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# 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}" | |
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() | |