diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1a8d18c..66427cd 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -48,9 +48,7 @@ jobs: run: | python -m unittest - - name: Test scripts - env: - ROBOFLOW_TOKEN: ${{ secrets.ROBOFLOW_API_KEY }} + - name: Test yolov5 train shell: bash # for Windows compatibility run: | pip install -e . @@ -66,34 +64,57 @@ jobs: python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10 yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --evolve 2 - # detect + + - name: Test yolov5 detect + shell: bash # for Windows compatibility + run: | python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device cpu yolov5 detect --weights yolov5/weights/yolov5n.pt --device cpu python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device cpu yolov5 detect --weights runs/train/exp/weights/last.pt --device cpu - # val + + - name: Test yolov5 val + shell: bash # for Windows compatibility + run: | python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu - # export + + - name: Test yolov5 export + shell: bash # for Windows compatibility + run: | pip install onnx onnx-simplifier tensorflowjs python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device cpu --include torchscript,onnx,tflite yolov5 export --weights yolov5/weights/yolov5n.pt --device cpu --simplify --include torchscript,onnx,saved_model,pb,tfjs - # benckmarks + + - name: Test yolov5 benchmarks + shell: bash # for Windows compatibility + run: | yolov5 benchmarks --weights yolov5n.pt --img 128 --pt-only --device cpu - # classify + + - name: Test yolov5 classify + shell: bash # for Windows compatibility + run: | yolov5 classify train --img 128 --data mnist2560 --model yolov5n-cls.pt --epochs 1 --device cpu yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu yolov5 classify val --img 128 --data datasets/mnist2560 --weights yolov5n-cls.pt --device cpu yolov5 classify predict --img 128 --weights yolov5n-cls.pt --device cpu - # segment + + - name: Test yolov5 segment + shell: bash # for Windows compatibility + run: | yolov5 segment train --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu yolov5 segment train --img 128 --weights fcakyon/yolov5n-seg-v7.0 --epochs 1 --device cpu # yolov5 segment val --img 128 --weights yolov5n-seg.pt --device cpu yolov5 segment predict --img 128 --weights yolov5n-seg.pt --device cpu - # roboflow - yolov5 train --data https://universe.roboflow.com/jacob-solawetz/aerial-maritime/dataset/10 --weights yolov5/weights/yolov5n.pt --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} - yolov5 classify train --data https://universe.roboflow.com/bagas-etcfr/flowerdata1/dataset/1 --img 128 --model yolov5n-cls.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} - yolov5 segment train --data https://universe.roboflow.com/scaffolding/scaffolding-distance/dataset/2 --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} + + - name: Test roboflow train + shell: bash # for Windows compatibility + env: + ROBOFLOW_TOKEN: ${{ secrets.ROBOFLOW_API_KEY }} + run: | + yolov5 train --data https://universe.roboflow.com/gdit/aerial-airport/dataset/1 --weights yolov5/weights/yolov5n.pt --img 128 --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} + # yolov5 classify train --data https://universe.roboflow.com/carlos-gabriel-da-silva-machado-siwvs/turtles-i1tlr/dataset/1 --img 128 --model yolov5n-cls.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} + yolov5 segment train --data https://universe.roboflow.com/aymane-outani-auooc/cable-fzjik/dataset/2 --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} diff --git a/.github/workflows/package_testing.yml b/.github/workflows/package_testing.yml index 3b0e345..bd6261a 100644 --- a/.github/workflows/package_testing.yml +++ b/.github/workflows/package_testing.yml @@ -46,9 +46,9 @@ jobs: run: | python -m unittest - - name: Test scripts + - name: Test yolov5 train + shell: bash # for Windows compatibility run: | - pip install -e . # donwload coco formatted testing data python tests/download_test_coco_dataset.py # train (dl base model from hf hub) @@ -61,29 +61,49 @@ jobs: python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10 yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --evolve 2 - # detect + - name: Test yolov5 detect + shell: bash # for Windows compatibility + run: | python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device cpu yolov5 detect --weights yolov5/weights/yolov5n.pt --device cpu python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device cpu yolov5 detect --weights runs/train/exp/weights/last.pt --device cpu - # val + - name: Test yolov5 val + shell: bash # for Windows compatibility + run: | python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu - # export + - name: Test yolov5 export + shell: bash # for Windows compatibility + run: | pip install onnx onnx-simplifier tensorflowjs python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device cpu --include torchscript,onnx,tflite yolov5 export --weights yolov5/weights/yolov5n.pt --device cpu --simplify --include torchscript,onnx,saved_model,pb,tfjs - # benckmarks + - name: Test yolov5 benchmarks + shell: bash # for Windows compatibility + run: | yolov5 benchmarks --weights yolov5n.pt --img 128 --pt-only --device cpu - # classify + - name: Test yolov5 classify + shell: bash # for Windows compatibility + run: | yolov5 classify train --img 128 --data mnist2560 --model yolov5n-cls.pt --epochs 1 --device cpu yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu yolov5 classify val --img 128 --data datasets/mnist2560 --weights yolov5n-cls.pt --device cpu yolov5 classify predict --img 128 --weights yolov5n-cls.pt --device cpu - # segment + - name: Test yolov5 segment + shell: bash # for Windows compatibility + run: | yolov5 segment train --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu yolov5 segment train --img 128 --weights fcakyon/yolov5n-seg-v7.0 --epochs 1 --device cpu # yolov5 segment val --img 128 --weights yolov5n-seg.pt --device cpu yolov5 segment predict --img 128 --weights yolov5n-seg.pt --device cpu + - name: Test roboflow train + shell: bash # for Windows compatibility + env: + ROBOFLOW_TOKEN: ${{ secrets.ROBOFLOW_API_KEY }} + run: | + yolov5 train --data https://universe.roboflow.com/gdit/aerial-airport/dataset/1 --weights yolov5/weights/yolov5n.pt --img 128 --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} + # yolov5 classify train --data https://universe.roboflow.com/carlos-gabriel-da-silva-machado-siwvs/turtles-i1tlr/dataset/1 --img 128 --model yolov5n-cls.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} + yolov5 segment train --data https://universe.roboflow.com/aymane-outani-auooc/cable-fzjik/dataset/2 --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu --roboflow_token ${{ env.ROBOFLOW_TOKEN }} diff --git a/README.md b/README.md index 866f615..e9b4070 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ You can finally install YOLOv5 o
-This yolov5 package contains everything from ultralytics/yolov5 at this commit plus: +This yolov5 package contains everything from ultralytics/yolov5 at this commit plus:
1. Easy installation via pip: pip install yolov5
diff --git a/requirements.txt b/requirements.txt index 4af966e..7d32a73 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -# YOLOv5 🚀 requirements +# YOLOv5 requirements # Usage: pip install -r requirements.txt # Base ------------------------------------------------------------------------ @@ -29,7 +29,7 @@ seaborn>=0.11.0 # Export ---------------------------------------------------------------------- # coremltools>=6.0 # CoreML export -# onnx>=1.9.0 # ONNX export +# onnx>=1.12.0 # ONNX export # onnx-simplifier>=0.4.1 # ONNX simplifier # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export @@ -44,7 +44,7 @@ seaborn>=0.11.0 # Extras ---------------------------------------------------------------------- # mss # screenshots # albumentations>=1.0.3 -# pycocotools>=2.0 # COCO mAP +# pycocotools>=2.0.6 # COCO mAP # roboflow>=0.2.27 # ultralytics # HUB https://hub.ultralytics.com @@ -55,4 +55,4 @@ boto3>=1.19.1 # coco to yolov5 conversion sahi>=0.11.10 # huggingface -huggingface-hub>=0.11.1 +huggingface-hub>=0.12.0 diff --git a/yolov5/__init__.py b/yolov5/__init__.py index ce6190f..ccf565d 100644 --- a/yolov5/__init__.py +++ b/yolov5/__init__.py @@ -1,4 +1,4 @@ from yolov5.helpers import YOLOv5 from yolov5.helpers import load_model as load -__version__ = "7.0.7" +__version__ = "7.0.8" diff --git a/yolov5/classify/predict.py b/yolov5/classify/predict.py index 8feafe6..dc9dc7c 100644 --- a/yolov5/classify/predict.py +++ b/yolov5/classify/predict.py @@ -213,7 +213,7 @@ def parse_opt(): 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/predict-cls', help='save results to project/name') + parser.add_argument('--project', default='runs/predict-cls', 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('--half', action='store_true', help='use FP16 half-precision inference') @@ -227,7 +227,7 @@ def parse_opt(): def main(): opt = parse_opt() - #check_requirements(exclude=('tensorboard', 'thop')) + check_requirements(exclude=('tensorboard', 'thop')) run(**vars(opt)) diff --git a/yolov5/classify/train.py b/yolov5/classify/train.py index bd2c6ad..c011c1c 100644 --- a/yolov5/classify/train.py +++ b/yolov5/classify/train.py @@ -6,7 +6,7 @@ $ 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 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 + $ 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 @@ -30,7 +30,7 @@ from torch.cuda import amp from tqdm import tqdm -from yolov5.utils.downloads import attempt_donwload_from_hub +from yolov5.utils.downloads import attempt_download_from_hub from yolov5.utils.roboflow import check_dataset_roboflow FILE = Path(__file__).resolve() @@ -43,18 +43,12 @@ from yolov5.models.experimental import attempt_load from yolov5.models.yolo import ClassificationModel, DetectionModel from yolov5.utils.dataloaders import create_classification_dataloader -from yolov5.