diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md index 362059b288d5..b7fc7c5a8838 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -12,7 +12,7 @@ Before submitting a bug report, please be aware that your issue **must be reprod - **Common dataset**: coco.yaml or coco128.yaml - **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments -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()`. +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()`. ## 🐛 Bug diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 20c1d5b026b0..a7964ea01d5d 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -68,9 +68,9 @@ jobs: # detect python detect.py --weights ${{ matrix.model }}.pt --device $di python detect.py --weights runs/train/exp/weights/last.pt --device $di - # test - python test.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di - python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di + # val + python val.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di + python val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di python hubconf.py # hub python models/yolo.py --cfg ${{ matrix.model }}.yaml # inspect diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index fdf1cfae8df5..787fbd71721b 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -52,5 +52,5 @@ jobs: ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg) - 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. + 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. diff --git a/README.md b/README.md index 64086643373c..035b7002774a 100755 --- a/README.md +++ b/README.md @@ -197,7 +197,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi * 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. * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. - * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` @@ -223,10 +223,10 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi Table Notes (click to expand) * APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. - * 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` - * SpeedGPU 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` + * 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` + * SpeedGPU 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` * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). - * 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` + * 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` diff --git a/models/yolo.py b/models/yolo.py index b11443377080..7b49dfcf48a3 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -310,4 +310,3 @@ def parse_model(d, ch): # model_dict, input_channels(3) # tb_writer = SummaryWriter('.') # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/") # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph - # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/train.py b/train.py index e58d7c4f0348..205c73d85e20 100644 --- a/train.py +++ b/train.py @@ -32,7 +32,7 @@ FILE = Path(__file__).absolute() sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path -import test # for end-of-epoch mAP +import val # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors @@ -57,9 +57,9 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary opt, device, ): - save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \ + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, = \ opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ - opt.resume, opt.notest, opt.nosave, opt.workers + opt.resume, opt.noval, opt.nosave, opt.workers # Directories save_dir = Path(save_dir) @@ -129,7 +129,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary with torch_distributed_zero_first(RANK): check_dataset(data_dict) # check train_path = data_dict['train'] - test_path = data_dict['val'] + val_path = data_dict['val'] # Freeze freeze = [] # parameter names to freeze (full or partial) @@ -207,7 +207,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary # Image sizes gs = max(int(model.stride.max()), 32) # grid size (max stride) nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) - imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + imgsz, imgsz_val = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: @@ -231,8 +231,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary # Process 0 if RANK in [-1, 0]: - testloader = create_dataloader(test_path, imgsz_test, batch_size // WORLD_SIZE * 2, gs, single_cls, - hyp=hyp, cache=opt.cache_images and not notest, rect=True, rank=-1, + valloader = create_dataloader(val_path, imgsz_val, batch_size // WORLD_SIZE * 2, gs, single_cls, + hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1, workers=workers, pad=0.5, prefix=colorstr('val: '))[0] @@ -276,7 +276,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary scheduler.last_epoch = start_epoch - 1 # do not move scaler = amp.GradScaler(enabled=cuda) compute_loss = ComputeLoss(model) # init loss class - logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' + logger.info(f'Image sizes {imgsz} train, {imgsz_val} val\n' f'Using {dataloader.num_workers} dataloader workers\n' f'Logging results to {save_dir}\n' f'Starting training for {epochs} epochs...') @@ -384,20 +384,20 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary # mAP ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) final_epoch = epoch + 1 == epochs - if not notest or final_epoch: # Calculate mAP + if not noval or final_epoch: # Calculate mAP wandb_logger.current_epoch = epoch + 1 - results, maps, _ = test.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz_test, - model=ema.ema, - single_cls=single_cls, - dataloader=testloader, - save_dir=save_dir, - save_json=is_coco and final_epoch, - verbose=nc < 50 and final_epoch, - plots=plots and final_epoch, - wandb_logger=wandb_logger, - compute_loss=compute_loss) + results, maps, _ = val.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz_val, + model=ema.ema, + single_cls=single_cls, + dataloader=valloader, + save_dir=save_dir, + save_json=is_coco and final_epoch, + verbose=nc < 50 and final_epoch, + plots=plots and final_epoch, + wandb_logger=wandb_logger, + compute_loss=compute_loss) # Write with open(results_file, 'a') as f: @@ -454,15 +454,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary if not evolve: if is_coco: # COCO dataset for m in [last, best] if best.exists() else [last]: # speed, mAP tests - results, _, _ = test.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz_test, - model=attempt_load(m, device).half(), - single_cls=single_cls, - dataloader=testloader, - save_dir=save_dir, - save_json=True, - plots=False) + results, _, _ = val.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz_val, + model=attempt_load(m, device).half(), + single_cls=single_cls, + dataloader=valloader, + save_dir=save_dir, + save_json=True, + plots=False) # Strip optimizers for f in last, best: @@ -486,11 +486,11 @@ def parse_opt(known=False): parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, val] image sizes') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') @@ -538,7 +538,7 @@ def main(opt): # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' - opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, val) opt.name = 'evolve' if opt.evolve else opt.name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) @@ -597,7 +597,7 @@ def main(opt): if 'anchors' not in hyp: # anchors commented in hyp.yaml hyp['anchors'] = 3 assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve' - opt.notest, opt.nosave = True, True # only test/save final epoch + opt.noval, opt.nosave = True, True # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here if opt.bucket: diff --git a/tutorial.ipynb b/tutorial.ipynb index 15d003c19606..957c0e140f88 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -643,8 +643,8 @@ "id": "0eq1SMWl6Sfn" }, "source": [ - "# 2. Test\n", - "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." + "# 2. Validate\n", + "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." ] }, { @@ -720,14 +720,14 @@ }, "source": [ "# Run YOLOv5x on COCO val2017\n", - "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half" + "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "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", + "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", "YOLOv5 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n", @@ -741,7 +741,7 @@ " all 5000 36335 0.746 0.626 0.68 0.49\n", "Speed: 5.3/1.5/6.8 ms inference/NMS/total per 640x640 image at batch-size 32\n", "\n", - "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n", + "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n", "loading annotations into memory...\n", "Done (t=0.44s)\n", "creating index...\n", @@ -767,7 +767,7 @@ " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n", - "Results saved to runs/test/exp\n" + "Results saved to runs/val/exp\n" ], "name": "stdout" } @@ -805,7 +805,7 @@ }, "source": [ "# Run YOLOv5s on COCO test-dev2017 using --task test\n", - "!python test.py --weights yolov5s.pt --data coco.yaml --task test" + "!python val.py --weights yolov5s.pt --data coco.yaml --task test" ], "execution_count": null, "outputs": [] @@ -976,7 +976,7 @@ "Plotting labels... \n", "\n", "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", - "Image sizes 640 train, 640 test\n", + "Image sizes 640 train, 640 val\n", "Using 2 dataloader workers\n", "Logging results to runs/train/exp\n", "Starting training for 3 epochs...\n", @@ -1036,7 +1036,7 @@ "source": [ "## Local Logging\n", "\n", - "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)." + "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", "> \n", - "`test_batch0_labels.jpg` shows test batch 0 labels\n", + "`test_batch0_labels.jpg` shows val batch 0 labels\n", "\n", "> \n", - "`test_batch0_pred.jpg` shows test batch 0 _predictions_" + "`test_batch0_pred.jpg` shows val batch 0 _predictions_" ] }, { @@ -1125,7 +1125,7 @@ "\n", "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\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 @@ "source": [ "# Reproduce\n", "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", - " !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP" + " !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 @@ " for d in 0 cpu; do # devices\n", " python detect.py --weights $m.pt --device $d # detect official\n", " 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", diff --git a/utils/augmentations.py b/utils/augmentations.py index c953fcbcc90b..69b835db0db9 100644 --- a/utils/augmentations.py +++ b/utils/augmentations.py @@ -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]) - 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 diff --git a/utils/general.py b/utils/general.py index 23a827d03d80..846c1464c28c 100755 --- a/utils/general.py +++ b/utils/general.py @@ -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 diff --git a/utils/plots.py b/utils/plots.py index 4e6b001dcc2f..cd9a45e8c761 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -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() diff --git a/test.py b/val.py similarity index 95% rename from test.py rename to val.py index 643dc441e521..fa5cb8f113e0 100644 --- a/test.py +++ b/val.py @@ -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