| 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 |