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 yolov5.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 yolov5.utils.loggers import GenericLogger from yolov5.utils.plots import imshow_cls -from yolov5.utils.torch_utils import (ModelEMA, model_info, - reshape_classifier_output, select_device, - smart_DDP, smart_optimizer, - smartCrossEntropyLoss, - torch_distributed_zero_first) +from yolov5.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)) @@ -116,7 +110,7 @@ def train(opt, device): # Model # try to download from hf hub - result = attempt_donwload_from_hub(opt.model, hf_token=None) + result = attempt_download_from_hub(opt.model, hf_token=None) if result is not None: opt.model = result with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): @@ -266,7 +260,7 @@ def train(opt, device): f"\nPredict: yolov5 classify predict --weights {best} --source im.jpg" f"\nValidate: yolov5 classify val --weights {best} --data {data_dir}" f"\nExport: yolov5 export --weights {best} --include onnx" - f"\nPyPi: model = yolov5.load('custom.pt')" + f"\nPython: model = yolov5.load('{best}')" f"\nVisualize: https://netron.app\n") # Plot examples diff --git a/yolov5/classify/val.py b/yolov5/classify/val.py index b31309b..7a0a23e 100644 --- a/yolov5/classify/val.py +++ b/yolov5/classify/val.py @@ -43,7 +43,7 @@ @smart_inference_mode() def run( - data=ROOT / '../datasets/mnist', # dataset dir + data='../datasets/mnist', # dataset dir weights='yolov5s-cls.pt', # model.pt path(s) batch_size=None, # batch size batch=None, # batch size @@ -155,14 +155,14 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)') + parser.add_argument('--data', type=str, default='../datasets/mnist', help='dataset path') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s-cls.pt', help='model.pt path(s)') parser.add_argument('--batch-size', type=int, default=128, help='batch size') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)') 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('--verbose', nargs='?', const=True, default=True, help='verbose output') - parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name') + parser.add_argument('--project', default='runs/val-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('--half', action='store_true', help='use FP16 half-precision inference') diff --git a/yolov5/data/hyps/hyp.no-augmentation.yaml b/yolov5/data/hyps/hyp.no-augmentation.yaml new file mode 100644 index 0000000..8fbd5b2 --- /dev/null +++ b/yolov5/data/hyps/hyp.no-augmentation.yaml @@ -0,0 +1,35 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters when using Albumentations frameworks +# python train.py --hyp hyp.no-augmentation.yaml +# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +# this parameters are all zero since we want to use albumentation framework +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0 # image HSV-Hue augmentation (fraction) +hsv_s: 00 # image HSV-Saturation augmentation (fraction) +hsv_v: 0 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0 # image translation (+/- fraction) +scale: 0 # image scale (+/- gain) +shear: 0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.0 # image flip left-right (probability) +mosaic: 0.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/yolov5/detect.py b/yolov5/detect.py index 771ce7b..153f2c2 100644 --- a/yolov5/detect.py +++ b/yolov5/detect.py @@ -262,7 +262,7 @@ def parse_opt(): def main(): opt = parse_opt() - #check_requirements(exclude=('tensorboard', 'thop')) + check_requirements(exclude=('tensorboard', 'thop')) run(**vars(opt)) diff --git a/yolov5/export.py b/yolov5/export.py index e041399..d7cd89a 100644 --- a/yolov5/export.py +++ b/yolov5/export.py @@ -132,7 +132,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:' @try_export def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')): # YOLOv5 ONNX export - check_requirements('onnx') + check_requirements('onnx>=1.12.0') import onnx LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') @@ -615,13 +615,13 @@ def run( LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)' f"\nResults saved to {colorstr('bold', file.parent.resolve())}" f"\nDetect: yolov5 {'detect' if det else 'predict'} --weights {f[-1]} {h}" - f"\nValidate: yolov5 yolov5 val --weights {f[-1]} {h}" - f"\nnPython: model = yolov5.load('{f[-1]}') {s}" + f"\nValidate: yolov5 val --weights {f[-1]} {h}" + f"\nPython: model = yolov5.load('{f[-1]}') {s}" f"\nVisualize: https://netron.app") return f # return list of exported files/dirs -def parse_opt(): +def parse_opt(known=False): 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='yolov5s.pt', help='model.pt path(s)') @@ -635,7 +635,7 @@ def parse_opt(): parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization') parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: 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('--opset', type=int, default=17, 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') @@ -649,7 +649,7 @@ def parse_opt(): nargs='+', default=['torchscript'], help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle') - opt = parser.parse_args() + opt = parser.parse_known_args()[0] if known else parser.parse_args() print_args(vars(opt)) return opt diff --git a/yolov5/models/common.py b/yolov5/models/common.py index 2497957..b2de4d0 100644 --- a/yolov5/models/common.py +++ b/yolov5/models/common.py @@ -27,7 +27,7 @@ from yolov5.utils import TryExcept from yolov5.utils.dataloaders import exif_transpose, letterbox -from yolov5.utils.downloads import attempt_donwload_from_hub +from yolov5.utils.downloads import attempt_download_from_hub from yolov5.utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, xyxy2xywh, yaml_load) @@ -333,11 +333,10 @@ def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, from yolov5.models.experimental import attempt_download, attempt_load # scoped to avoid circular import super().__init__() - w = str(weights[0] if isinstance(weights, list) else weights) # try to dowload from hf hub - result = attempt_donwload_from_hub(w, hf_token=hf_token) + result = attempt_download_from_hub(w, hf_token=hf_token) if result is not None: w = result diff --git a/yolov5/segment/train.py b/yolov5/segment/train.py index d0a0b08..e8baca5 100644 --- a/yolov5/segment/train.py +++ b/yolov5/segment/train.py @@ -47,7 +47,7 @@ from yolov5.utils.autoanchor import check_anchors from yolov5.utils.autobatch import check_train_batch_size from yolov5.utils.callbacks import Callbacks -from yolov5.utils.downloads import (attempt_donwload_from_hub, +from yolov5.utils.downloads import (attempt_download_from_hub, attempt_download, is_url) from yolov5.utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, @@ -119,7 +119,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Model # try to download from hf hub - result = attempt_donwload_from_hub(weights, hf_token=None) + result = attempt_download_from_hub(weights, hf_token=None) if result is not None: weights = result check_suffix(weights, '.pt') # check weights diff --git a/yolov5/train.py b/yolov5/train.py index 7e8154d..1091aff 100644 --- a/yolov5/train.py +++ b/yolov5/train.py @@ -36,6 +36,7 @@ from yolov5.helpers import (convert_coco_dataset_to_yolo, push_to_hfhub, upload_to_s3) from yolov5.utils.roboflow import check_dataset_roboflow +from yolov5 import __version__ FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory @@ -50,25 +51,19 @@ from yolov5.utils.autobatch import check_train_batch_size from yolov5.utils.callbacks import Callbacks from yolov5.utils.dataloaders import create_dataloader -from yolov5.utils.downloads import (attempt_donwload_from_hub, - attempt_download, is_url) -from yolov5.utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, - check_dataset, check_file, check_img_size, - check_suffix, check_yaml, colorstr, - get_latest_run, increment_path, init_seeds, - intersect_dicts, labels_to_class_weights, - labels_to_image_weights, methods, one_cycle, - print_args, print_mutation, strip_optimizer, - yaml_save) +from yolov5.utils.downloads import attempt_download, is_url, attempt_download_from_hub +from yolov5.utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, + check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, + get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, + labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, + yaml_save) from yolov5.utils.loggers import Loggers from yolov5.utils.loggers.comet.comet_utils import check_comet_resume from yolov5.utils.loss import ComputeLoss from yolov5.utils.metrics import fitness from yolov5.utils.plots import plot_evolve -from yolov5.