export onnx

[yhwang-hub]

请问具体是修改yolo.py中的哪个forward？我导出的onnx每个输出头仍然会存在reeshape和transpose节点

yolo.py:

import argparse import logging import sys from copy import deepcopy from pathlib import Path

sys.path.append(Path(file).parent.parent.absolute().str()) # 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

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=(), nkpt=None, ch=(), inplace=True, dw_conv_kpt=False):  # detection layer
    super(Detect, 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
    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.m[i](x[i])
        else :
            x[i] = torch.cat((self.m[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[..., 0::3].tanh() * 2.) ** 3 * self.anchor_grid[i][:,0].repeat(self.nkpt,1).permute(1,0).view(1, self.na, 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][:,0].repeat(self.nkpt,1).permute(1,0).view(1, self.na, 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 IDetect(nn.Module): stride = None # strides computed during build export = False # onnx export

def __init__(self, nc=80, anchors=(), nkpt=None, ch=(), inplace=True, dw_conv_kpt=False):  # detection layer
    super(IDetect, 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 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):
# def forward_keypoint(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 Model(nn.Module): def init(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).init() 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.safe_load(f) # 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
    self.inplace = self.yaml.get('inplace', True)
    # logger.info([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) or isinstance(m, IDetect) or isinstance(m, IKeypoint):
        s = 256  # 2x min stride
        m.inplace = self.inplace
        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
        # logger.info('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:
        return self.forward_augment(x)  # augmented inference, None
    else:
        return self.forward_once(x, profile)  # single-scale inference, train

def forward_augment(self, x):
    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 = self._descale_pred(yi, fi, si, img_size)
        y.append(yi)
    return torch.cat(y, 1), None  # augmented 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 isinstance(m, nn.Upsample):
            m.recompute_scale_factor = False

        if profile:
            o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
            t = time_synchronized()
            for _ in range(10):
                _ = m(x)
            dt.append((time_synchronized() - t) * 100)
            if m == self.model[0]:
                logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s}  {'module'}")
            logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')

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

    if profile:
        logger.info('%.1fms total' % sum(dt))
    return x

def _descale_pred(self, p, flips, scale, img_size):
    # de-scale predictions following augmented inference (inverse operation)
    if self.inplace:
        p[..., :4] /= scale  # de-scale
        if flips == 2:
            p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
        elif flips == 3:
            p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
    else:
        x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
        if flips == 2:
            y = img_size[0] - y  # de-flip ud
        elif flips == 3:
            x = img_size[1] - x  # de-flip lr
        p = torch.cat((x, y, wh, p[..., 4:]), -1)
    return p

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 _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)
        logger.info(
            ('%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:
#             logger.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
    logger.info('Fusing layers... ')
    for m in self.model.modules():
        if 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:
        logger.info('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:
        logger.info('Removing NMS... ')
        self.model = self.model[:-1]  # remove
    return self

def autoshape(self):  # add autoShape module
    logger.info('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, nkpt, gd, gw = d['anchors'], d['nc'], d['nkpt'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na (nc + 5 + 2nkpt) # 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
    args_dict = {}
    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 [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, ConvFocus, CrossConv, BottleneckCSP,
             C3, C3TR, BottleneckCSPF, BottleneckCSP2, SPPCSP, SPPCSPC]:
        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 [BottleneckCSP, C3, C3TR, BottleneckCSPF, BottleneckCSP2, SPPCSP, SPPCSPC]:
            args.insert(2, n)  # number of repeats
            n = 1
        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, DWConv, MixConv2d, Focus, ConvFocus, CrossConv, BottleneckCSP, C3, C3TR]:
            if 'act' in d.keys():
                args_dict = {"act" : d['act']}
    elif m is nn.BatchNorm2d:
        args = [ch[f]]
    elif m is Concat:
        c2 = sum([ch[x] for x in f])
    elif m in [Detect, IDetect, 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)
        if 'dw_conv_kpt' in d.keys():
            args_dict = {"dw_conv_kpt" : d['dw_conv_kpt']}
    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, **args_dict) for _ in range(n)]) if n > 1 else m(*args, **args_dict)  # 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='yolov5s.yaml', help='model.yaml') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 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()

# Profile
# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 320, 320).to(device)
# y = model(img, profile=True)

# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
# from torch.utils.tensorboard import SummaryWriter
# tb_writer = SummaryWriter('.')
# logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph
# tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard

[nanmi]

you should modify is the forward function in the keypoint-related code class IKeypoint(nn.Module)