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| import math | |
| from copy import deepcopy | |
| from pathlib import Path | |
| import torch | |
| import yaml # for torch hub | |
| from torch import nn | |
| from facelib.detection.yolov5face.models.common import ( | |
| C3, | |
| NMS, | |
| SPP, | |
| AutoShape, | |
| Bottleneck, | |
| BottleneckCSP, | |
| Concat, | |
| Conv, | |
| DWConv, | |
| Focus, | |
| ShuffleV2Block, | |
| StemBlock, | |
| ) | |
| from facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d | |
| from facelib.detection.yolov5face.utils.autoanchor import check_anchor_order | |
| from facelib.detection.yolov5face.utils.general import make_divisible | |
| from facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn | |
| class Detect(nn.Module): | |
| stride = None # strides computed during build | |
| export = False # onnx export | |
| def __init__(self, nc=80, anchors=(), ch=()): # detection layer | |
| super().__init__() | |
| self.nc = nc # number of classes | |
| self.no = nc + 5 + 10 # 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): | |
| z = [] # inference output | |
| if self.export: | |
| for i in range(self.nl): | |
| x[i] = self.m[i](x[i]) | |
| return x | |
| 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 = torch.full_like(x[i], 0) | |
| y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid() | |
| y[..., 5:15] = x[i][..., 5:15] | |
| y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy | |
| y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh | |
| y[..., 5:7] = ( | |
| y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] | |
| ) # landmark x1 y1 | |
| y[..., 7:9] = ( | |
| y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] | |
| ) # landmark x2 y2 | |
| y[..., 9:11] = ( | |
| y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] | |
| ) # landmark x3 y3 | |
| y[..., 11:13] = ( | |
| y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] | |
| ) # landmark x4 y4 | |
| y[..., 13:15] = ( | |
| y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] | |
| ) # landmark x5 y5 | |
| z.append(y.view(bs, -1, self.no)) | |
| return x if self.training else (torch.cat(z, 1), x) | |
| def _make_grid(nx=20, ny=20): | |
| # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10 | |
| 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): # model, input channels, number of classes | |
| super().__init__() | |
| self.yaml_file = Path(cfg).name | |
| with Path(cfg).open(encoding="utf8") 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"]: | |
| self.yaml["nc"] = nc # 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 | |
| # Build strides, anchors | |
| m = self.model[-1] # Detect() | |
| if isinstance(m, Detect): | |
| s = 128 # 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 | |
| def forward(self, x): | |
| return self.forward_once(x) # single-scale inference, train | |
| def forward_once(self, x): | |
| y = [] # 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 | |
| x = m(x) # run | |
| y.append(x if m.i in self.save else None) # save output | |
| 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 | |
| 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) | |
| print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) | |
| def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers | |
| print("Fusing layers... ") | |
| for m in self.model.modules(): | |
| if isinstance(m, 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 | |
| elif type(m) is nn.Upsample: | |
| m.recompute_scale_factor = None # torch 1.11.0 compatibility | |
| return self | |
| def nms(self, mode=True): # add or remove NMS module | |
| present = isinstance(self.model[-1], 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=str(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 parse_model(d, ch): # model_dict, input_channels(3) | |
| 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 [ | |
| Conv, | |
| Bottleneck, | |
| SPP, | |
| DWConv, | |
| MixConv2d, | |
| Focus, | |
| CrossConv, | |
| BottleneckCSP, | |
| C3, | |
| ShuffleV2Block, | |
| StemBlock, | |
| ]: | |
| c1, c2 = ch[f], args[0] | |
| c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 | |
| args = [c1, c2, *args[1:]] | |
| if m in [BottleneckCSP, C3]: | |
| args.insert(2, n) | |
| n = 1 | |
| elif m is nn.BatchNorm2d: | |
| args = [ch[f]] | |
| elif m is Concat: | |
| c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) | |
| elif m is Detect: | |
| args.append([ch[x + 1] for x in f]) | |
| if isinstance(args[1], int): # number of anchors | |
| args[1] = [list(range(args[1] * 2))] * len(f) | |
| 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 | |
| save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist | |
| layers.append(m_) | |
| ch.append(c2) | |
| return nn.Sequential(*layers), sorted(save) | |