Spaces:
Running
Running
| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
| """ | |
| YOLO-specific modules | |
| Usage: | |
| $ python models/yolo.py --cfg yolov5s.yaml | |
| """ | |
| import argparse | |
| import contextlib | |
| import os | |
| import platform | |
| 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 | |
| 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, profile, scale_img, select_device, | |
| time_sync) | |
| try: | |
| import thop # for FLOPs computation | |
| except ImportError: | |
| thop = None | |
| class Detect(nn.Module): | |
| # YOLOv5 Detect head for detection models | |
| stride = None # strides computed during build | |
| dynamic = False # force grid reconstruction | |
| export = False # export mode | |
| 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.empty(0) for _ in range(self.nl)] # init grid | |
| self.anchor_grid = [torch.empty(0) for _ in range(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 inplace 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.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) | |
| if isinstance(self, Segment): # (boxes + masks) | |
| xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) | |
| xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy | |
| wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh | |
| y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) | |
| else: # Detect (boxes only) | |
| xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) | |
| xy = (xy * 2 + 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, self.na * nx * ny, self.no)) | |
| return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) | |
| def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')): | |
| d = self.anchors[i].device | |
| t = self.anchors[i].dtype | |
| shape = 1, self.na, ny, nx, 2 # grid shape | |
| y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) | |
| yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility | |
| grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 | |
| anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) | |
| return grid, anchor_grid | |
| class Segment(Detect): | |
| # YOLOv5 Segment head for segmentation models | |
| def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): | |
| super().__init__(nc, anchors, ch, inplace) | |
| self.nm = nm # number of masks | |
| self.npr = npr # number of protos | |
| self.no = 5 + nc + self.nm # number of outputs per anchor | |
| self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv | |
| self.proto = Proto(ch[0], self.npr, self.nm) # protos | |
| self.detect = Detect.forward | |
| def forward(self, x): | |
| p = self.proto(x[0]) | |
| x = self.detect(self, x) | |
| return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) | |
| class BaseModel(nn.Module): | |
| # YOLOv5 base model | |
| def forward(self, x, profile=False, visualize=False): | |
| return self._forward_once(x, profile, visualize) # single-scale 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 _profile_one_layer(self, m, x, dt): | |
| c = m == self.model[-1] # 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 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, Segment)): | |
| 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 | |
| class DetectionModel(BaseModel): | |
| # YOLOv5 detection model | |
| 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, Segment)): | |
| s = 256 # 2x min stride | |
| m.inplace = self.inplace | |
| forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) | |
| m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward | |
| check_anchor_order(m) | |
| m.anchors /= m.stride.view(-1, 1, 1) | |
| 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 _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 _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[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls | |
| mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) | |
| Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility | |
| class SegmentationModel(DetectionModel): | |
| # YOLOv5 segmentation model | |
| def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): | |
| super().__init__(cfg, ch, nc, anchors) | |
| class ClassificationModel(BaseModel): | |
| # YOLOv5 classification model | |
| def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index | |
| super().__init__() | |
| self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) | |
| def _from_detection_model(self, model, nc=1000, cutoff=10): | |
| # Create a YOLOv5 classification model from a YOLOv5 detection model | |
| if isinstance(model, DetectMultiBackend): | |
| model = model.model # unwrap DetectMultiBackend | |
| model.model = model.model[:cutoff] # backbone | |
| m = model.model[-1] # last layer | |
| ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module | |
| c = Classify(ch, nc) # Classify() | |
| c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type | |
| model.model[-1] = c # replace | |
| self.model = model.model | |
| self.stride = model.stride | |
| self.save = [] | |
| self.nc = nc | |
| def _from_yaml(self, cfg): | |
| # Create a YOLOv5 classification model from a *.yaml file | |
| self.model = None | |
| def parse_model(d, ch): # model_dict, input_channels(3) | |
| # Parse a YOLOv5 model.yaml dictionary | |
| LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") | |
| anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') | |
| if act: | |
| Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() | |
| LOGGER.info(f"{colorstr('activation:')} {act}") # print | |
| 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): | |
| with contextlib.suppress(NameError): | |
| args[j] = eval(a) if isinstance(a, str) else a # eval strings | |
| 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, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: | |
| 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, C3x}: | |
| 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) | |
| # TODO: channel, gw, gd | |
| elif m in {Detect, Segment}: | |
| 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 m is Segment: | |
| args[3] = make_divisible(args[3] * gw, 8) | |
| 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(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # 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('--batch-size', type=int, default=1, help='total batch size for all GPUs') | |
| 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') | |
| parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer') | |
| parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') | |
| opt = parser.parse_args() | |
| opt.cfg = check_yaml(opt.cfg) # check YAML | |
| print_args(vars(opt)) | |
| device = select_device(opt.device) | |
| # Create model | |
| im = torch.rand(opt.batch_size, 3, 640, 640).to(device) | |
| model = Model(opt.cfg).to(device) | |
| # Options | |
| if opt.line_profile: # profile layer by layer | |
| model(im, profile=True) | |
| elif opt.profile: # profile forward-backward | |
| results = profile(input=im, ops=[model], n=3) | |
| elif opt.test: # test all models | |
| for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): | |
| try: | |
| _ = Model(cfg) | |
| except Exception as e: | |
| print(f'Error in {cfg}: {e}') | |
| else: # report fused model summary | |
| model.fuse() | |