def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
opt = parser.parse_args()
opt.data = check_yaml(opt.data) # check YAML
opt.save_json |= opt.data.endswith('coco.yaml')
opt.save_txt |= opt.save_hybrid
print_args(FILE.stem, opt)
return opt
def main(opt, callbacks=Callbacks()):
# Checks
if RANK in [-1, 0]:
print_args(FILE.stem, opt)
check_git_status()
check_requirements(exclude=['thop'])
# Resume
if opt.resume and not check_wandb_resume(opt): # resume an interrupted run
ckpt = opt.resume if isinstance(
opt.resume,
str) else get_latest_run() # specified or most recent path
assert os.path.isfile(
ckpt), 'ERROR: --resume checkpoint does not exist'
with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
LOGGER.info(f'Resuming training from {ckpt}')
else:
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
assert len(opt.cfg) or len(
opt.weights), 'either --cfg or --weights must be specified'
opt.save_dir = str(
increment_path(Path(opt.project) / opt.name,
exist_ok=opt.exist_ok))
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
if LOCAL_RANK != -1:
assert torch.cuda.device_count(
) > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
torch.cuda.set_device(LOCAL_RANK)
device = torch.device('cuda', LOCAL_RANK)
dist.init_process_group(
backend="nccl" if dist.is_nccl_available() else "gloo")
# Train
train(opt.hyp, opt, device, callbacks)
if WORLD_SIZE > 1 and RANK == 0:
LOGGER.info('Destroying process group... ')
dist.destroy_process_group()
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights',
type=str,
default=ROOT / 'yolov5s.pt',
help='weights path')
parser.add_argument('--imgsz',
'--img',
'--img-size',
nargs='+',
type=int,
default=[640],
help='inference size h,w')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--dynamic',
action='store_true',
help='dynamic batch size')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(FILE.stem, opt)
return opt
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights',
nargs='+',
type=str,
default=ROOT / 'yolov5s.pt',
help='model path(s)')
parser.add_argument('--source',
type=str,
default=ROOT / 'data/images',
help='file/dir/URL/glob, 0 for webcam')
parser.add_argument('--data',
type=str,
default=ROOT / 'data/coco128.yaml',
help='(optional) dataset.yaml path')
parser.add_argument('--imgsz',
'--img',
'--img-size',
nargs='+',
type=int,
default=[640],
help='inference size h,w')
parser.add_argument('--conf-thres',
type=float,
default=0.25,
help='confidence threshold')
parser.add_argument('--iou-thres',
type=float,
default=0.45,
help='NMS IoU threshold')
parser.add_argument('--max-det',
type=int,
default=1000,
help='maximum detections per image')
parser.add_argument('--device',
default='',
help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='show results')
parser.add_argument('--save-txt',
action='store_true',
help='save results to *.txt')
parser.add_argument('--save-conf',
action='store_true',
help='save confidences in --save-txt labels')
parser.add_argument('--save-crop',
action='store_true',
help='save cropped prediction boxes')
parser.add_argument('--nosave',
action='store_true',
help='do not save images/videos')
parser.add_argument(
'--classes',
nargs='+',
type=int,
help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--agnostic-nms',
action='store_true',
help='class-agnostic NMS')
parser.add_argument('--augment',
action='store_true',
help='augmented inference')
parser.add_argument('--visualize',
action='store_true',
help='visualize features')
parser.add_argument('--update',
action='store_true',
help='update all models')
parser.add_argument('--project',
default=ROOT / 'runs/detect',
help='save results to project/name')
parser.add_argument('--name',
default='exp',
help='save results to project/name')
parser.add_argument('--exist-ok',
action='store_true',
help='existing project/name ok, do not increment')
parser.add_argument('--line-thickness',
default=3,
type=int,
help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels',
default=False,
action='store_true',
help='hide labels')
parser.add_argument('--hide-conf',
default=False,
action='store_true',
help='hide confidences')
parser.add_argument('--half',
action='store_true',
help='use FP16 half-precision inference')
parser.add_argument('--dnn',
action='store_true',
help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(FILE.stem, opt)
return opt
def main(opt, callbacks=Callbacks()):
# Checks
if RANK in [-1, 0]:
print_args(FILE.stem, opt)
check_git_status()
check_requirements(exclude=['thop'])
# Resume
if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
LOGGER.info(f'Resuming training from {ckpt}')
else:
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
if opt.evolve:
if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve
opt.project = str(ROOT / 'runs/evolve')
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
if LOCAL_RANK != -1:
msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
assert not opt.image_weights, f'--image-weights {msg}'
assert not opt.evolve, f'--evolve {msg}'
assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
torch.cuda.set_device(LOCAL_RANK)
device = torch.device('cuda', LOCAL_RANK)
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
# Train
if not opt.evolve:
train(opt.hyp, opt, device, callbacks)
if WORLD_SIZE > 1 and RANK == 0:
LOGGER.info('Destroying process group... ')
dist.destroy_process_group()
# Evolve hyperparameters (optional)
else:
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
'box': (1, 0.02, 0.2), # box loss gain
'cls': (1, 0.2, 4.0), # cls loss gain
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
'iou_t': (0, 0.1, 0.7), # IoU training threshold
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
'mixup': (1, 0.0, 1.0), # image mixup (probability)
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
with open(opt.hyp, errors='ignore') as f:
hyp = yaml.safe_load(f) # load hyps dict
if 'anchors' not in hyp: # anchors commented in hyp.yaml
hyp['anchors'] = 3
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
if opt.bucket:
os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}') # download evolve.csv if exists
for _ in range(opt.evolve): # generations to evolve
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
mp, s = 0.8, 0.2 # mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
ng = len(meta)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = float(x[i + 7] * v[i]) # mutate
# Constrain to limits
for k, v in meta.items():
hyp[k] = max(hyp[k], v[1]) # lower limit
hyp[k] = min(hyp[k], v[2]) # upper limit
hyp[k] = round(hyp[k], 5) # significant digits
# Train mutation
results = train(hyp.copy(), opt, device, callbacks)
callbacks = Callbacks()
# Write mutation results
print_mutation(results, hyp.copy(), save_dir, opt.bucket)
# Plot results
plot_evolve(evolve_csv)
LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
f"Results saved to {colorstr('bold', save_dir)}\n"
f'Usage example: $ python train.py --hyp {evolve_yaml}')
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data',
type=str,
default=ROOT / 'data/coco128.yaml',
help='dataset.yaml path')
parser.add_argument('--weights',
nargs='+',
type=str,
default=ROOT / 'yolov5s.pt',
help='model.pt path(s)')
parser.add_argument('--imgsz',
'--img',
'--img-size',
nargs='+',
type=int,
default=[640, 640],
help='image (h, w)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--device',
default='cpu',
help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--half',
action='store_true',
help='FP16 half-precision export')
parser.add_argument('--inplace',
action='store_true',
help='set YOLOv5 Detect() inplace=True')
parser.add_argument('--train',
action='store_true',
help='model.train() mode')
parser.add_argument('--optimize',
action='store_true',
help='TorchScript: optimize for mobile')
parser.add_argument('--int8',
action='store_true',
help='CoreML/TF INT8 quantization')
parser.add_argument('--dynamic',
action='store_true',
help='ONNX/TF: dynamic axes')
parser.add_argument('--simplify',
action='store_true',
help='ONNX: simplify model')
parser.add_argument('--opset',
type=int,
default=12,
help='ONNX: opset version')
parser.add_argument('--verbose',
action='store_true',
help='TensorRT: verbose log')
parser.add_argument('--workspace',
type=int,
default=4,
help='TensorRT: workspace size (GB)')
parser.add_argument('--nms',
action='store_true',
help='TF: add NMS to model')
parser.add_argument('--agnostic-nms',
action='store_true',
help='TF: add agnostic NMS to model')
parser.add_argument('--topk-per-class',
type=int,
default=100,
help='TF.js NMS: topk per class to keep')
parser.add_argument('--topk-all',
type=int,
default=100,
help='TF.js NMS: topk for all classes to keep')
parser.add_argument('--iou-thres',
type=float,
default=0.45,
help='TF.js NMS: IoU threshold')
parser.add_argument('--conf-thres',
type=float,
default=0.25,
help='TF.js NMS: confidence threshold')
parser.add_argument(
'--include',
nargs='+',
default=['torchscript', 'onnx'],
help=
'torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs'
)
opt = parser.parse_args()
print_args(FILE.stem, opt)
return opt
parser.add_argument('--cfg',
type=str,
default='yolov5s.yaml',
help='model.yaml')
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('--test',
action='store_true',
help='test all yolo*.yaml')
opt = parser.parse_args()
opt.cfg = check_yaml(opt.cfg) # check YAML
print_args(FILE.stem, opt)
device = select_device(opt.device)
# Create model
model = Model(opt.cfg).to(device)
model.train()
# Profile
if opt.profile:
img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640,
640).to(device)
y = model(img, profile=True)
# Test all models
if opt.test:
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
