Delete models/yolo.py

models/yolo.py

@@ -1,552 +0,0 @@
-
-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. - 0.5 + 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. - 0.5 + 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. - 0.5 + 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())
-
-        # 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)
-
-    # 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
-