import argparse
	import sys
	from copy import deepcopy
	import os
	import platform
	from pathlib import Path
	FILE = Path(__file__).resolve()
	ROOT = FILE.parents[1] # YOLOv5 root directory
	if str(ROOT) not in sys.path:
	sys.path.append(str(ROOT)) # add ROOT to PATH
	if platform.system() != 'Windows':
	ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
	from models.common import *
	from models.experimental import *
	from utils.autoanchor import check_anchor_order
	from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
	from utils.plots import feature_visualization
	from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync
	try:
	import thop # for FLOPs computation
	except ImportError:
	thop = None
	class Detect(nn.Module):
	stride = None # strides computed during build
	onnx_dynamic = False # ONNX export parameter
	def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
	super().__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
	self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
	self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
	self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
	self.inplace = inplace # use in-place ops (e.g. slice assignment)
	def forward(self, x):
	z = [] # inference output
	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] or self.onnx_dynamic:
	self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
	y = x[i].sigmoid()
	if self.inplace:
	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: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
	xy, wh, conf = y.tensor_split((2, 4), 4)
	xy = (xy * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
	wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
	y = torch.cat((xy, wh, conf), 4)
	z.append(y.view(bs, -1, self.no))
	return x if self.training else (torch.cat(z, 1), x)
	def _make_grid(self, nx=20, ny=20, i=0):
	d = self.anchors[i].device
	shape = 1, self.na, ny, nx, 2 # grid shape
	if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
	yv, xv = torch.meshgrid(torch.arange(ny).to(d), torch.arange(nx).to(d), indexing='ij')
	else:
	yv, xv = torch.meshgrid(torch.arange(ny, device=d), torch.arange(nx, device=d))
	grid = torch.stack((xv, yv), 2).expand(shape).float()
	anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape).float()
	return grid, anchor_grid
	class Decoupled_Detect(nn.Module):
	stride = None # strides computed during build
	onnx_dynamic = False # ONNX export parameter
	def __init__(self, nc=80, anchors=(), ch=(),inplace=True): # detection layer
	super().__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
	self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
	self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
	self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
	self.cls_c1=Conv(self.no * self.na , self.no * self.na , 3)
	self.cls_c2=Conv(self.no * self.na , self.no * self.na , 3)
	self.reg_c1=Conv(self.no * self.na , self.no * self.na , 3)
	self.reg_c2=Conv(self.no * self.na , self.no * self.na , 3)
	self.cls_head = nn.Conv2d(self.no * self.na,self.nc*self.na,1)
	self.reg_head = nn.Conv2d(self.no * self.na,4*self.na,1)
	self.obj_head = nn.Conv2d(self.no * self.na,self.na,1)
	self.inplace = inplace # use in-place ops (e.g. slice assignment)
	def forward(self, x):
	z = [] # inference output
	for i in range(self.nl):
	#print( 'check output:',x[i].size())
	x[i] = self.m[i](x[i]) # conv
	reg_heads=self.reg_c2(self.reg_c1(x[i])) #回归头
	x[i]=torch.cat([self.reg_head(reg_heads),self.obj_head(reg_heads),self.cls_head(self.cls_c2(self.cls_c1(x[i])))],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()
	if not self.training: # inference
	if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
	self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
	y = x[i].sigmoid()
	if self.inplace:
	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: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
	xy, wh, conf = y.tensor_split((2, 4), 4)
	xy = (xy * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
	wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
	y = torch.cat((xy, wh, conf), 4)
	z.append(y.view(bs, -1, self.no))
	return x if self.training else (torch.cat(z, 1), x)
	def _make_grid(self, nx=20, ny=20, i=0):
	d = self.anchors[i].device
	shape = 1, self.na, ny, nx, 2 # grid shape
	if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
	yv, xv = torch.meshgrid(torch.arange(ny).to(d), torch.arange(nx).to(d), indexing='ij')
	else:
	yv, xv = torch.meshgrid(torch.arange(ny, device=d), torch.arange(nx, device=d))
	grid = torch.stack((xv, yv), 2).expand(shape).float()
	anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape).float()
	return grid, anchor_grid
	class ASFF_Detect(nn.Module): #add ASFFV5 layer and Rfb
	stride = None # strides computed during build
	onnx_dynamic = False # ONNX export parameter
	def __init__(self, nc=80, anchors=(), ch=(), multiplier=0.5,rfb=False,inplace=True): # detection layer
	super().__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
	self.l0_fusion = ASFFV5(level=0, multiplier=multiplier,rfb=rfb)
	self.l1_fusion = ASFFV5(level=1, multiplier=multiplier,rfb=rfb)
	self.l2_fusion = ASFFV5(level=2, multiplier=multiplier,rfb=rfb)
	self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
	self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
	self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
