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- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- YOLO-specific modules
- Usage:
- $ python path/to/models/yolo.py --cfg yolov5s.yaml
- """
- import argparse
- import sys
- from copy import deepcopy
- 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
- from models.common import *
- from models.experimental import *
- from utils.autoanchor import check_anchor_order
- from utils.general import check_yaml, make_divisible, set_logging
- from utils.plots import feature_visualization
- from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
- select_device, time_sync
- try:
- import thop # for FLOPs computation
- except ImportError:
- thop = None
- LOGGER = logging.getLogger(__name__)
- 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
- 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.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._make_grid(nx, ny).to(x[i].device)
- 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 = (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
- 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().__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)
- # Build strides, anchors
- m = self.model[-1] # Detect()
- if isinstance(m, 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)
- 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 _profile_one_layer(self, m, x, dt):
- c = isinstance(m, 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 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, 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 = 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, C3, C3TR, C3SPP, C3Ghost]:
- 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, 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 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)
- 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
- set_logging()
- 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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