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, - select_device, smart_DDP, - smart_optimizer, smart_resume, - torch_distributed_zero_first) +from yolov5.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, + smart_resume, 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)) @@ -134,7 +129,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Model # try to download from hf hub - result = attempt_donwload_from_hub(weights, hf_token=None) + result = attempt_download_from_hub(weights, hf_token=None) if result is not None: weights = result check_suffix(weights, '.pt') # check weights @@ -222,7 +217,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '), - shuffle=True) + shuffle=True, + seed=opt.seed) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' @@ -271,7 +267,6 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class - map50s = np.zeros(nc) # mAP50 per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) @@ -402,7 +397,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'opt': vars(opt), - 'date': datetime.now().isoformat()} + 'date': datetime.now().isoformat(), + 'yolov5pip_version': __version__} # Save last, best and delete torch.save(ckpt, last) @@ -442,7 +438,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f'\nValidating {f}...') - results, _, _, _ = validate.run( + results, _, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, @@ -557,7 +553,7 @@ def main(opt, callbacks=Callbacks()): if RANK in {-1, 0}: print_args(vars(opt)) #check_git_status() - #check_requirements() + check_requirements() if "roboflow.com" in str(opt.data): opt.data = check_dataset_roboflow( @@ -711,6 +707,7 @@ def run(**kwargs): main(opt) return opt + def run_cli(**kwargs): ''' To be called from yolov5.cli @@ -720,6 +717,7 @@ def run_cli(**kwargs): setattr(opt, k, v) main(opt) + if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/yolov5/utils/dataloaders.py b/yolov5/utils/dataloaders.py index b35e35d..8e80535 100644 --- a/yolov5/utils/dataloaders.py +++ b/yolov5/utils/dataloaders.py @@ -115,7 +115,8 @@ def create_dataloader(path, image_weights=False, quad=False, prefix='', - shuffle=False): + shuffle=False, + seed=0): if rect and shuffle: LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') shuffle = False @@ -141,7 +142,7 @@ def create_dataloader(path, sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() - generator.manual_seed(6148914691236517205 + RANK) + generator.manual_seed(6148914691236517205 + seed + RANK) return loader(dataset, batch_size=batch_size, shuffle=shuffle and sampler is None, @@ -756,7 +757,7 @@ def load_image(self, i): r = self.img_size / max(h0, w0) # ratio if r != 1: # if sizes are not equal interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA - im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp) + im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized diff --git a/yolov5/utils/downloads.py b/yolov5/utils/downloads.py index a4bdb9b..394d9a0 100644 --- a/yolov5/utils/downloads.py +++ b/yolov5/utils/downloads.py @@ -71,7 +71,7 @@ def github_assets(repository, version='latest'): return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets # try to download from hf hub - result = attempt_donwload_from_hub(file, hf_token=hf_token) + result = attempt_download_from_hub(file, hf_token=hf_token) if result is not None: file = result @@ -110,8 +110,6 @@ def github_assets(repository, version='latest'): min_bytes=1E5, error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}') - - return str(file) @@ -128,7 +126,7 @@ def get_model_filename_from_hfhub(repo_id, hf_token=None): return None -def attempt_donwload_from_hub(repo_id, hf_token=None): +def attempt_download_from_hub(repo_id, hf_token=None): from huggingface_hub import hf_hub_download, list_repo_files from huggingface_hub.utils._errors import RepositoryNotFoundError from huggingface_hub.utils._validators import HFValidationError diff --git a/yolov5/utils/general.py b/yolov5/utils/general.py index 0695da5..a29e893 100644 --- a/yolov5/utils/general.py +++ b/yolov5/utils/general.py @@ -750,30 +750,30 @@ def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) def xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center - y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center - y[:, 2] = x[:, 2] - x[:, 0] # width - y[:, 3] = x[:, 3] - x[:, 1] # height + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center + y[..., 2] = x[..., 2] - x[..., 0] # width + y[..., 3] = x[..., 3] - x[..., 1] # height return y def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.