	self.inplace = inplace # use in-place ops (e.g. slice assignment)
	def forward(self, x):
	z = [] # inference output
	result=[]
	result.append(self.l2_fusion(x))
	result.append(self.l1_fusion(x))
	result.append(self.l0_fusion(x))
	x=result
	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] or self.onnx_dynamic:
	self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
	y = x[i].sigmoid()
	if self.inplace:
	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: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
	xy, wh, conf = y.tensor_split((2, 4), 4)
	xy = (xy * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
	wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
	y = torch.cat((xy, wh, conf), 4)
	z.append(y.view(bs, -1, self.no))
	return x if self.training else (torch.cat(z, 1), x)
	def _make_grid(self, nx=20, ny=20, i=0):
	d = self.anchors[i].device
	shape = 1, self.na, ny, nx, 2 # grid shape
	if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
	yv, xv = torch.meshgrid(torch.arange(ny).to(d), torch.arange(nx).to(d), indexing='ij')
	else:
	yv, xv = torch.meshgrid(torch.arange(ny, device=d), torch.arange(nx, device=d))
	grid = torch.stack((xv, yv), 2).expand(shape).float()
	anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape).float()
	return grid, anchor_grid
	class Model(nn.Module):
	def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
	super().__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, encoding='ascii', errors='ignore') 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)
	# Build strides, anchors
	m = self.model[-1] # Detect()
	if isinstance(m, Detect)or isinstance(m, ASFF_Detect)or isinstance(m,Decoupled_Detect):
	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
	# Init weights, biases
	initialize_weights(self)
	self.info()
	LOGGER.info('')
	def forward(self, x, augment=False, profile=False, visualize=False):
	if augment:
	return self._forward_augment(x) # augmented inference, None
	return self._forward_once(x, profile, visualize) # 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)
	y = self._clip_augmented(y) # clip augmented tails
	return torch.cat(y, 1), None # augmented inference, train
	def _forward_once(self, x, profile=False, visualize=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 profile:
	self._profile_one_layer(m, x, dt)
	x = m(x) # run
	y.append(x if m.i in self.save else None) # save output
	if visualize:
	feature_visualization(x, m.type, m.i, save_dir=visualize)
	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 _clip_augmented(self, y):
	# Clip YOLOv5 augmented inference tails
	nl = self.model[-1].nl # number of detection layers (P3-P5)
	g = sum(4 ** x for x in range(nl)) # grid points
	e = 1 # exclude layer count
	i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices
	y[0] = y[0][:, :-i] # large
	i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
	y[-1] = y[-1][:, i:] # small
	return y
	def _profile_one_layer(self, m, x, dt):
	c = isinstance(m, Detect) or isinstance(m, ASFF_Detect) # is final layer, copy input as inplace fix
	o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
	t = time_sync()
	for _ in range(10):
	m(x.copy() if c else x)
	dt.append((time_sync() - 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}')
	if c:
	LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
	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 isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
	m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
	delattr(m, 'bn') # remove batchnorm
	m.forward = m.forward_fuse # update forward
	self.info()
	return self
	def info(self, verbose=False, img_size=640): # print model information
	model_info(self, verbose, img_size)
	def _apply(self, fn):
	# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
	self = super()._apply(fn)
	m = self.model[-1] # Detect()
	if isinstance(m, Detect)or isinstance(m, ASFF_Detect) or isinstance(m,Decoupled_Detect):
	m.stride = fn(m.stride)
	m.grid = list(map(fn, m.grid))
	if isinstance(m.anchor_grid, list):
	m.anchor_grid = list(map(fn, m.anchor_grid))
	return self
	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 NameError:
	pass
	n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
	if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,CBAM,ResBlock_CBAM,
	CoordAtt,CrossConv,C3,CTR3,Involution, C3SPP, C3Ghost, CARAFE]:
	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,CTR3,C3Ghost]:
	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 Concat_bifpn:
	c2 = max([ch[x] for x in f])
	elif m is Detect:
	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 ASFF_Detect or (m is Decoupled_Detect):
	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 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)
	#print('parse model success')
	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')
	parser.add_argument('--profile', action='store_true', help='profile model speed')
	opt = parser.parse_args()
	opt.cfg = check_yaml(opt.cfg) # check YAML
	print_args(FILE.stem, opt)
	device = select_device(opt.device)
	# Create model
	model = Model(opt.cfg).to(device)
	model.train()
	# Profile
	if opt.profile:
	img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).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

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