From 0c3fe173b60b119a4df050067c936564d05e4775 Mon Sep 17 00:00:00 2001 From: Yunhao Meng Date: Sat, 18 Oct 2025 22:03:55 +0800 Subject: [PATCH] Initial commit --- .gitignore | 302 ++++++++++++++++++ Dockerfile | 27 ++ LICENSE | 18 ++ README.md | 79 +++++ docker-compose.yml | 59 ++++ main.py | 331 +++++++++++++++++++ main.sh | 2 + nets/nn.py | 357 +++++++++++++++++++++ utils/args.yaml | 100 ++++++ utils/dataset.py | 415 ++++++++++++++++++++++++ utils/util.py | 777 +++++++++++++++++++++++++++++++++++++++++++++ 11 files changed, 2467 insertions(+) create mode 100644 .gitignore create mode 100644 Dockerfile create mode 100644 LICENSE create mode 100644 README.md create mode 100644 docker-compose.yml create mode 100755 main.py create mode 100755 main.sh create mode 100755 nets/nn.py create mode 100755 utils/args.yaml create mode 100644 utils/dataset.py create mode 100644 utils/util.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..76052ab --- /dev/null +++ b/.gitignore @@ -0,0 +1,302 @@ +# ---> Python +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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IN NO +EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..9c27934 --- /dev/null +++ b/README.md @@ -0,0 +1,79 @@ +YOLOv11 re-implementation using PyTorch + +### fix +* fix the label size [0,1] tensor, which have two dim not adjusted size of [1,] + * if pic do not have object (if label is empty), the phenomenon occurs + * find XXX to look + +### Installation + +``` +conda create -n YOLO python=3.10.10 +conda activate YOLO +conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia +pip install opencv-python +pip install PyYAML +pip install tqdm +``` + +### Train + +* Configure your dataset path in `main.py` for training +* Run `bash main.sh $ --train` for training, `$` is number of GPUs +* Run `nohup bash main.sh 1 --train --epochs 300 > train.log 2>&1 &` for training in background + +### Test + +* Configure your dataset path in `main.py` for testing +* Run `python main.py --test` for testing + +### Results + +| Version | Epochs | Box mAP | Download | +|:-------:|:------:|--------:|--------------------------------------------------------------------------------------:| +| v11_n | 600 | 38.6 | [Model](https://github.com/jahongir7174/YOLOv11-pt/blob/master/weights/best.pt) | +| v11_n* | - | 39.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_n.pt) | +| v11_s* | - | 46.5 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_s.pt) | +| v11_m* | - | 51.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_m.pt) | +| v11_l* | - | 53.0 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_l.pt) | +| v11_x* | - | 54.3 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_x.pt) | + +``` + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.415 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.569 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.321 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.533 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.588 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.777 +``` + +* `*` means that it is from original repository, see reference +* In the official YOLOv11 code, mask annotation information is used, which leads to higher performance + +### Dataset structure + + ├── COCO + ├── images + ├── train2017 + ├── 1111.jpg + ├── 2222.jpg + ├── val2017 + ├── 1111.jpg + ├── 2222.jpg + ├── labels + ├── train2017 + ├── 1111.txt + ├── 2222.txt + ├── val2017 + ├── 1111.txt + ├── 2222.txt + +#### Reference + +* [YOLOv11-pt](https://github.com/jahongir7174/YOLOv11-pt) diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..846201f --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,59 @@ +# version: "3.9" + +services: + dl: + build: + context: . + dockerfile: Dockerfile + args: + BASE_IMAGE: "pytorch/pytorch:2.9.0-cuda13.0-cudnn9-devel" + USER: "dev" + UID: "1000" + GID: "1000" + container_name: dl + # GPUs + large DataLoader shared memory + gpus: all + shm_size: "12g" + ipc: host + + environment: + # Always use GPUs (you can limit to some: e.g., "0,1") + - NVIDIA_VISIBLE_DEVICES=0 + - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video + # Prefer NCCL for multi-GPU + - TORCH_DISTRIBUTED_DEBUG=INFO + - NCCL_P2P_DISABLE=0 + - NCCL_ASYNC_ERROR_HANDLING=1 + # Persisted virtualenv on PATH (lives in a named volume) + - VIRTUAL_ENV=./venv + - PATH=./venv/bin:/usr/local/bin:/usr/bin:/bin + - PYTHONUNBUFFERED=1 + - TZ=America/Los_Angeles + + volumes: + # your code/data + - .:/workspace + - /home/image1325/ssd1/dataset/coco:/data + # persisted venv: your pip installs live here and survive image/container removal + - venv:./venv + # (optional) speed up installs + - pip-cache:/home/dev/.cache/pip + + working_dir: /workspace + ulimits: + memlock: -1 + stack: 67108864 + + # On first run, create the venv if it doesn't exist; then drop to a shell. + command: > + bash -lc " + if [ ! -d /opt/venv/bin ]; then + python -m venv /opt/venv; + /opt/venv/bin/python -m pip install --upgrade pip; + fi; + exec bash + " + +volumes: + venv: + pip-cache: diff --git a/main.py b/main.py new file mode 100755 index 0000000..2eb698e --- /dev/null +++ b/main.py @@ -0,0 +1,331 @@ +import copy +import csv +import os +import warnings +from argparse import ArgumentParser +from typing import cast + +import torch +import tqdm +import yaml +from torch.utils import data +from torch.amp.autocast_mode import autocast + +from nets import nn +from utils import util +from utils.dataset import Dataset + +warnings.filterwarnings("ignore") + +data_dir = "/home/image1325/ssd1/dataset/coco" + + +def train(args, params): + # Model + model = nn.yolo_v11_n(len(params["names"])) + model.cuda() + + # Optimizer + accumulate = max(round(64 / (args.batch_size * args.world_size)), 1) + params["weight_decay"] *= args.batch_size * args.world_size * accumulate / 64 + + optimizer = torch.optim.SGD( + util.set_params(model, params["weight_decay"]), params["min_lr"], params["momentum"], nesterov=True + ) + + # EMA + ema = util.EMA(model) if args.local_rank == 0 else None + + filenames = [] + with open(f"{data_dir}/train2017.txt") as f: + for filename in f.readlines(): + filename = os.path.basename(filename.rstrip()) + filenames.append(f"{data_dir}/images/train2017/" + filename) + + sampler = None + dataset = Dataset(filenames, args.input_size, params, augment=True) + + if args.distributed: + sampler = data.DistributedSampler(dataset) + + loader = data.DataLoader( + dataset, + args.batch_size, + sampler is None, + sampler, + num_workers=8, + pin_memory=True, + collate_fn=Dataset.collate_fn, + ) + + # Scheduler + num_steps = len(loader) + scheduler = util.LinearLR(args, params, num_steps) + + if args.distributed: + # DDP mode + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) + model = torch.nn.parallel.DistributedDataParallel( + module=model, device_ids=[args.local_rank], output_device=args.local_rank + ) + + best = 0 + amp_scale = torch.amp.grad_scaler.GradScaler() + criterion = util.ComputeLoss(model, params) + + with open("weights/step.csv", "w") as log: + if args.local_rank == 0: + logger = csv.DictWriter( + log, fieldnames=["epoch", "box", "cls", "dfl", "Recall", "Precision", "mAP@50", "mAP"] + ) + logger.writeheader() + + for epoch in range(args.epochs): + model.train() + if args.distributed and sampler: + sampler.set_epoch(epoch) + if args.epochs - epoch == 10: + ds = cast(Dataset, loader.dataset) + ds.mosaic = False + + p_bar = enumerate(loader) + + if args.local_rank == 0: + print(("\n" + "%10s" * 5) % ("epoch", "memory", "box", "cls", "dfl")) + p_bar = tqdm.tqdm(p_bar, total=num_steps, ascii=" >-") + + optimizer.zero_grad() + avg_box_loss = util.AverageMeter() + avg_cls_loss = util.AverageMeter() + avg_dfl_loss = util.AverageMeter() + for i, (samples, targets) in p_bar: + step = i + num_steps * epoch + scheduler.step(step, optimizer) + + samples = samples.cuda().float() / 255 + + # Forward + with autocast("cuda"): + outputs = model(samples) # forward + loss_box, loss_cls, loss_dfl = criterion(outputs, targets) + + avg_box_loss.update(loss_box.item(), samples.size(0)) + avg_cls_loss.update(loss_cls.item(), samples.size(0)) + avg_dfl_loss.update(loss_dfl.item(), samples.size(0)) + + loss_box *= args.batch_size # loss scaled by batch_size + loss_cls *= args.batch_size # loss scaled by batch_size + loss_dfl *= args.batch_size # loss scaled by batch_size + loss_box *= args.world_size # gradient averaged between devices in DDP mode + loss_cls *= args.world_size # gradient averaged between devices in DDP mode + loss_dfl *= args.world_size # gradient averaged between devices in DDP mode + + # Backward + amp_scale.scale(loss_box + loss_cls + loss_dfl).backward() + + # Optimize + if step % accumulate == 0: + # amp_scale.unscale_(optimizer) # unscale gradients + # util.clip_gradients(model) # clip gradients + amp_scale.step(optimizer) # optimizer.step + amp_scale.