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 + 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 xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x - y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y - y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x - y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x + y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y + y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x + y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y return y @@ -782,18 +782,18 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): if clip: clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip y = x.clone() if isinstance(x, torch.Tensor) else np.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 + 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 xyn2xy(x, w=640, h=640, padw=0, padh=0): # Convert normalized segments into pixel segments, shape (n,2) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * x[:, 0] + padw # top left x - y[:, 1] = h * x[:, 1] + padh # top left y + y[..., 0] = w * x[..., 0] + padw # top left x + y[..., 1] = h * x[..., 1] + padh # top left y return y @@ -833,9 +833,9 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): gain = ratio_pad[0][0] pad = ratio_pad[1] - boxes[:, [0, 2]] -= pad[0] # x padding - boxes[:, [1, 3]] -= pad[1] # y padding - boxes[:, :4] /= gain + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain clip_boxes(boxes, img0_shape) return boxes @@ -862,13 +862,13 @@ def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=F def clip_boxes(boxes, shape): # Clip boxes (xyxy) to image shape (height, width) if isinstance(boxes, torch.Tensor): # faster individually - boxes[:, 0].clamp_(0, shape[1]) # x1 - boxes[:, 1].clamp_(0, shape[0]) # y1 - boxes[:, 2].clamp_(0, shape[1]) # x2 - boxes[:, 3].clamp_(0, shape[0]) # y2 + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 else: # np.array (faster grouped) - boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2 + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 def clip_segments(segments, shape): @@ -898,6 +898,9 @@ def non_max_suppression( list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) prediction = prediction[0] # select only inference output @@ -909,10 +912,6 @@ def non_max_suppression( nc = prediction.shape[2] - nm - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates - # Checks - assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' - assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' - # Settings # min_wh = 2 # (pixels) minimum box width and height max_wh = 7680 # (pixels) maximum box width and height @@ -970,17 +969,13 @@ def non_max_suppression( n = x.shape[0] # number of boxes if not n: # no boxes continue - elif n > max_nms: # excess boxes - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence - else: - x = x[x[:, 4].argsort(descending=True)] # sort by confidence + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - if i.shape[0] > max_det: # limit detections - i = i[:max_det] + i = i[:max_det] # limit detections if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix diff --git a/yolov5/utils/loggers/__init__.py b/yolov5/utils/loggers/__init__.py index ed1354e..b93d3b5 100644 --- a/yolov5/utils/loggers/__init__.py +++ b/yolov5/utils/loggers/__init__.py @@ -115,6 +115,17 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, if not neptune: prefix = colorstr('Neptune AI: ') s = f"{prefix}run 'pip install neptune-client' to automatically track and visualize YOLOv5 🚀 runs" + # if not wandb: + # prefix = colorstr('Weights & Biases: ') + # s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases" + # self.logger.info(s) + # if not clearml: + # prefix = colorstr('ClearML: ') + # s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML" + # self.logger.info(s) + # if not comet_ml: + # prefix = colorstr('Comet: ') + # s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" self.logger.info(s) # TensorBoard s = self.save_dir @@ -129,6 +140,10 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None self.opt.hyp = self.hyp # add hyperparameters self.wandb = WandbLogger(self.opt, run_id) + # temp warn. because nested artifacts not supported after 0.12.10 + # if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'): + # s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected." + # self.logger.warning(s) else: self.wandb = None diff --git a/yolov5/utils/loggers/comet/README.md b/yolov5/utils/loggers/comet/README.md index 3a51cb9..8a361e2 100644 --- a/yolov5/utils/loggers/comet/README.md +++ b/yolov5/utils/loggers/comet/README.md @@ -2,13 +2,13 @@ # YOLOv5 with Comet -This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet) +This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) # About Comet Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. -Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! +Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! # Getting Started @@ -51,10 +51,10 @@ python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yo That's it! Comet will automatically log your hyperparameters, command line arguments, training and valiation metrics. You can visualize and analyze your runs in the Comet UI -yolo-ui +yolo-ui # Try out an Example! -Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) +Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) Or better yet, try it out yourself in this Colab Notebook @@ -119,7 +119,7 @@ You can control the frequency of logged predictions and the associated images by **Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. -Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) +Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) ```shell @@ -161,7 +161,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \ ## Uploading a Dataset to Comet Artifacts -If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration), you can do so using the `upload_dataset` flag. +If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. @@ -251,6 +251,6 @@ comet optimizer -j utils/loggers/comet/hpo.py \ ### Visualizing Results -Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) +Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) hyperparameter-yolo diff --git a/yolov5/utils/loggers/wandb/wandb_utils.py b/yolov5/utils/loggers/wandb/wandb_utils.py index 35485f4..17c4b00 100644 --- a/yolov5/utils/loggers/wandb/wandb_utils.py +++ b/yolov5/utils/loggers/wandb/wandb_utils.py @@ -17,7 +17,7 @@ from yolov5 import __version__ from yolov5.utils.dataloaders import LoadImagesAndLabels, img2label_paths -from yolov5.utils.general import LOGGER, check_dataset, check_file +from utils.general import LOGGER, check_dataset, check_file try: import wandb diff --git a/yolov5/utils/metrics.py b/yolov5/utils/metrics.py index 52c7919..ff6946a 100644 --- a/yolov5/utils/metrics.py +++ b/yolov5/utils/metrics.py @@ -208,7 +208,7 @@ def plot(self, normalize=True, save_dir='', names=()): vmin=0.0, xticklabels=ticklabels, yticklabels=ticklabels).set_facecolor((1, 1, 1)) - ax.set_ylabel('True') + ax.set_xlabel('True') ax.set_ylabel('Predicted') ax.set_title('Confusion Matrix') fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) diff --git a/yolov5/utils/plots.py b/yolov5/utils/plots.py index fffa5bc..08a9c71 100644 --- a/yolov5/utils/plots.py +++ b/yolov5/utils/plots.py @@ -88,7 +88,8 @@ def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 2 if self.pil or not is_ascii(label): self.draw.rectangle(box, width=self.lw, outline=color) # box if label: - w, h = self.font.getsize(label) # text width, height + w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0 + # _, _, w, h = self.font.getbbox(label) # text width, height (New) outside = box[1] - h >= 0 # label fits outside box self.draw.rectangle( (box[0], box[1] - h if outside else box[1], box[0] + w + 1, diff --git a/yolov5/utils/roboflow.py b/yolov5/utils/roboflow.py index b243cd7..aa7d919 100644 --- a/yolov5/utils/roboflow.py +++ b/yolov5/utils/roboflow.py @@ -19,7 +19,7 @@ def extract_roboflow_metadata(url: str) -> tuple: def resolve_roboflow_model_format(task: str) -> str: task_format_mapping = { - "detect": "yolov8", + "detect": "yolov5", "segment": "yolov5", "classify": "folder" } diff --git a/yolov5/utils/segment/dataloaders.py b/yolov5/utils/segment/dataloaders.py index 9de6f0f..d66b361 100644 --- a/yolov5/utils/segment/dataloaders.py +++ b/yolov5/utils/segment/dataloaders.py @@ -37,7 +37,8 @@ def create_dataloader(path, prefix='', shuffle=False, mask_downsample_ratio=1, - overlap_mask=False): + overlap_mask=False, + seed=0): if rect and shuffle: LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') shuffle = False @@ -64,7 +65,7 @@ def create_dataloader(path, sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() - generator.manual_seed(6148914691236517205 + RANK) + generator.manual_seed(6148914691236517205 + seed + RANK) return loader( dataset, batch_size=batch_size, diff --git a/yolov5/val.py b/yolov5/val.py index e8c6dd7..2b880f9 100644 --- a/yolov5/val.py +++ b/yolov5/val.py @@ -336,7 +336,7 @@ def run( json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb - check_requirements('pycocotools') + check_requirements('pycocotools>=2.0.6') from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval @@ -400,7 +400,7 @@ def parse_opt(): def main(): opt = parse_opt() - #check_requirements(exclude=('tensorboard', 'thop')) + check_requirements(exclude=('tensorboard', 'thop')) if opt.task in ('train', 'val', 'test'): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466