# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
YOLO-specific modules.
Usage:
$ python models/yolo.py --cfg yolov5s.yaml
"""
import argparse
import contextlib
import math
import os
import platform
import sys
from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
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 (
C3,
C3SPP,
C3TR,
SPP,
SPPF,
Bottleneck,
BottleneckCSP,
C3Ghost,
C3x,
Classify,
Concat,
Contract,
Conv,
CrossConv,
DetectMultiBackend,
DWConv,
DWConvTranspose2d,
Expand,
Focus,
GhostBottleneck,
GhostConv,
Proto,
)
from models.experimental import MixConv2d
from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, colorstr, 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 processing input tensors and generating detection outputs in object 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):
"""Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations."""
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):
"""Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`."""
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")):
"""Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10."""
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, extending Detect with mask and prototype layers."""
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
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):
"""Processes input through the network, returning detections and prototypes; adjusts output based on
training/export mode.
"""
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):
"""Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and
visualization.
"""
return self._forward_once(x, profile, visualize) # single-scale inference, train
def _forward_once(self, x, profile=False, visualize=False):
"""Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options."""
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):
"""Profiles a single layer's performance by computing GFLOPs, execution time, and parameters."""
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):
"""Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
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):
"""Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`."""
model_info(self, verbose, img_size)
def _apply(self, fn):
"""Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding 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 class for object detection tasks, supporting custom configurations and anchors."""
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
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)):
def _forward(x):
"""Passes the input 'x' through the model and returns the processed output."""
return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
s = 256 # 2x min stride
m.inplace = self.inplace
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):
"""Performs single-scale or augmented inference and may include profiling or visualization."""
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):
"""Performs augmented inference across different scales and flips, returning combined detections."""
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-scales predictions from augmented inference, adjusting for flips and image size."""
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):
"""Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and
layer counts.
"""
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):
"""
Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf).
For details see 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 for object detection and segmentation tasks with configurable parameters."""
def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
super().__init__(cfg, ch, nc, anchors)
class ClassificationModel(BaseModel):
"""YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
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):
"""Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification
layer.
"""
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):
"""Creates a YOLOv5 classification model from a specified *.yaml configuration file."""
self.model = None
def parse_model(d, ch):
"""Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture."""
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw, act, ch_mul = (
d["anchors"],
d["nc"],
d["depth_multiple"],
d["width_multiple"],
d.get("activation"),
d.get("channel_multiple"),
)
if act:
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
LOGGER.info(f"{colorstr('activation:')} {act}") # print
if not ch_mul:
ch_mul = 8
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, ch_mul)
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, ch_mul)
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()