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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Train a YOLOv5 classifier model on a classification dataset
Usage - Single-GPU training:
$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
Usage - Multi-GPU DDP training:
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
"""
import argparse
import os
import subprocess
import sys
import time
from copy import deepcopy
from datetime import datetime
from pathlib import Path
import torch
import torch.distributed as dist
import torch.hub as hub
import torch.optim.lr_scheduler as lr_scheduler
import torchvision
from torch.cuda import amp
from tqdm import tqdm
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
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from classify import val as validate
from models.experimental import attempt_load
from models.yolo import ClassificationModel, DetectionModel
from utils.dataloaders import create_classification_dataloader
from utils.general import (
DATASETS_DIR,
LOGGER,
TQDM_BAR_FORMAT,
WorkingDirectory,
check_git_info,
check_git_status,
check_requirements,
colorstr,
download,
increment_path,
init_seeds,
print_args,
yaml_save,
)
from utils.loggers import GenericLogger
from utils.plots import imshow_cls
from utils.torch_utils import (
ModelEMA,
model_info,
reshape_classifier_output,
select_device,
smart_DDP,
smart_optimizer,
smartCrossEntropyLoss,
torch_distributed_zero_first,
)
LOCAL_RANK = int(
os.getenv("LOCAL_RANK", -1)
) # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv("RANK", -1))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
GIT_INFO = check_git_info()
def train(opt, device):
init_seeds(opt.seed + 1 + RANK, deterministic=True)
save_dir, data, bs, epochs, nw, imgsz, pretrained = (
opt.save_dir,
Path(opt.data),
opt.batch_size,
opt.epochs,
min(os.cpu_count() - 1, opt.workers),
opt.imgsz,
str(opt.pretrained).lower() == "true",
)
cuda = device.type != "cpu"
# Directories
wdir = save_dir / "weights"
wdir.mkdir(parents=True, exist_ok=True) # make dir
last, best = wdir / "last.pt", wdir / "best.pt"
# Save run settings
yaml_save(save_dir / "opt.yaml", vars(opt))
# Logger
logger = (
GenericLogger(opt=opt, console_logger=LOGGER)
if RANK in {-1, 0}
else None
)
# Download Dataset
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
data_dir = data if data.is_dir() else (DATASETS_DIR / data)
if not data_dir.is_dir():
LOGGER.info(
f"\nDataset not found ⚠️, missing path {data_dir}, attempting download..."
)
t = time.time()
if str(data) == "imagenet":
subprocess.run(
f"bash {ROOT / 'data/scripts/get_imagenet.sh'}",
shell=True,
check=True,
)
else:
url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip"
download(url, dir=data_dir.parent)
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
LOGGER.info(s)
# Dataloaders
nc = len(
[x for x in (data_dir / "train").glob("*") if x.is_dir()]
) # number of classes
trainloader = create_classification_dataloader(
path=data_dir / "train",
imgsz=imgsz,
batch_size=bs // WORLD_SIZE,
augment=True,
cache=opt.cache,
rank=LOCAL_RANK,
workers=nw,
)
test_dir = (
data_dir / "test" if (data_dir / "test").exists() else data_dir / "val"
) # data/test or data/val
if RANK in {-1, 0}:
testloader = create_classification_dataloader(
path=test_dir,
imgsz=imgsz,
batch_size=bs // WORLD_SIZE * 2,
augment=False,
cache=opt.cache,
rank=-1,
workers=nw,
)
# Model
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
if Path(opt.model).is_file() or opt.model.endswith(".pt"):
model = attempt_load(opt.model, device="cpu", fuse=False)
elif (
opt.model in torchvision.models.__dict__
): # TorchVision models i.e. resnet50, efficientnet_b0
model = torchvision.models.__dict__[opt.model](
weights="IMAGENET1K_V1" if pretrained else None
)
else:
m = hub.list(
"ultralytics/yolov5"
) # + hub.list('pytorch/vision') # models
raise ModuleNotFoundError(
f"--model {opt.model} not found. Available models are: \n"
+ "\n".join(m)
)
if isinstance(model, DetectionModel):
LOGGER.warning(
"WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'"
)
model = ClassificationModel(
model=model, nc=nc, cutoff=opt.cutoff or 10
) # convert to classification model
reshape_classifier_output(model, nc) # update class count
for m in model.modules():
if not pretrained and hasattr(m, "reset_parameters"):
m.reset_parameters()
if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
m.p = opt.dropout # set dropout
for p in model.parameters():
p.requires_grad = True # for training
model = model.to(device)
# Info
if RANK in {-1, 0}:
model.names = trainloader.dataset.classes # attach class names
model.transforms = (
testloader.dataset.torch_transforms
) # attach inference transforms
model_info(model)
if opt.verbose:
LOGGER.info(model)
images, labels = next(iter(trainloader))
file = imshow_cls(
images[:25],
labels[:25],
names=model.names,
f=save_dir / "train_images.jpg",
)
logger.log_images(file, name="Train Examples")
logger.log_graph(model, imgsz) # log model
# Optimizer
optimizer = smart_optimizer(
model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay
)
# Scheduler
lrf = 0.01 # final lr (fraction of lr0)
# lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine
lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf # linear
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
# final_div_factor=1 / 25 / lrf)
# EMA
ema = ModelEMA(model) if RANK in {-1, 0} else None
# DDP mode
if cuda and RANK != -1:
model = smart_DDP(model)
# Train
t0 = time.time()
criterion = smartCrossEntropyLoss(
label_smoothing=opt.label_smoothing
) # loss function
best_fitness = 0.0
scaler = amp.GradScaler(enabled=cuda)
val = test_dir.stem # 'val' or 'test'
LOGGER.info(
f"Image sizes {imgsz} train, {imgsz} test\n"
f"Using {nw * WORLD_SIZE} dataloader workers\n"
f"Logging results to {colorstr('bold', save_dir)}\n"
f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
)
for epoch in range(epochs): # loop over the dataset multiple times
tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
model.train()
if RANK != -1:
trainloader.sampler.set_epoch(epoch)
pbar = enumerate(trainloader)
if RANK in {-1, 0}:
pbar = tqdm(
enumerate(trainloader),
total=len(trainloader),
bar_format=TQDM_BAR_FORMAT,
)
for i, (images, labels) in pbar: # progress bar
images, labels = images.to(device, non_blocking=True), labels.to(
device
)
# Forward
with amp.autocast(enabled=cuda): # stability issues when enabled
loss = criterion(model(images), labels)
# Backward
scaler.scale(loss).backward()
# Optimize
scaler.unscale_(optimizer) # unscale gradients
torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm=10.0
) # clip gradients
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
if RANK in {-1, 0}:
# Print
tloss = (tloss * i + loss.item()) / (
i + 1
) # update mean losses
mem = "%.3gG" % (
torch.cuda.memory_reserved() / 1e9
if torch.cuda.is_available()
else 0
) # (GB)
pbar.desc = (
f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}"
+ " " * 36
)
# Test
if i == len(pbar) - 1: # last batch
top1, top5, vloss = validate.run(
model=ema.ema,
dataloader=testloader,
criterion=criterion,
pbar=pbar,
) # test accuracy, loss
fitness = top1 # define fitness as top1 accuracy
# Scheduler
scheduler.step()
# Log metrics
if RANK in {-1, 0}:
# Best fitness
if fitness > best_fitness:
best_fitness = fitness
# Log
metrics = {
"train/loss": tloss,
f"{val}/loss": vloss,
"metrics/accuracy_top1": top1,
"metrics/accuracy_top5": top5,
"lr/0": optimizer.param_groups[0]["lr"],
} # learning rate
logger.log_metrics(metrics, epoch)
# Save model
final_epoch = epoch + 1 == epochs
if (not opt.nosave) or final_epoch:
ckpt = {
"epoch": epoch,
"best_fitness": best_fitness,
"model": deepcopy(
ema.ema
).half(), # deepcopy(de_parallel(model)).half(),
"ema": None, # deepcopy(ema.ema).half(),
"updates": ema.updates,
"optimizer": None, # optimizer.state_dict(),
"opt": vars(opt),
"git": GIT_INFO, # {remote, branch, commit} if a git repo
"date": datetime.now().isoformat(),
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fitness:
torch.save(ckpt, best)
del ckpt
# Train complete
if RANK in {-1, 0} and final_epoch:
LOGGER.info(
f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
f"\nResults saved to {colorstr('bold', save_dir)}"
f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
f"\nExport: python export.py --weights {best} --include onnx"
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
f"\nVisualize: https://netron.app\n"
)
# Plot examples
images, labels = (
x[:25] for x in next(iter(testloader))
) # first 25 images and labels
pred = torch.max(ema.ema(images.to(device)), 1)[1]
file = imshow_cls(
images,
labels,
pred,
model.names,
verbose=False,
f=save_dir / "test_images.jpg",
)
# Log results
meta = {
"epochs": epochs,
"top1_acc": best_fitness,
"date": datetime.now().isoformat(),
}
logger.log_images(
file, name="Test Examples (true-predicted)", epoch=epoch
)
logger.log_model(best, epochs, metadata=meta)
def parse_opt(known=False):
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
type=str,
default="yolov5s-cls.pt",
help="initial weights path",
)
parser.add_argument(
"--data",
type=str,
default="imagenette160",
help="cifar10, cifar100, mnist, imagenet, ...",
)
parser.add_argument(
"--epochs", type=int, default=10, help="total training epochs"
)
parser.add_argument(
"--batch-size",
type=int,
default=64,
help="total batch size for all GPUs",
)
parser.add_argument(
"--imgsz",
"--img",
"--img-size",
type=int,
default=224,
help="train, val image size (pixels)",
)
parser.add_argument(
"--nosave", action="store_true", help="only save final checkpoint"
)
parser.add_argument(
"--cache",
type=str,
nargs="?",
const="ram",
help='--cache images in "ram" (default) or "disk"',
)
parser.add_argument(
"--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
)
parser.add_argument(
"--workers",
type=int,
default=8,
help="max dataloader workers (per RANK in DDP mode)",
)
parser.add_argument(
"--project",
default=ROOT / "runs/train-cls",
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(
"--pretrained",
nargs="?",
const=True,
default=True,
help="start from i.e. --pretrained False",
)
parser.add_argument(
"--optimizer",
choices=["SGD", "Adam", "AdamW", "RMSProp"],
default="Adam",
help="optimizer",
)
parser.add_argument(
"--lr0", type=float, default=0.001, help="initial learning rate"
)
parser.add_argument(
"--decay", type=float, default=5e-5, help="weight decay"
)
parser.add_argument(
"--label-smoothing",
type=float,
default=0.1,
help="Label smoothing epsilon",
)
parser.add_argument(
"--cutoff",
type=int,
default=None,
help="Model layer cutoff index for Classify() head",
)
parser.add_argument(
"--dropout", type=float, default=None, help="Dropout (fraction)"
)
parser.add_argument("--verbose", action="store_true", help="Verbose mode")
parser.add_argument(
"--seed", type=int, default=0, help="Global training seed"
)
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help="Automatic DDP Multi-GPU argument, do not modify",
)
return parser.parse_known_args()[0] if known else parser.parse_args()
def main(opt):
# Checks
if RANK in {-1, 0}:
print_args(vars(opt))
check_git_status()
check_requirements()
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
if LOCAL_RANK != -1:
assert (
opt.batch_size != -1
), "AutoBatch is coming soon for classification, 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"
)
# Parameters
opt.save_dir = increment_path(
Path(opt.project) / opt.name, exist_ok=opt.exist_ok
) # increment run
# Train
train(opt, device)
def run(**kwargs):
# Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
opt = parse_opt(True)
for k, v in kwargs.items():
setattr(opt, k, v)
main(opt)
return opt
if __name__ == "__main__":
opt = parse_opt()
main(opt)
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