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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)
@staticmethod
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)
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