ch=(), inplace=True, dw_conv_kpt=False):  # detection layer

        super(IKeypoint, self).__init__()

        self.nc = nc  # number of classes

        self.nkpt = nkpt

        self.dw_conv_kpt = dw_conv_kpt

        self.no_det=(nc + 5)  # number of outputs per anchor for box and class

        self.no_kpt = 3*self.nkpt ## number of outputs per anchor for keypoints

        self.no = self.no_det+self.no_kpt

        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

        self.flip_test = False

        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_det * 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_det * self.na) for _ in ch)

        if self.nkpt is not None:

            if self.dw_conv_kpt: #keypoint head is slightly more complex

                self.m_kpt = nn.ModuleList(

                            nn.Sequential(DWConv(x, x, k=3), Conv(x,x),

                                          DWConv(x, x, k=3), Conv(x, x),

                                          DWConv(x, x, k=3), Conv(x,x),

                                          DWConv(x, x, k=3), Conv(x, x),

                                          DWConv(x, x, k=3), Conv(x, x),

                                          DWConv(x, x, k=3), nn.Conv2d(x, self.no_kpt * self.na, 1)) for x in ch)

            else: #keypoint head is a single convolution

                self.m_kpt = nn.ModuleList(nn.Conv2d(x, self.no_kpt * self.na, 1) for x in ch)

        self.inplace = inplace  # use in-place ops (e.g. slice assignment)

    def forward(self, x):

        # x = x.copy()  # for profiling

        z = []  # inference output

        self.training |= self.export

        for i in range(self.nl):

            if self.nkpt is None or self.nkpt==0:

                x[i] = self.im[i](self.m[i](self.ia[i](x[i])))  # conv

            else :

                x[i] = torch.cat((self.im[i](self.m[i](self.ia[i](x[i]))), self.m_kpt[i](x[i])), axis=1)

            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_det = x[i][..., :6]

            x_kpt = x[i][..., 6:]

            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)

                kpt_grid_x = self.grid[i][..., 0:1]

                kpt_grid_y = self.grid[i][..., 1:2]

                if self.nkpt == 0:

                    y = x[i].sigmoid()

                else:

                    y = x_det.sigmoid()

                if self.inplace:

                    xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy

                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh

                    if self.nkpt != 0:

                        x_kpt[..., 0::3] = (x_kpt[..., ::3] * 2. - 0.5 + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i]  # xy

                        x_kpt[..., 1::3] = (x_kpt[..., 1::3] * 2. - 0.5 + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i]  # xy

                        #x_kpt[..., 0::3] = (x_kpt[..., ::3] + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i]  # xy

                        #x_kpt[..., 1::3] = (x_kpt[..., 1::3] + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i]  # xy

                        #print('=============')

                        #print(self.anchor_grid[i].shape)

                        #print(self.anchor_grid[i][...,0].unsqueeze(4).shape)

                        #print(x_kpt[..., 0::3].shape)

                        #x_kpt[..., 0::3] = ((x_kpt[..., 0::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i]  # xy

                        #x_kpt[..., 1::3] = ((x_kpt[..., 1::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i]  # xy

                        #x_kpt[..., 0::3] = (((x_kpt[..., 0::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i]  # xy

                        #x_kpt[..., 1::3] = (((x_kpt[..., 1::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i]  # xy

                        x_kpt[..., 2::3] = x_kpt[..., 2::3].sigmoid()

                    y = torch.cat((xy, wh, y[..., 4:], x_kpt), dim = -1)

                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953

                    xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy

                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                    if self.nkpt != 0:

                        y[..., 6:] = (y[..., 6:] * 2. - 0.5 + self.grid[i].repeat((1,1,1,1,self.nkpt))) * self.stride[i]  # xy

                    y = torch.cat((xy, wh, y[..., 4:]), -1)

                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. - 0.5 + 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. - 0.5 + 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())

        if isinstance(m, IKeypoint):

            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_kpt()  # 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) or isinstance(m, IKeypoint):

                    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 _initialize_biases_kpt(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 _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

            elif isinstance(m, IDetect):

                m.fuse()

                m.forward = m.fuseforward

        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, IKeypoint]:

            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)

    # Profile

    # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)

    # y = model(img, profile=True)

    # Tensorboard

    # from torch.utils.tensorboard import SummaryWriter

    # tb_writer = SummaryWriter()

    # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")

    # tb_writer.add_graph(model.model, img)  # add model to tensorboard

    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard