Rename `test.py` to `val.py` (#4000)
Glenn Jocher
GitHub
parent
8ee9fd1505
commit
720aaa65c8
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@ -12,7 +12,7 @@ Before submitting a bug report, please be aware that your issue **must be reprod
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- **Common dataset**: coco.yaml or coco128.yaml
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- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments
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If this is a custom dataset/training question you **must include** your `train*.jpg`, `test*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
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If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
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## 🐛 Bug
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@ -68,9 +68,9 @@ jobs:
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# detect
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python detect.py --weights ${{ matrix.model }}.pt --device $di
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python detect.py --weights runs/train/exp/weights/last.pt --device $di
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# test
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python test.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di
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python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
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# val
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python val.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di
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python val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
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python hubconf.py # hub
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python models/yolo.py --cfg ${{ matrix.model }}.yaml # inspect
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@ -52,5 +52,5 @@ jobs:
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If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
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If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
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@ -197,7 +197,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
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* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
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* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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</details>
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@ -223,10 +223,10 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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<summary>Table Notes (click to expand)</summary>
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* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
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* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
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* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
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* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
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* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
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* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
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* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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</details>
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@ -310,4 +310,3 @@ if __name__ == '__main__':
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# tb_writer = SummaryWriter('.')
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# logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
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# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph
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# tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
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68
train.py
68
train.py
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@ -32,7 +32,7 @@ from tqdm import tqdm
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FILE = Path(__file__).absolute()
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sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
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import test # for end-of-epoch mAP
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import val # for end-of-epoch mAP
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from models.experimental import attempt_load
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from models.yolo import Model
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from utils.autoanchor import check_anchors
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@ -57,9 +57,9 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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opt,
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device,
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):
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save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \
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save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, = \
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opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
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opt.resume, opt.notest, opt.nosave, opt.workers
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opt.resume, opt.noval, opt.nosave, opt.workers
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# Directories
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save_dir = Path(save_dir)
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@ -129,7 +129,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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with torch_distributed_zero_first(RANK):
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check_dataset(data_dict) # check
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train_path = data_dict['train']
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test_path = data_dict['val']
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val_path = data_dict['val']
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# Freeze
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freeze = [] # parameter names to freeze (full or partial)
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@ -207,7 +207,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Image sizes
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
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imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
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imgsz, imgsz_val = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
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# DP mode
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if cuda and RANK == -1 and torch.cuda.device_count() > 1:
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@ -231,8 +231,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Process 0
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if RANK in [-1, 0]:
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testloader = create_dataloader(test_path, imgsz_test, batch_size // WORLD_SIZE * 2, gs, single_cls,
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hyp=hyp, cache=opt.cache_images and not notest, rect=True, rank=-1,
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valloader = create_dataloader(val_path, imgsz_val, batch_size // WORLD_SIZE * 2, gs, single_cls,
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hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
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workers=workers,
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pad=0.5, prefix=colorstr('val: '))[0]
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@ -276,7 +276,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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scheduler.last_epoch = start_epoch - 1 # do not move
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scaler = amp.GradScaler(enabled=cuda)
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compute_loss = ComputeLoss(model) # init loss class
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logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
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logger.info(f'Image sizes {imgsz} train, {imgsz_val} val\n'
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f'Using {dataloader.num_workers} dataloader workers\n'
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f'Logging results to {save_dir}\n'
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f'Starting training for {epochs} epochs...')
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@ -384,20 +384,20 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# mAP
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ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
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final_epoch = epoch + 1 == epochs
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if not notest or final_epoch: # Calculate mAP
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if not noval or final_epoch: # Calculate mAP
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wandb_logger.current_epoch = epoch + 1
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results, maps, _ = test.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz_test,
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model=ema.ema,
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single_cls=single_cls,
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dataloader=testloader,
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save_dir=save_dir,
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save_json=is_coco and final_epoch,
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verbose=nc < 50 and final_epoch,
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plots=plots and final_epoch,
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wandb_logger=wandb_logger,
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compute_loss=compute_loss)
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results, maps, _ = val.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz_val,
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model=ema.ema,
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single_cls=single_cls,
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dataloader=valloader,
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save_dir=save_dir,
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save_json=is_coco and final_epoch,
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verbose=nc < 50 and final_epoch,
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plots=plots and final_epoch,
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wandb_logger=wandb_logger,
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compute_loss=compute_loss)
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# Write
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with open(results_file, 'a') as f:
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@ -454,15 +454,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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if not evolve:
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if is_coco: # COCO dataset
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for m in [last, best] if best.exists() else [last]: # speed, mAP tests
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results, _, _ = test.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz_test,
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model=attempt_load(m, device).half(),
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single_cls=single_cls,
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dataloader=testloader,
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save_dir=save_dir,
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save_json=True,
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plots=False)
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results, _, _ = val.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz_val,
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model=attempt_load(m, device).half(),
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single_cls=single_cls,
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dataloader=valloader,
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save_dir=save_dir,
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save_json=True,
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plots=False)
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# Strip optimizers
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for f in last, best:
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@ -486,11 +486,11 @@ def parse_opt(known=False):
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parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path')
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parser.add_argument('--epochs', type=int, default=300)
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, val] image sizes')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--noval', action='store_true', help='only validate final epoch')
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parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
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parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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@ -538,7 +538,7 @@ def main(opt):
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# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
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opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
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assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
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opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
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opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, val)
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opt.name = 'evolve' if opt.evolve else opt.name
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opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
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@ -597,7 +597,7 @@ def main(opt):
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if 'anchors' not in hyp: # anchors commented in hyp.yaml
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hyp['anchors'] = 3
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assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve'
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opt.notest, opt.nosave = True, True # only test/save final epoch
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opt.noval, opt.nosave = True, True # only val/save final epoch
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# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
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yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
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if opt.bucket:
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@ -643,8 +643,8 @@
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"id": "0eq1SMWl6Sfn"
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},
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"source": [
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"# 2. Test\n",
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"Test a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
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"# 2. Validate\n",
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"Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
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]
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},
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{
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@ -720,14 +720,14 @@
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},
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"source": [
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"# Run YOLOv5x on COCO val2017\n",
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"!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
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"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, half=True, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
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"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, half=True, img_size=640, iou_thres=0.65, name='exp', project='runs/val', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
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"YOLOv5 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
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"\n",
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"Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
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@ -741,7 +741,7 @@
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" all 5000 36335 0.746 0.626 0.68 0.49\n",
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"Speed: 5.3/1.5/6.8 ms inference/NMS/total per 640x640 image at batch-size 32\n",
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"\n",
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"Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
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"Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
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"loading annotations into memory...\n",
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"Done (t=0.44s)\n",
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"creating index...\n",
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@ -767,7 +767,7 @@
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" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n",
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" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",
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" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n",
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"Results saved to runs/test/exp\n"
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"Results saved to runs/val/exp\n"
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],
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"name": "stdout"
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}
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@ -805,7 +805,7 @@
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},
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"source": [
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"# Run YOLOv5s on COCO test-dev2017 using --task test\n",
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"!python test.py --weights yolov5s.pt --data coco.yaml --task test"
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"!python val.py --weights yolov5s.pt --data coco.yaml --task test"
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],
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"execution_count": null,
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"outputs": []
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@ -976,7 +976,7 @@
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"Plotting labels... \n",
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"\n",
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"\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
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"Image sizes 640 train, 640 test\n",
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"Image sizes 640 train, 640 val\n",
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"Using 2 dataloader workers\n",
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"Logging results to runs/train/exp\n",
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"Starting training for 3 epochs...\n",
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@ -1036,7 +1036,7 @@
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"source": [
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"## Local Logging\n",
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"\n",
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"All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
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"All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and val jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1046,8 +1046,8 @@
|
|||
},
|
||||
"source": [
|
||||
"Image(filename='runs/train/exp/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
|
||||
"Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800) # test batch 0 labels\n",
|
||||
"Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
|
||||
"Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800) # val batch 0 labels\n",
|
||||
"Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800) # val batch 0 predictions"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
|
|
@ -1062,10 +1062,10 @@
|
|||
"`train_batch0.jpg` shows train batch 0 mosaics and labels\n",
|
||||
"\n",
|
||||
"> <img src=\"https://user-images.githubusercontent.com/26833433/124931217-4826f080-e002-11eb-87b9-ae0925a8c94b.jpg\" width=\"700\"> \n",
|
||||
"`test_batch0_labels.jpg` shows test batch 0 labels\n",
|
||||
"`test_batch0_labels.jpg` shows val batch 0 labels\n",
|
||||
"\n",
|
||||
"> <img src=\"https://user-images.githubusercontent.com/26833433/124931209-46f5c380-e002-11eb-9bd5-7a3de2be9851.jpg\" width=\"700\"> \n",
|
||||
"`test_batch0_pred.jpg` shows test batch 0 _predictions_"
|
||||
"`test_batch0_pred.jpg` shows val batch 0 _predictions_"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1125,7 +1125,7 @@
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|||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
|
||||
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1147,8 +1147,8 @@
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|||
"source": [
|
||||
"# Reproduce\n",
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||||
"for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n",
|
||||
" !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n",
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" !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP"
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" !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n",
|
||||
" !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
|
|
@ -1193,8 +1193,8 @@
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|||
" for d in 0 cpu; do # devices\n",
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||||
" python detect.py --weights $m.pt --device $d # detect official\n",
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||||
" python detect.py --weights runs/train/exp/weights/best.pt --device $d # detect custom\n",
|
||||
" python test.py --weights $m.pt --device $d # test official\n",
|
||||
" python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n",
|
||||
" python val.py --weights $m.pt --device $d # val official\n",
|
||||
" python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",
|
||||
" done\n",
|
||||
" python hubconf.py # hub\n",
|
||||
" python models/yolo.py --cfg $m.yaml # inspect\n",
|
||||
|
|
|
|||
|
|
@ -90,7 +90,7 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
|
|||
|
||||
# Scale ratio (new / old)
|
||||
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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||||
if not scaleup: # only scale down, do not scale up (for better test mAP)
|
||||
if not scaleup: # only scale down, do not scale up (for better val mAP)
|
||||
r = min(r, 1.0)
|
||||
|
||||
# Compute padding
|
||||
|
|
|
|||
|
|
@ -633,7 +633,7 @@ def apply_classifier(x, model, img, im0):
|
|||
for j, a in enumerate(d): # per item
|
||||
cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
|
||||
im = cv2.resize(cutout, (224, 224)) # BGR
|
||||
# cv2.imwrite('test%i.jpg' % j, cutout)
|
||||
# cv2.imwrite('example%i.jpg' % j, cutout)
|
||||
|
||||
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
||||
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
|
||||
|
|
|
|||
|
|
@ -219,9 +219,9 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
|
|||
plt.close()
|
||||
|
||||
|
||||
def plot_test_txt(): # from utils.plots import *; plot_test()
|
||||
# Plot test.txt histograms
|
||||
x = np.loadtxt('test.txt', dtype=np.float32)
|
||||
def plot_val_txt(): # from utils.plots import *; plot_val()
|
||||
# Plot val.txt histograms
|
||||
x = np.loadtxt('val.txt', dtype=np.float32)
|
||||
box = xyxy2xywh(x[:, :4])
|
||||
cx, cy = box[:, 0], box[:, 1]
|
||||
|
||||
|
|
@ -250,7 +250,7 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
|
|||
|
||||
|
||||
def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt()
|
||||
# Plot study.txt generated by test.py
|
||||
# Plot study.txt generated by val.py
|
||||
plot2 = False # plot additional results
|
||||
if plot2:
|
||||
ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
"""Test a trained YOLOv5 model accuracy on a custom dataset
|
||||
"""Validate a trained YOLOv5 model accuracy on a custom dataset
|
||||
|
||||
Usage:
|
||||
$ python path/to/test.py --data coco128.yaml --weights yolov5s.pt --img 640
|
||||
$ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
|
@ -44,7 +44,7 @@ def run(data,
|
|||
save_hybrid=False, # save label+prediction hybrid results to *.txt
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_json=False, # save a cocoapi-compatible JSON results file
|
||||
project='runs/test', # save to project/name
|
||||
project='runs/val', # save to project/name
|
||||
name='exp', # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=True, # use FP16 half-precision inference
|
||||
|
|
@ -228,9 +228,9 @@ def run(data,
|
|||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
|
||||
f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels
|
||||
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
|
||||
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
|
||||
f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions
|
||||
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
|
||||
|
||||
# Compute statistics
|
||||
|
|
@ -262,7 +262,7 @@ def run(data,
|
|||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
if wandb_logger and wandb_logger.wandb:
|
||||
val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
|
||||
val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('val*.jpg'))]
|
||||
wandb_logger.log({"Validation": val_batches})
|
||||
if wandb_images:
|
||||
wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
|
||||
|
|
@ -305,7 +305,7 @@ def run(data,
|
|||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser = argparse.ArgumentParser(prog='val.py')
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
|
||||
|
|
@ -321,7 +321,7 @@ def parse_opt():
|
|||
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
||||
parser.add_argument('--project', default='runs/test', help='save to project/name')
|
||||
parser.add_argument('--project', default='runs/val', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
|
|
@ -334,7 +334,7 @@ def parse_opt():
|
|||
|
||||
def main(opt):
|
||||
set_logging()
|
||||
print(colorstr('test: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
|
||||
print(colorstr('val: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
|
||||
check_requirements(exclude=('tensorboard', 'thop'))
|
||||
|
||||
if opt.task in ('train', 'val', 'test'): # run normally
|
||||
|
|
@ -346,7 +346,7 @@ def main(opt):
|
|||
save_json=False, plots=False)
|
||||
|
||||
elif opt.task == 'study': # run over a range of settings and save/plot
|
||||
# python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
|
||||
x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
|
||||
for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
|
||||
f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
|
||||
Loading…
Reference in New Issue