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + torch.cuda.synchronize() + + # Log + if args.local_rank == 0: + memory = f"{torch.cuda.memory_reserved() / 1e9:.4g}G" # (GB) + s = ("%10s" * 2 + "%10.3g" * 3) % ( + f"{epoch + 1}/{args.epochs}", + memory, + avg_box_loss.avg, + avg_cls_loss.avg, + avg_dfl_loss.avg, + ) + p_bar = cast(tqdm.tqdm, p_bar) + p_bar.set_description(s) + + if args.local_rank == 0: + # mAP + last = test(args, params, ema.ema if ema else None) + + logger.writerow( + { + "epoch": str(epoch + 1).zfill(3), + "box": str(f"{avg_box_loss.avg:.3f}"), + "cls": str(f"{avg_cls_loss.avg:.3f}"), + "dfl": str(f"{avg_dfl_loss.avg:.3f}"), + "mAP": str(f"{last[0]:.3f}"), + "mAP@50": str(f"{last[1]:.3f}"), + "Recall": str(f"{last[2]:.3f}"), + "Precision": str(f"{last[3]:.3f}"), + } + ) + log.flush() + + # Update best mAP + if last[0] > best: + best = last[0] + + # Save model + save = {"epoch": epoch + 1, "model": copy.deepcopy(ema.ema if ema else None)} + + # Save last, best and delete + torch.save(save, f="./weights/last.pt") + if best == last[0]: + torch.save(save, f="./weights/best.pt") + del save + + if args.local_rank == 0: + util.strip_optimizer("./weights/best.pt") # strip optimizers + util.strip_optimizer("./weights/last.pt") # strip optimizers + + +@torch.no_grad() +def test(args, params, model=None): + filenames = [] + with open(f"{data_dir}/val2017.txt") as f: + for filename in f.readlines(): + filename = os.path.basename(filename.rstrip()) + filenames.append(f"{data_dir}/images/val2017/" + filename) + + dataset = Dataset(filenames, args.input_size, params, augment=False) + loader = data.DataLoader( + dataset, batch_size=4, shuffle=False, num_workers=4, pin_memory=True, collate_fn=Dataset.collate_fn + ) + + plot = False + if not model: + plot = True + model = torch.load(f="./weights/best.pt", map_location="cuda", weights_only=False) + model = model["model"].float().fuse() + + model.half() + model.eval() + + # Configure + iou_v = torch.linspace(start=0.5, end=0.95, steps=10).cuda() # iou vector for mAP@0.5:0.95 + n_iou = iou_v.numel() + + m_pre = 0 + m_rec = 0 + map50 = 0 + mean_ap = 0 + metrics = [] + p_bar = tqdm.tqdm(loader, desc=("%10s" * 5) % ("", "precision", "recall", "mAP50", "mAP"), ascii=" >-") + for samples, targets in p_bar: + samples = samples.cuda() + samples = samples.half() # uint8 to fp16/32 + samples = samples / 255.0 # 0 - 255 to 0.0 - 1.0 + _, _, h, w = samples.shape # batch-size, channels, height, width + scale = torch.tensor((w, h, w, h)).cuda() + # Inference + outputs = model(samples) + # NMS + outputs = util.non_max_suppression(outputs) + # Metrics + for i, output in enumerate(outputs): + # Ensure idx is a 1D boolean mask (squeeze any trailing dimension) to match cls/box shapes + idx = targets["idx"] + if idx.dim() > 1: + idx = idx.squeeze(-1) + idx = idx == i + + # XXX: initially, the code was like below, which caused shape mismatch when idx has extra dimension + # idx = targets["idx"] == i + cls = targets["cls"][idx] + box = targets["box"][idx] + + cls = cls.cuda() + box = box.cuda() + + metric = torch.zeros(output.shape[0], n_iou, dtype=torch.bool).cuda() + + if output.shape[0] == 0: + if cls.shape[0]: + metrics.append((metric, *torch.zeros((2, 0)).cuda(), cls.squeeze(-1))) + continue + # Evaluate + if cls.shape[0]: + target = torch.cat(tensors=(cls, util.wh2xy(box) * scale), dim=1) + metric = util.compute_metric(output[:, :6], target, iou_v) + # Append + metrics.append((metric, output[:, 4], output[:, 5], cls.squeeze(-1))) + + # Compute metrics + metrics = [torch.cat(x, dim=0).cpu().numpy() for x in zip(*metrics)] # to numpy + if len(metrics) and metrics[0].any(): + tp, fp, m_pre, m_rec, map50, mean_ap = util.compute_ap(*metrics, plot=plot, names=params["names"]) + # Print results + print(("%10s" + "%10.3g" * 4) % ("", m_pre, m_rec, map50, mean_ap)) + # Return results + model.float() # for training + return mean_ap, map50, m_rec, m_pre + + +def profile(args, params): + import thop + + shape = (1, 3, args.input_size, args.input_size) + model = nn.yolo_v11_n(len(params["names"])).fuse() + + model.eval() + model(torch.zeros(shape)) + + x = torch.empty(shape) + flops, num_params = thop.profile(model, inputs=[x], verbose=False) + flops, num_params = thop.clever_format(nums=[2 * flops, num_params], format="%.3f") + + if args.local_rank == 0: + print(f"Number of parameters: {num_params}") + print(f"Number of FLOPs: {flops}") + + +def main(): + parser = ArgumentParser() + parser.add_argument("--input-size", default=640, type=int) + parser.add_argument("--batch-size", default=32, type=int) + parser.add_argument("--local-rank", default=0, type=int) + parser.add_argument("--local_rank", default=0, type=int) + parser.add_argument("--epochs", default=600, type=int) + parser.add_argument("--train", action="store_true") + parser.add_argument("--test", action="store_true") + + args = parser.parse_args() + + args.local_rank = int(os.getenv("LOCAL_RANK", 0)) + args.world_size = int(os.getenv("WORLD_SIZE", 1)) + args.distributed = int(os.getenv("WORLD_SIZE", 1)) > 1 + + if args.distributed: + torch.cuda.set_device(device=args.local_rank) + torch.distributed.init_process_group(backend="nccl", init_method="env://") + + if args.local_rank == 0: + if not os.path.exists("weights"): + os.makedirs("weights") + + with open("utils/args.yaml", errors="ignore") as f: + params = yaml.safe_load(f) + + util.setup_seed() + util.setup_multi_processes() + + profile(args, params) + + if args.train: + train(args, params) + if args.test: + test(args, params) + + # Clean + if args.distributed: + torch.distributed.destroy_process_group() + torch.cuda.empty_cache() + + +if __name__ == "__main__": + main() diff --git a/main.sh b/main.sh new file mode 100755 index 0000000..acfdd6b --- /dev/null +++ b/main.sh @@ -0,0 +1,2 @@ +GPUS=$1 +python3 -m torch.distributed.run --nproc_per_node=$GPUS main.py ${@:2} \ No newline at end of file diff --git a/nets/nn.py b/nets/nn.py new file mode 100755 index 0000000..ffa5373 --- /dev/null +++ b/nets/nn.py @@ -0,0 +1,357 @@ +import math + +import torch + +from utils.util import make_anchors + + +def fuse_conv(conv, norm): + fused_conv = ( + torch.nn.Conv2d( + conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True, + ) + .requires_grad_(False) + .to(conv.weight.device) + ) + + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_norm = torch.diag(norm.weight.div(torch.sqrt(norm.eps + norm.running_var))) + fused_conv.weight.copy_(torch.mm(w_norm, w_conv).view(fused_conv.weight.size())) + + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_norm = norm.bias - norm.weight.mul(norm.running_mean).div(torch.sqrt(norm.running_var + norm.eps)) + fused_conv.bias.copy_(torch.mm(w_norm, b_conv.reshape(-1, 1)).reshape(-1) + b_norm) + + return fused_conv + + +class Conv(torch.nn.Module): + def __init__(self, in_ch, out_ch, activation, k=1, s=1, p=0, g=1): + super().__init__() + self.conv = torch.nn.Conv2d(in_ch, out_ch, k, s, p, groups=g, bias=False) + self.norm = torch.nn.BatchNorm2d(out_ch, eps=0.001, momentum=0.03) + self.relu = activation + + def forward(self, x): + return self.relu(self.norm(self.conv(x))) + + def fuse_forward(self, x): + return self.relu(self.conv(x)) + + +class Residual(torch.nn.Module): + def __init__(self, ch, e=0.5): + super().__init__() + self.conv1 = Conv(ch, int(ch * e), torch.nn.SiLU(), k=3, p=1) + self.conv2 = Conv(int(ch * e), ch, torch.nn.SiLU(), k=3, p=1) + + def forward(self, x): + return x + self.conv2(self.conv1(x)) + + +class CSPModule(torch.nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.conv1 = Conv(in_ch, out_ch // 2, torch.nn.SiLU()) + self.conv2 = Conv(in_ch, out_ch // 2, torch.nn.SiLU()) + self.conv3 = Conv(2 * (out_ch // 2), out_ch, torch.nn.SiLU()) + self.res_m = torch.nn.Sequential(Residual(out_ch // 2, e=1.0), Residual(out_ch // 2, e=1.0)) + + def forward(self, x): + y = self.res_m(self.conv1(x)) + return self.conv3(torch.cat((y, self.conv2(x)), dim=1)) + + +class CSP(torch.nn.Module): + def __init__(self, in_ch, out_ch, n, csp, r): + super().__init__() + self.conv1 = Conv(in_ch, 2 * (out_ch // r), torch.nn.SiLU()) + self.conv2 = Conv((2 + n) * (out_ch // r), out_ch, torch.nn.SiLU()) + + if not csp: + self.res_m = torch.nn.ModuleList(Residual(out_ch // r) for _ in range(n)) + else: + self.res_m = torch.nn.ModuleList(CSPModule(out_ch // r, out_ch // r) for _ in range(n)) + + def forward(self, x): + y = list(self.conv1(x).chunk(2, 1)) + y.extend(m(y[-1]) for m in self.res_m) + return self.conv2(torch.cat(y, dim=1)) + + +class SPP(torch.nn.Module): + def __init__(self, in_ch, out_ch, k=5): + super().__init__() + self.conv1 = Conv(in_ch, in_ch // 2, torch.nn.SiLU()) + self.conv2 = Conv(in_ch * 2, out_ch, torch.nn.SiLU()) + self.res_m = torch.nn.MaxPool2d(k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.conv1(x) + y1 = self.res_m(x) + y2 = self.res_m(y1) + return self.conv2(torch.cat(tensors=[x, y1, y2, self.res_m(y2)], dim=1)) + + +class Attention(torch.nn.Module): + def __init__(self, ch, num_head): + super().__init__() + self.num_head = num_head + self.dim_head = ch // num_head + self.dim_key = self.dim_head // 2 + self.scale = self.dim_key**-0.5 + + self.qkv = Conv(ch, ch + self.dim_key * num_head * 2, torch.nn.Identity()) + + self.conv1 = Conv(ch, ch, torch.nn.Identity(), k=3, p=1, g=ch) + self.conv2 = Conv(ch, ch, torch.nn.Identity()) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv(x) + qkv = qkv.view(b, self.num_head, self.dim_key * 2 + self.dim_head, h * w) + + q, k, v = qkv.split([self.dim_key, self.dim_key, self.dim_head], dim=2) + + attn = (q.transpose(-2, -1) @ k) * self.scale + attn = attn.softmax(dim=-1) + + x = (v @ attn.transpose(-2, -1)).view(b, c, h, w) + self.conv1(v.reshape(b, c, h, w)) + return self.conv2(x) + + +class PSABlock(torch.nn.Module): + def __init__(self, ch, num_head): + super().__init__() + self.conv1 = Attention(ch, num_head) + self.conv2 = torch.nn.Sequential(Conv(ch, ch * 2, torch.nn.SiLU()), Conv(ch * 2, ch, torch.nn.Identity())) + + def forward(self, x): + x = x + self.conv1(x) + return x + self.conv2(x) + + +class PSA(torch.nn.Module): + def __init__(self, ch, n): + super().__init__() + self.conv1 = Conv(ch, 2 * (ch // 2), torch.nn.SiLU()) + self.conv2 = Conv(2 * (ch // 2), ch, torch.nn.SiLU()) + self.res_m = torch.nn.Sequential(*(PSABlock(ch // 2, ch // 128) for _ in range(n))) + + def forward(self, x): + x, y = self.conv1(x).chunk(2, 1) + return self.conv2(torch.cat(tensors=(x, self.res_m(y)), dim=1)) + + +class DarkNet(torch.nn.Module): + def __init__(self, width, depth, csp): + super().__init__() + self.p1 = [] + self.p2 = [] + self.p3 = [] + self.p4 = [] + self.p5 = [] + + # p1/2 + self.p1.append(Conv(width[0], width[1], torch.nn.SiLU(), k=3, s=2, p=1)) + # p2/4 + self.p2.append(Conv(width[1], width[2], torch.nn.SiLU(), k=3, s=2, p=1)) + self.p2.append(CSP(width[2], width[3], depth[0], csp[0], r=4)) + # p3/8 + self.p3.append(Conv(width[3], width[3], torch.nn.SiLU(), k=3, s=2, p=1)) + self.p3.append(CSP(width[3], width[4], depth[1], csp[0], r=4)) + # p4/16 + self.p4.append(Conv(width[4], width[4], torch.nn.SiLU(), k=3, s=2, p=1)) + self.p4.append(CSP(width[4], width[4], depth[2], csp[1], r=2)) + # p5/32 + self.p5.append(Conv(width[4], width[5], torch.nn.SiLU(), k=3, s=2, p=1)) + self.p5.append(CSP(width[5], width[5], depth[3], csp[1], r=2)) + self.p5.append(SPP(width[5], width[5])) + self.p5.append(PSA(width[5], depth[4])) + + self.p1 = torch.nn.Sequential(*self.p1) + self.p2 = torch.nn.Sequential(*self.p2) + self.p3 = torch.nn.Sequential(*self.p3) + self.p4 = torch.nn.Sequential(*self.p4) + self.p5 = torch.nn.Sequential(*self.p5) + + def forward(self, x): + p1 = self.p1(x) + p2 = self.p2(p1) + p3 = self.p3(p2) + p4 = self.p4(p3) + p5 = self.p5(p4) + return p3, p4, p5 + + +class DarkFPN(torch.nn.Module): + def __init__(self, width, depth, csp): + super().__init__() + self.up = torch.nn.Upsample(scale_factor=2) + self.h1 = CSP(width[4] + width[5], width[4], depth[5], csp[0], r=2) + self.h2 = CSP(width[4] + width[4], width[3], depth[5], csp[0], r=2) + self.h3 = Conv(width[3], width[3], torch.nn.SiLU(), k=3, s=2, p=1) + self.h4 = CSP(width[3] + width[4], width[4], depth[5], csp[0], r=2) + self.h5 = Conv(width[4], width[4], torch.nn.SiLU(), k=3, s=2, p=1) + self.h6 = CSP(width[4] + width[5], width[5], depth[5], csp[1], r=2) + + def forward(self, x): + p3, p4, p5 = x + p4 = self.h1(torch.cat(tensors=[self.up(p5), p4], dim=1)) + p3 = self.h2(torch.cat(tensors=[self.up(p4), p3], dim=1)) + p4 = self.h4(torch.cat(tensors=[self.h3(p3), p4], dim=1)) + p5 = self.h6(torch.cat(tensors=[self.h5(p4), p5], dim=1)) + return p3, p4, p5 + + +class DFL(torch.nn.Module): + # Generalized Focal Loss + # https://ieeexplore.ieee.org/document/9792391 + def __init__(self, ch=16): + super().__init__() + self.ch = ch + self.conv = torch.nn.Conv2d(ch, out_channels=1, kernel_size=1, bias=False).requires_grad_(False) + x = torch.arange(ch, dtype=torch.float).view(1, ch, 1, 1) + self.conv.weight.data[:] = torch.nn.Parameter(x) + + def forward(self, x): + b, c, a = x.shape + x = x.view(b, 4, self.ch, a).transpose(2, 1) + return self.conv(x.softmax(1)).view(b, 4, a) + + +class Head(torch.nn.Module): + anchors = torch.empty(0) + strides = torch.empty(0) + + def __init__(self, nc=80, filters=()): + super().__init__() + self.ch = 16 # DFL channels + self.nc = nc # number of classes + self.nl = len(filters) # number of detection layers + self.no = nc + self.ch * 4 # number of outputs per anchor + self.stride = torch.zeros(self.nl) # strides computed during build + + box = max(64, filters[0] // 4) + cls = max(80, filters[0], self.nc) + + self.dfl = DFL(self.ch) + self.box = torch.nn.ModuleList( + torch.nn.Sequential( + Conv(x, box, torch.nn.SiLU(), k=3, p=1), + Conv(box, box, torch.nn.SiLU(), k=3, p=1), + torch.nn.Conv2d(box, out_channels=4 * self.ch, kernel_size=1), + ) + for x in filters + ) + self.cls = torch.nn.ModuleList( + torch.nn.Sequential( + Conv(x, x, torch.nn.SiLU(), k=3, p=1, g=x), + Conv(x, cls, torch.nn.SiLU()), + Conv(cls, cls, torch.nn.SiLU(), k=3, p=1, g=cls), + Conv(cls, cls, torch.nn.SiLU()), + torch.nn.Conv2d(cls, out_channels=self.nc, kernel_size=1), + ) + for x in filters + ) + + def forward(self, x): + for i, (box, cls) in enumerate(zip(self.box, self.cls)): + x[i] = torch.cat(tensors=(box(x[i]), cls(x[i])), dim=1) + if self.training: + return x + + self.anchors, self.strides = (i.transpose(0, 1) for i in make_anchors(x, self.stride)) + x = torch.cat([i.view(x[0].shape[0], self.no, -1) for i in x], dim=2) + box, cls = x.split(split_size=(4 * self.ch, self.nc), dim=1) + + a, b = self.dfl(box).chunk(2, 1) + a = self.anchors.unsqueeze(0) - a + b = self.anchors.unsqueeze(0) + b + box = torch.cat(tensors=((a + b) / 2, b - a), dim=1) + + return torch.cat(tensors=(box * self.strides, cls.sigmoid()), dim=1) + + def initialize_biases(self): + # Initialize biases + # WARNING: requires stride availability + for box, cls, s in zip(self.box, self.cls, self.stride): + # box + box[-1].bias.data[:] = 1.0 + # cls (.01 objects, 80 classes, 640 image) + cls[-1].bias.data[: self.nc] = math.log(5 / self.nc / (640 / s) ** 2) + + +class YOLO(torch.nn.Module): + def __init__(self, width, depth, csp, num_classes): + super().__init__() + self.net = DarkNet(width, depth, csp) + self.fpn = DarkFPN(width, depth, csp) + + img_dummy = torch.zeros(1, width[0], 256, 256) + self.head = Head(num_classes, (width[3], width[4], width[5])) + self.head.stride = torch.tensor([256 / x.shape[-2] for x in self.forward(img_dummy)]) + self.stride = self.head.stride + self.head.initialize_biases() + + def forward(self, x): + x = self.net(x) + x = self.fpn(x) + return self.head(list(x)) + + def fuse(self): + for m in self.modules(): + if type(m) is Conv and hasattr(m, "norm"): + m.conv = fuse_conv(m.conv, m.norm) + m.forward = m.fuse_forward + delattr(m, "norm") + return self + + +def yolo_v11_n(num_classes: int = 80): + csp = [False, True] + depth = [1, 1, 1, 1, 1, 1] + width = [3, 16, 32, 64, 128, 256] + return YOLO(width, depth, csp, num_classes) + + +def yolo_v11_t(num_classes: int = 80): + csp = [False, True] + depth = [1, 1, 1, 1, 1, 1] + width = [3, 24, 48, 96, 192, 384] + return YOLO(width, depth, csp, num_classes) + + +def yolo_v11_s(num_classes: int = 80): + csp = [False, True] + depth = [1, 1, 1, 1, 1, 1] + width = [3, 32, 64, 128, 256, 512] + return YOLO(width, depth, csp, num_classes) + + +def yolo_v11_m(num_classes: int = 80): + csp = [True, True] + depth = [1, 1, 1, 1, 1, 1] + width = [3, 64, 128, 256, 512, 512] + return YOLO(width, depth, csp, num_classes) + + +def yolo_v11_l(num_classes: int = 80): + csp = [True, True] + depth = [2, 2, 2, 2, 2, 2] + width = [3, 64, 128, 256, 512, 512] + return YOLO(width, depth, csp, num_classes) + + +def yolo_v11_x(num_classes: int = 80): + csp = [True, True] + depth = [2, 2, 2, 2, 2, 2] + width = [3, 96, 192, 384, 768, 768] + return YOLO(width, depth, csp, num_classes) diff --git a/utils/args.yaml b/utils/args.yaml new file mode 100755 index 0000000..a63baea --- /dev/null +++ b/utils/args.yaml @@ -0,0 +1,100 @@ +min_lr: 0.000100000000 # initial learning rate +max_lr: 0.010000000000 # maximum learning rate +momentum: 0.9370000000 # SGD momentum/Adam beta1 +weight_decay: 0.000500 # optimizer weight decay +warmup_epochs: 3.00000 # warmup epochs +box: 7.500000000000000 # box loss gain +cls: 0.500000000000000 # cls loss gain +dfl: 1.500000000000000 # dfl loss gain +hsv_h: 0.0150000000000 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7000000000000 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4000000000000 # image HSV-Value augmentation (fraction) +degrees: 0.00000000000 # image rotation (+/- deg) +translate: 0.100000000 # image translation (+/- fraction) +scale: 0.5000000000000 # image scale (+/- gain) +shear: 0.0000000000000 # image shear (+/- deg) +flip_ud: 0.00000000000 # image flip up-down (probability) +flip_lr: 0.50000000000 # image flip left-right (probability) +mosaic: 1.000000000000 # image mosaic (probability) +mix_up: 0.000000000000 # image mix-up (probability) +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush diff --git a/utils/dataset.py b/utils/dataset.py new file mode 100644 index 0000000..f6eaea2 --- /dev/null +++ b/utils/dataset.py @@ -0,0 +1,415 @@ +import math +import os +import random + +import cv2 +import numpy +import torch +from PIL import Image +from torch.utils import data + +FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp" + + +class Dataset(data.Dataset): + def __init__(self, filenames, input_size, params, augment): + self.params = params + self.mosaic = augment + self.augment = augment + self.input_size = input_size + + # Read labels + labels = self.load_label(filenames) + self.labels = list(labels.values()) + self.filenames = list(labels.keys()) # update + self.n = len(self.filenames) # number of samples + self.indices = range(self.n) + # Albumentations (optional, only used if package is installed) + self.albumentations = Albumentations() + + def __getitem__(self, index): + index = self.indices[index] + + if self.mosaic and random.random() < self.params["mosaic"]: + # Load MOSAIC + image, label = self.load_mosaic(index, self.params) + # MixUp augmentation + if random.random() < self.params["mix_up"]: + index = random.choice(self.indices) + mix_image1, mix_label1 = image, label + mix_image2, mix_label2 = self.load_mosaic(index, self.params) + + image, label = mix_up(mix_image1, mix_label1, mix_image2, mix_label2) + else: + # Load image + image, shape = self.load_image(index) + h, w = image.shape[:2] + + # Resize + image, ratio, pad = resize(image, self.input_size, self.augment) + + label = self.labels[index].copy() + if label.size: + label[:, 1:] = wh2xy(label[:, 1:], ratio[0] * w, ratio[1] * h, pad[0], pad[1]) + if self.augment: + image, label = random_perspective(image, label, self.params) + + nl = len(label) # number of labels + h, w = image.shape[:2] + cls = label[:, 0:1] + box = label[:, 1:5] + box = xy2wh(box, w, h) + + if self.augment: + # Albumentations + image, box, cls = self.albumentations(image, box, cls) + nl = len(box) # update after albumentations + # HSV color-space + augment_hsv(image, self.params) + # Flip up-down + if random.random() < self.params["flip_ud"]: + image = numpy.flipud(image) + if nl: + box[:, 1] = 1 - box[:, 1] + # Flip left-right + if random.random() < self.params["flip_lr"]: + image = numpy.fliplr(image) + if nl: + box[:, 0] = 1 - box[:, 0] + + # XXX: when nl=0, torch.from_numpy(box) will error + if nl: + target_cls = torch.from_numpy(cls).view(-1, 1).float() # always (N,1) + target_box = torch.from_numpy(box).reshape(-1, 4).float() # always (N,4) + else: + target_cls = torch.zeros((0, 1), dtype=torch.float32) + target_box = torch.zeros((0, 4), dtype=torch.float32) + # target_cls = torch.zeros((nl, 1)) + # target_box = torch.zeros((nl, 4)) + # if nl: + # target_cls = torch.from_numpy(cls) + # target_box = torch.from_numpy(box) + + # Convert HWC to CHW, BGR to RGB + sample = image.transpose((2, 0, 1))[::-1] + sample = numpy.ascontiguousarray(sample) + + # return torch.from_numpy(sample), target_cls, target_box, torch.zeros(nl) + return torch.from_numpy(sample), target_cls, target_box, torch.zeros((nl, 1), dtype=torch.long) + + def __len__(self): + return len(self.filenames) + + def load_image(self, i): + image = cv2.imread(self.filenames[i]) + if image is None: + raise FileNotFoundError(f"Image Not Found {self.filenames[i]}") + h, w = image.shape[:2] + r = self.input_size / max(h, w) + if r != 1: + image = cv2.resize( + image, dsize=(int(w * r), int(h * r)), interpolation=resample() if self.augment else cv2.INTER_LINEAR + ) + return image, (h, w) + + def load_mosaic(self, index, params): + label4 = [] + border = [-self.input_size // 2, -self.input_size // 2] + image4 = numpy.full((self.input_size * 2, self.input_size * 2, 3), 0, dtype=numpy.uint8) + y1a, y2a, x1a, x2a, y1b, y2b, x1b, x2b = (None, None, None, None, None, None, None, None) + + xc = int(random.uniform(-border[0], 2 * self.input_size + border[1])) + yc = int(random.uniform(-border[0], 2 * self.input_size + border[1])) + + indices = [index] + random.choices(self.indices, k=3) + random.shuffle(indices) + + for i, index in enumerate(indices): + # Load image + image, _ = self.load_image(index) + shape = image.shape + if i == 0: # top left + x1a = max(xc - shape[1], 0) + y1a = max(yc - shape[0], 0) + x2a = xc + y2a = yc + x1b = shape[1] - (x2a - x1a) + y1b = shape[0] - (y2a - y1a) + x2b = shape[1] + y2b = shape[0] + if i == 1: # top right + x1a = xc + y1a = max(yc - shape[0], 0) + x2a = min(xc + shape[1], self.input_size * 2) + y2a = yc + x1b = 0 + y1b = shape[0] - (y2a - y1a) + x2b = min(shape[1], x2a - x1a) + y2b = shape[0] + if i == 2: # bottom left + x1a = max(xc - shape[1], 0) + y1a = yc + x2a = xc + y2a = min(self.input_size * 2, yc + shape[0]) + x1b = shape[1] - (x2a - x1a) + y1b = 0 + x2b = shape[1] + y2b = min(y2a - y1a, shape[0]) + if i == 3: # bottom right + x1a = xc + y1a = yc + x2a = min(xc + shape[1], self.input_size * 2) + y2a = min(self.input_size * 2, yc + shape[0]) + x1b = 0 + y1b = 0 + x2b = min(shape[1], x2a - x1a) + y2b = min(y2a - y1a, shape[0]) + + pad_w = x1a - x1b + pad_h = y1a - y1b + image4[y1a:y2a, x1a:x2a] = image[y1b:y2b, x1b:x2b] + + # Labels + label = self.labels[index].copy() + if len(label): + label[:, 1:] = wh2xy(label[:, 1:], shape[1], shape[0], pad_w, pad_h) + label4.append(label) + + # Concat/clip labels + label4 = numpy.concatenate(label4, 0) + for x in label4[:, 1:]: + numpy.clip(x, 0, 2 * self.input_size, out=x) + + # Augment + image4, label4 = random_perspective(image4, label4, params, border) + + return image4, label4 + + @staticmethod + def collate_fn(batch): + samples, cls, box, indices = zip(*batch) + + cls = torch.cat(cls, dim=0) + box = torch.cat(box, dim=0) + + new_indices = list(indices) + for i in range(len(indices)): + new_indices[i] += i + indices = torch.cat(new_indices, dim=0) + + targets = {"cls": cls, "box": box, "idx": indices} + return torch.stack(samples, dim=0), targets + + @staticmethod + def load_label(filenames): + path = f"{os.path.dirname(filenames[0])}.cache" + if os.path.exists(path): + return torch.load(path, weights_only=False) + x = {} + for filename in filenames: + try: + # verify images + with open(filename, "rb") as f: + image = Image.open(f) + image.verify() # PIL verify + shape = image.size # image size + assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" + assert image.format.lower() in FORMATS, f"invalid image format {image.format}" + + # verify labels + a = f"{os.sep}images{os.sep}" + b = f"{os.sep}labels{os.sep}" + if os.path.isfile(b.join(filename.rsplit(a, 1)).rsplit(".", 1)[0] + ".txt"): + with open(b.join(filename.rsplit(a, 1)).rsplit(".", 1)[0] + ".txt") as f: + label = [x.split() for x in f.read().strip().splitlines() if len(x)] + label = numpy.array(label, dtype=numpy.float32) + nl = len(label) + if nl: + assert (label >= 0).all() + assert label.shape[1] == 5 + assert (label[:, 1:] <= 1).all() + _, i = numpy.unique(label, axis=0, return_index=True) + if len(i) < nl: # duplicate row check + label = label[i] # remove duplicates + else: + label = numpy.zeros((0, 5), dtype=numpy.float32) + else: + label = numpy.zeros((0, 5), dtype=numpy.float32) + except FileNotFoundError: + label = numpy.zeros((0, 5), dtype=numpy.float32) + except AssertionError: + continue + x[filename] = label + torch.save(x, path) + return x + + +def wh2xy(x, w=640, h=640, pad_w=0, pad_h=0): + # Convert nx4 boxes + # from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = numpy.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + pad_w # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + pad_h # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + pad_w # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + pad_h # bottom right y + return y + + +def xy2wh(x, w, h): + # warning: inplace clip + x[:, [0, 2]] = x[:, [0, 2]].clip(0, w - 1e-3) # x1, x2 + x[:, [1, 3]] = x[:, [1, 3]].clip(0, h - 1e-3) # y1, y2 + + # Convert nx4 boxes + # from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right + y = numpy.copy(x) + y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w # x center + y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h # y center + y[:, 2] = (x[:, 2] - x[:, 0]) / w # width + y[:, 3] = (x[:, 3] - x[:, 1]) / h # height + return y + + +def resample(): + choices = (cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4) + return random.choice(seq=choices) + + +def augment_hsv(image, params): + # HSV color-space augmentation + h = params["hsv_h"] + s = params["hsv_s"] + v = params["hsv_v"] + + r = numpy.random.uniform(-1, 1, 3) * [h, s, v] + 1 + h, s, v = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV)) + + x = numpy.arange(0, 256, dtype=r.dtype) + lut_h = ((x * r[0]) % 180).astype("uint8") + lut_s = numpy.clip(x * r[1], 0, 255).astype("uint8") + lut_v = numpy.clip(x * r[2], 0, 255).astype("uint8") + + hsv = cv2.merge((cv2.LUT(h, lut_h), cv2.LUT(s, lut_s), cv2.LUT(v, lut_v))) + cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR, dst=image) # no return needed + + +def resize(image, input_size, augment): + # Resize and pad image while meeting stride-multiple constraints + shape = image.shape[:2] # current shape [height, width] + + # Scale ratio (new / old) + r = min(input_size / shape[0], input_size / shape[1]) + if not augment: # only scale down, do not scale up (for better val mAP) + r = min(r, 1.0) + + # Compute padding + pad = int(round(shape[1] * r)), int(round(shape[0] * r)) + w = (input_size - pad[0]) / 2 + h = (input_size - pad[1]) / 2 + + if shape[::-1] != pad: # resize + image = cv2.resize(image, dsize=pad, interpolation=resample() if augment else cv2.INTER_LINEAR) + top, bottom = int(round(h - 0.1)), int(round(h + 0.1)) + left, right = int(round(w - 0.1)), int(round(w + 0.1)) + image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT) # add border + return image, (r, r), (w, h) + + +def candidates(box1, box2): + # box1(4,n), box2(4,n) + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + aspect_ratio = numpy.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio + return (w2 > 2) & (h2 > 2) & (w2 * h2 / (w1 * h1 + 1e-16) > 0.1) & (aspect_ratio < 100) + + +def random_perspective(image, label, params, border=(0, 0)): + h = image.shape[0] + border[0] * 2 + w = image.shape[1] + border[1] * 2 + + # Center + center = numpy.eye(3) + center[0, 2] = -image.shape[1] / 2 # x translation (pixels) + center[1, 2] = -image.shape[0] / 2 # y translation (pixels) + + # Perspective + perspective = numpy.eye(3) + + # Rotation and Scale + rotate = numpy.eye(3) + a = random.uniform(-params["degrees"], params["degrees"]) + s = random.uniform(1 - params["scale"], 1 + params["scale"]) + rotate[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + shear = numpy.eye(3) + shear[0, 1] = math.tan(random.uniform(-params["shear"], params["shear"]) * math.pi / 180) + shear[1, 0] = math.tan(random.uniform(-params["shear"], params["shear"]) * math.pi / 180) + + # Translation + translate = numpy.eye(3) + translate[0, 2] = random.uniform(0.5 - params["translate"], 0.5 + params["translate"]) * w + translate[1, 2] = random.uniform(0.5 - params["translate"], 0.5 + params["translate"]) * h + + # Combined rotation matrix, order of operations (right to left) is IMPORTANT + matrix = translate @ shear @ rotate @ perspective @ center + if (border[0] != 0) or (border[1] != 0) or (matrix != numpy.eye(3)).any(): # image changed + image = cv2.warpAffine(image, matrix[:2], dsize=(w, h), borderValue=(0, 0, 0)) + + # Transform label coordinates + n = len(label) + if n: + xy = numpy.ones((n * 4, 3)) + xy[:, :2] = label[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ matrix.T # transform + xy = xy[:, :2].reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + box = numpy.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + box[:, [0, 2]] = box[:, [0, 2]].clip(0, w) + box[:, [1, 3]] = box[:, [1, 3]].clip(0, h) + # filter candidates + indices = candidates(box1=label[:, 1:5].T * s, box2=box.T) + + label = label[indices] + label[:, 1:5] = box[indices] + + return image, label + + +def mix_up(image1, label1, image2, label2): + # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + alpha = numpy.random.beta(a=32.0, b=32.0) # mix-up ratio, alpha=beta=32.0 + image = (image1 * alpha + image2 * (1 - alpha)).astype(numpy.uint8) + label = numpy.concatenate((label1, label2), 0) + return image, label + + +class Albumentations: + def __init__(self): + self.transform = None + try: + import albumentations + + transforms = [ + albumentations.Blur(p=0.01), + albumentations.CLAHE(p=0.01), + albumentations.ToGray(p=0.01), + albumentations.MedianBlur(p=0.01), + ] + self.transform = albumentations.Compose(transforms, albumentations.BboxParams("yolo", ["class_labels"])) + + except ImportError: # package not installed, skip + pass + + def __call__(self, image, box, cls): + if self.transform: + x = self.transform(image=image, bboxes=box, class_labels=cls) + image = x["image"] + box = numpy.array(x["bboxes"]) + cls = numpy.array(x["class_labels"]) + return image, box, cls diff --git a/utils/util.py b/utils/util.py new file mode 100644 index 0000000..3449e05 --- /dev/null +++ b/utils/util.py @@ -0,0 +1,777 @@ +import copy +import random +from time import time + +import math +import numpy +import torch +import torchvision +from torch.nn.functional import cross_entropy + + +def setup_seed(): + """ + Setup random seed. + """ + random.seed(0) + numpy.random.seed(0) + torch.manual_seed(0) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + + +def setup_multi_processes(): + """ + Setup multi-processing environment variables. + """ + import cv2 + from os import environ + from platform import system + + # set multiprocess start method as `fork` to speed up the training + if system() != "Windows": + torch.multiprocessing.set_start_method("fork", force=True) + + # disable opencv multithreading to avoid system being overloaded + cv2.setNumThreads(0) + + # setup OMP threads + if "OMP_NUM_THREADS" not in environ: + environ["OMP_NUM_THREADS"] = "1" + + # setup MKL threads + if "MKL_NUM_THREADS" not in environ: + environ["MKL_NUM_THREADS"] = "1" + + +def export_onnx(args): + import onnx # noqa + + inputs = ["images"] + outputs = ["outputs"] + dynamic = {"outputs": {0: "batch", 1: "anchors"}} + + m = torch.load("./weights/best.pt", weights_only=False)["model"].float() + x = torch.zeros((1, 3, args.input_size, args.input_size)) + + torch.onnx.export( + m.cpu(), + (x.cpu(),), + f="./weights/best.onnx", + verbose=False, + opset_version=12, + # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + do_constant_folding=True, + input_names=inputs, + output_names=outputs, + dynamic_axes=dynamic or None, + ) + + # Checks + model_onnx = onnx.load("./weights/best.onnx") # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + onnx.save(model_onnx, "./weights/best.onnx") + # Inference example + # https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/autobackend.py + + +def wh2xy(x): + y = x.clone() if isinstance(x, torch.Tensor) else numpy.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def make_anchors(x, strides, offset=0.5): + assert x is not None + anchor_tensor, stride_tensor = [], [] + dtype, device = x[0].dtype, x[0].device + for i, stride in enumerate(strides): + _, _, h, w = x[i].shape + sx = torch.arange(end=w, device=device, dtype=dtype) + offset # shift x + sy = torch.arange(end=h, device=device, dtype=dtype) + offset # shift y + sy, sx = torch.meshgrid(sy, sx) + anchor_tensor.append(torch.stack((sx, sy), -1).view(-1, 2)) + stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device)) + return torch.cat(anchor_tensor), torch.cat(stride_tensor) + + +def compute_metric(output, target, iou_v): + # intersection(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2) = target[:, 1:].unsqueeze(1).chunk(2, 2) + (b1, b2) = output[:, :4].unsqueeze(0).chunk(2, 2) + intersection = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + # IoU = intersection / (area1 + area2 - intersection) + iou = intersection / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - intersection + 1e-7) + + correct = numpy.zeros((output.shape[0], iou_v.shape[0])) + correct = correct.astype(bool) + for i in range(len(iou_v)): + # IoU > threshold and classes match + x = torch.where((iou >= iou_v[i]) & (target[:, 0:1] == output[:, 5])) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[numpy.unique(matches[:, 1], return_index=True)[1]] + matches = matches[numpy.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=output.device) + + +def non_max_suppression(outputs, confidence_threshold=0.001, iou_threshold=0.65): + max_wh = 7680 + max_det = 300 + max_nms = 30000 + + bs = outputs.shape[0] # batch size + nc = outputs.shape[1] - 4 # number of classes + xc = outputs[:, 4 : 4 + nc].amax(1) > confidence_threshold # candidates + + # Settings + start = time() + limit = 0.5 + 0.05 * bs # seconds to quit after + output = [torch.zeros((0, 6), device=outputs.device)] * bs + for index, x in enumerate(outputs): # image index, image inference + x = x.transpose(0, -1)[xc[index]] # confidence + + # If none remain process next image + if not x.shape[0]: + continue + + # matrix nx6 (box, confidence, cls) + box, cls = x.split((4, nc), 1) + box = wh2xy(box) # (cx, cy, w, h) to (x1, y1, x2, y2) + if nc > 1: + i, j = (cls > confidence_threshold).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float()), 1) + else: # best class only + conf, j = cls.max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > confidence_threshold] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes + + # Batched NMS + c = x[:, 5:6] * max_wh # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes, scores + indices = torchvision.ops.nms(boxes, scores, iou_threshold) # NMS + indices = indices[:max_det] # limit detections + + output[index] = x[indices] + if (time() - start) > limit: + break # time limit exceeded + + return output + + +def smooth(y, f=0.1): + # Box filter of fraction f + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = numpy.ones(nf // 2) # ones padding + yp = numpy.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return numpy.convolve(yp, numpy.ones(nf) / nf, mode="valid") # y-smoothed + + +def plot_pr_curve(px, py, ap, names, save_dir): + from matplotlib import pyplot + + fig, ax = pyplot.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = numpy.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean()) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title("Precision-Recall Curve") + fig.savefig(save_dir, dpi=250) + pyplot.close(fig) + + +def plot_curve(px, py, names, save_dir, x_label="Confidence", y_label="Metric"): + from matplotlib import pyplot + + figure, ax = pyplot.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) + + y = smooth(py.mean(0), f=0.05) + ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.3f} at {px[y.argmax()]:.3f}") + ax.set_xlabel(x_label) + ax.set_ylabel(y_label) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title(f"{y_label}-Confidence Curve") + figure.savefig(save_dir, dpi=250) + pyplot.close(figure) + + +def compute_ap(tp, conf, output, target, plot=False, names=(), eps=1e-16): + """ + Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Object-ness value from 0-1 (nparray). + output: Predicted object classes (nparray). + target: True object classes (nparray). + # Returns + The average precision + """ + # Sort by object-ness + i = numpy.argsort(-conf) + tp, conf, output = tp[i], conf[i], output[i] + + # Find unique classes + unique_classes, nt = numpy.unique(target, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + p = numpy.zeros((nc, 1000)) + r = numpy.zeros((nc, 1000)) + ap = numpy.zeros((nc, tp.shape[1])) + px, py = numpy.linspace(start=0, stop=1, num=1000), [] # for plotting + for ci, c in enumerate(unique_classes): + i = output == c + nl = nt[ci] # number of labels + no = i.sum() # number of outputs + if no == 0 or nl == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (nl + eps) # recall curve + # negative x, xp because xp decreases + r[ci] = numpy.interp(-px, -conf[i], recall[:, 0], left=0) + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = numpy.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + m_rec = numpy.concatenate(([0.0], recall[:, j], [1.0])) + m_pre = numpy.concatenate(([1.0], precision[:, j], [0.0])) + + # Compute the precision envelope + m_pre = numpy.flip(numpy.maximum.accumulate(numpy.flip(m_pre))) + + # Integrate area under curve + x = numpy.linspace(start=0, stop=1, num=101) # 101-point interp (COCO) + ap[ci, j] = numpy.trapz(numpy.interp(x, m_rec, m_pre), x) # integrate + if plot and j == 0: + py.append(numpy.interp(px, m_rec, m_pre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + eps) + if plot: + names = dict(enumerate(names)) # to dict + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + plot_pr_curve(px, py, ap, names, save_dir="./weights/PR_curve.png") + plot_curve(px, f1, names, save_dir="./weights/F1_curve.png", y_label="F1") + plot_curve(px, p, names, save_dir="./weights/P_curve.png", y_label="Precision") + plot_curve(px, r, names, save_dir="./weights/R_curve.png", y_label="Recall") + i = smooth(f1.mean(0), 0.1).argmax() # max F1 index + p, r, f1 = p[:, i], r[:, i], f1[:, i] + tp = (r * nt).round() # true positives + fp = (tp / (p + eps) - tp).round() # false positives + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 + m_pre, m_rec = p.mean(), r.mean() + map50, mean_ap = ap50.mean(), ap.mean() + return tp, fp, m_pre, m_rec, map50, mean_ap + + +def compute_iou(box1, box2, eps=1e-7): + # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( + b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) + ).clamp(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + c2 = cw**2 + ch**2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 + # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + + +def strip_optimizer(filename): + x = torch.load(filename, map_location="cpu", weights_only=False) + x["model"].half() # to FP16 + for p in x["model"].parameters(): + p.requires_grad = False + torch.save(x, f=filename) + + +def clip_gradients(model, max_norm=10.0): + parameters = model.parameters() + torch.nn.utils.clip_grad_norm_(parameters, max_norm=max_norm) + + +def load_weight(model, ckpt): + dst = model.state_dict() + src = torch.load(ckpt, weights_only=False)["model"].float().cpu() + + ckpt = {} + for k, v in src.state_dict().items(): + if k in dst and v.shape == dst[k].shape: + ckpt[k] = v + + model.load_state_dict(state_dict=ckpt, strict=False) + return model + + +def set_params(model, decay): + p1 = [] + p2 = [] + norm = tuple(v for k, v in torch.nn.__dict__.items() if "Norm" in k) + for m in model.modules(): + for n, p in m.named_parameters(recurse=0): + if not p.requires_grad: + continue + if n == "bias": # bias (no decay) + p1.append(p) + elif n == "weight" and isinstance(m, norm): # norm-weight (no decay) + p1.append(p) + else: + p2.append(p) # weight (with decay) + return [{"params": p1, "weight_decay": 0.00}, {"params": p2, "weight_decay": decay}] + + +def plot_lr(args, optimizer, scheduler, num_steps): + from matplotlib import pyplot + + optimizer = copy.copy(optimizer) + scheduler = copy.copy(scheduler) + + y = [] + for epoch in range(args.epochs): + for i in range(num_steps): + step = i + num_steps * epoch + scheduler.step(step, optimizer) + y.append(optimizer.param_groups[0]["lr"]) + pyplot.plot(y, ".-", label="LR") + pyplot.xlabel("step") + pyplot.ylabel("LR") + pyplot.grid() + pyplot.xlim(0, args.epochs * num_steps) + pyplot.ylim(0) + pyplot.savefig("./weights/lr.png", dpi=200) + pyplot.close() + + +class CosineLR: + def __init__(self, args, params, num_steps): + max_lr = params["max_lr"] + min_lr = params["min_lr"] + + warmup_steps = int(max(params["warmup_epochs"] * num_steps, 100)) + decay_steps = int(args.epochs * num_steps - warmup_steps) + + warmup_lr = numpy.linspace(min_lr, max_lr, int(warmup_steps)) + + decay_lr = [] + for step in range(1, decay_steps + 1): + alpha = math.cos(math.pi * step / decay_steps) + decay_lr.append(min_lr + 0.5 * (max_lr - min_lr) * (1 + alpha)) + + self.total_lr = numpy.concatenate((warmup_lr, decay_lr)) + + def step(self, step, optimizer): + for param_group in optimizer.param_groups: + param_group["lr"] = self.total_lr[step] + + +class LinearLR: + def __init__(self, args, params, num_steps): + max_lr = params["max_lr"] + min_lr = params["min_lr"] + + warmup_steps = int(max(params["warmup_epochs"] * num_steps, 100)) + decay_steps = int(args.epochs * num_steps - warmup_steps) + + warmup_lr = numpy.linspace(min_lr, max_lr, int(warmup_steps), endpoint=False) + decay_lr = numpy.linspace(max_lr, min_lr, decay_steps) + + self.total_lr = numpy.concatenate((warmup_lr, decay_lr)) + + def step(self, step, optimizer): + for param_group in optimizer.param_groups: + param_group["lr"] = self.total_lr[step] + + +class EMA: + """ + Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + # Create EMA + self.ema = copy.deepcopy(model).eval() # FP32 EMA + self.updates = updates # number of EMA updates + # decay exponential ramp (to help early epochs) + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + if hasattr(model, "module"): + model = model.module + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = model.state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1 - d) * msd[k].detach() + + +class AverageMeter: + def __init__(self): + self.num = 0 + self.sum = 0 + self.avg = 0 + + def update(self, v, n): + if not math.isnan(float(v)): + self.num = self.num + n + self.sum = self.sum + v * n + self.avg = self.sum / self.num + + +class Assigner(torch.nn.Module): + def __init__(self, nc=80, top_k=13, alpha=1.0, beta=6.0, eps=1e-9): + super().__init__() + self.top_k = top_k + self.nc = nc + self.alpha = alpha + self.beta = beta + self.eps = eps + + @torch.no_grad() + def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt): + batch_size = pd_scores.size(0) + num_max_boxes = gt_bboxes.size(1) + + if num_max_boxes == 0: + device = gt_bboxes.device + return ( + torch.zeros_like(pd_bboxes).to(device), + torch.zeros_like(pd_scores).to(device), + torch.zeros_like(pd_scores[..., 0]).to(device), + ) + + num_anchors = anc_points.shape[0] + shape = gt_bboxes.shape + lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) + mask_in_gts = torch.cat((anc_points[None] - lt, rb - anc_points[None]), dim=2) + mask_in_gts = mask_in_gts.view(shape[0], shape[1], num_anchors, -1).amin(3).gt_(self.eps) + na = pd_bboxes.shape[-2] + gt_mask = (mask_in_gts * mask_gt).bool() # b, max_num_obj, h*w + overlaps = torch.zeros([batch_size, num_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device) + bbox_scores = torch.zeros([batch_size, num_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device) + + ind = torch.zeros([2, batch_size, num_max_boxes], dtype=torch.long) # 2, b, max_num_obj + ind[0] = torch.arange(end=batch_size).view(-1, 1).expand(-1, num_max_boxes) # b, max_num_obj + ind[1] = gt_labels.squeeze(-1) # b, max_num_obj + bbox_scores[gt_mask] = pd_scores[ind[0], :, ind[1]][gt_mask] # b, max_num_obj, h*w + + pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, num_max_boxes, -1, -1)[gt_mask] + gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[gt_mask] + overlaps[gt_mask] = compute_iou(gt_boxes, pd_boxes).squeeze(-1).clamp_(0) + + align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta) + + top_k_mask = mask_gt.expand(-1, -1, self.top_k).bool() + top_k_metrics, top_k_indices = torch.topk(align_metric, self.top_k, dim=-1, largest=True) + if top_k_mask is None: + top_k_mask = (top_k_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(top_k_indices) + top_k_indices.masked_fill_(~top_k_mask, 0) + + mask_top_k = torch.zeros(align_metric.shape, dtype=torch.int8, device=top_k_indices.device) + ones = torch.ones_like(top_k_indices[:, :, :1], dtype=torch.int8, device=top_k_indices.device) + for k in range(self.top_k): + mask_top_k.scatter_add_(-1, top_k_indices[:, :, k : k + 1], ones) + mask_top_k.masked_fill_(mask_top_k > 1, 0) + mask_top_k = mask_top_k.to(align_metric.dtype) + mask_pos = mask_top_k * mask_in_gts * mask_gt + + fg_mask = mask_pos.sum(-2) + if fg_mask.max() > 1: + mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, num_max_boxes, -1) + max_overlaps_idx = overlaps.argmax(1) + + is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device) + is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1) + + mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float() + fg_mask = mask_pos.sum(-2) + target_gt_idx = mask_pos.argmax(-2) + + # Assigned target + index = torch.arange(end=batch_size, dtype=torch.int64, device=gt_labels.device)[..., None] + target_index = target_gt_idx + index * num_max_boxes + target_labels = gt_labels.long().flatten()[target_index] + + target_bboxes = gt_bboxes.view(-1, gt_bboxes.shape[-1])[target_index] + + # Assigned target scores + target_labels.clamp_(0) + + target_scores = torch.zeros( + (target_labels.shape[0], target_labels.shape[1], self.nc), dtype=torch.int64, device=target_labels.device + ) + target_scores.scatter_(2, target_labels.unsqueeze(-1), 1) + + fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.nc) + target_scores = torch.where(fg_scores_mask > 0, target_scores, 0) + + # Normalize + align_metric *= mask_pos + pos_align_metrics = align_metric.amax(dim=-1, keepdim=True) + pos_overlaps = (overlaps * mask_pos).amax(dim=-1, keepdim=True) + norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1) + target_scores = target_scores * norm_align_metric + + return target_bboxes, target_scores, fg_mask.bool() + + +class QFL(torch.nn.Module): + def __init__(self, beta=2.0): + super().__init__() + self.beta = beta + self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none") + + def forward(self, outputs, targets): + bce_loss = self.bce_loss(outputs, targets) + return torch.pow(torch.abs(targets - outputs.sigmoid()), self.beta) * bce_loss + + +class VFL(torch.nn.Module): + def __init__(self, alpha=0.75, gamma=2.00, iou_weighted=True): + super().__init__() + assert alpha >= 0.0 + self.alpha = alpha + self.gamma = gamma + self.iou_weighted = iou_weighted + self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none") + + def forward(self, outputs, targets): + assert outputs.size() == targets.size() + targets = targets.type_as(outputs) + + if self.iou_weighted: + focal_weight = ( + targets * (targets > 0.0).float() + + self.alpha * (outputs.sigmoid() - targets).abs().pow(self.gamma) * (targets <= 0.0).float() + ) + + else: + focal_weight = (targets > 0.0).float() + self.alpha * (outputs.sigmoid() - targets).abs().pow( + self.gamma + ) * (targets <= 0.0).float() + + return self.bce_loss(outputs, targets) * focal_weight + + +class FocalLoss(torch.nn.Module): + def __init__(self, alpha=0.25, gamma=1.5): + super().__init__() + self.alpha = alpha + self.gamma = gamma + self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none") + + def forward(self, outputs, targets): + loss = self.bce_loss(outputs, targets) + + if self.alpha > 0: + alpha_factor = targets * self.alpha + (1 - targets) * (1 - self.alpha) + loss *= alpha_factor + + if self.gamma > 0: + outputs_sigmoid = outputs.sigmoid() + p_t = targets * outputs_sigmoid + (1 - targets) * (1 - outputs_sigmoid) + gamma_factor = (1.0 - p_t) ** self.gamma + loss *= gamma_factor + + return loss + + +class BoxLoss(torch.nn.Module): + def __init__(self, dfl_ch): + super().__init__() + self.dfl_ch = dfl_ch + + def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask): + # IoU loss + weight = torch.masked_select(target_scores.sum(-1), fg_mask).unsqueeze(-1) + iou = compute_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask]) + loss_box = ((1.0 - iou) * weight).sum() / target_scores_sum + + # DFL loss + a, b = target_bboxes.chunk(2, -1) + target = torch.cat((anchor_points - a, b - anchor_points), -1) + target = target.clamp(0, self.dfl_ch - 0.01) + loss_dfl = self.df_loss(pred_dist[fg_mask].view(-1, self.dfl_ch + 1), target[fg_mask]) + loss_dfl = (loss_dfl * weight).sum() / target_scores_sum + + return loss_box, loss_dfl + + @staticmethod + def df_loss(pred_dist, target): + # Distribution Focal Loss (DFL) + # https://ieeexplore.ieee.org/document/9792391 + tl = target.long() # target left + tr = tl + 1 # target right + wl = tr - target # weight left + wr = 1 - wl # weight right + left_loss = cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape) + right_loss = cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape) + return (left_loss * wl + right_loss * wr).mean(-1, keepdim=True) + + +class ComputeLoss: + def __init__(self, model, params): + if hasattr(model, "module"): + model = model.module + + device = next(model.parameters()).device + + m = model.head # Head() module + + self.params = params + self.stride = m.stride + self.nc = m.nc + self.no = m.no + self.reg_max = m.ch + self.device = device + + self.box_loss = BoxLoss(m.ch - 1).to(device) + self.cls_loss = torch.nn.BCEWithLogitsLoss(reduction="none") + self.assigner = Assigner(nc=self.nc, top_k=10, alpha=0.5, beta=6.0) + + self.project = torch.arange(m.ch, dtype=torch.float, device=device) + + def box_decode(self, anchor_points, pred_dist): + b, a, c = pred_dist.shape + pred_dist = pred_dist.view(b, a, 4, c // 4) + pred_dist = pred_dist.softmax(3) + pred_dist = pred_dist.matmul(self.project.type(pred_dist.dtype)) + lt, rb = pred_dist.chunk(2, -1) + x1y1 = anchor_points - lt + x2y2 = anchor_points + rb + return torch.cat(tensors=(x1y1, x2y2), dim=-1) + + def __call__(self, outputs, targets): + x = torch.cat([i.view(outputs[0].shape[0], self.no, -1) for i in outputs], dim=2) + pred_distri, pred_scores = x.split(split_size=(self.reg_max * 4, self.nc), dim=1) + + pred_scores = pred_scores.permute(0, 2, 1).contiguous() + pred_distri = pred_distri.permute(0, 2, 1).contiguous() + + data_type = pred_scores.dtype + batch_size = pred_scores.shape[0] + input_size = torch.tensor(outputs[0].shape[2:], device=self.device, dtype=data_type) * self.stride[0] + anchor_points, stride_tensor = make_anchors(outputs, self.stride, offset=0.5) + + idx = targets["idx"].view(-1, 1) + cls = targets["cls"].view(-1, 1) + box = targets["box"] + + targets = torch.cat((idx, cls, box), dim=1).to(self.device) + if targets.shape[0] == 0: + gt = torch.zeros(batch_size, 0, 5, device=self.device) + else: + i = targets[:, 0] + _, counts = i.unique(return_counts=True) + counts = counts.to(dtype=torch.int32) + gt = torch.zeros(batch_size, counts.max(), 5, device=self.device) + for j in range(batch_size): + matches = i == j + n = matches.sum() + if n: + gt[j, :n] = targets[matches, 1:] + x = gt[..., 1:5].mul_(input_size[[1, 0, 1, 0]]) + y = torch.empty_like(x) + dw = x[..., 2] / 2 # half-width + dh = x[..., 3] / 2 # half-height + y[..., 0] = x[..., 0] - dw # top left x + y[..., 1] = x[..., 1] - dh # top left y + y[..., 2] = x[..., 0] + dw # bottom right x + y[..., 3] = x[..., 1] + dh # bottom right y + gt[..., 1:5] = y + gt_labels, gt_bboxes = gt.split((1, 4), 2) + mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) + + pred_bboxes = self.box_decode(anchor_points, pred_distri) + assigned_targets = self.assigner( + pred_scores.detach().sigmoid(), + (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype), + anchor_points * stride_tensor, + gt_labels, + gt_bboxes, + mask_gt, + ) + target_bboxes, target_scores, fg_mask = assigned_targets + + target_scores_sum = max(target_scores.sum(), 1) + + loss_cls = self.cls_loss(pred_scores, target_scores.to(data_type)).sum() / target_scores_sum # BCE + + # Box loss + loss_box = torch.zeros(1, device=self.device) + loss_dfl = torch.zeros(1, device=self.device) + if fg_mask.sum(): + target_bboxes /= stride_tensor + loss_box, loss_dfl = self.box_loss( + pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask + ) + + loss_box *= self.params["box"] # box gain + loss_cls *= self.params["cls"] # cls gain + loss_dfl *= self.params["dfl"] # dfl gain + + return loss_box, loss_cls, loss_dfl