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|
- import argparse
- import json
- import os
- from pathlib import Path
- from threading import Thread
- import numpy as np
- import torch
- import yaml
- from tqdm import tqdm
- from models.experimental import attempt_load
- from utils.datasets import create_dataloader
- from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
- box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path
- from utils.loss import compute_loss
- from utils.metrics import ap_per_class, ConfusionMatrix
- from utils.plots import plot_images, output_to_target, plot_study_txt
- from utils.torch_utils import select_device, time_synchronized
- def test(data,
- weights=None,
- batch_size=32,
- imgsz=640,
- conf_thres=0.001,
- iou_thres=0.6, # for NMS
- save_json=False,
- single_cls=False,
- augment=False,
- verbose=False,
- model=None,
- dataloader=None,
- save_dir=Path(''), # for saving images
- save_txt=False, # for auto-labelling
- save_hybrid=False, # for hybrid auto-labelling
- save_conf=False, # save auto-label confidences
- plots=True,
- log_imgs=0): # number of logged images
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device = next(model.parameters()).device # get model device
- else: # called directly
- set_logging()
- device = select_device(opt.device, batch_size=batch_size)
- # Directories
- save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
- # Load model
- model = attempt_load(weights, map_location=device) # load FP32 model
- imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
- # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
- # if device.type != 'cpu' and torch.cuda.device_count() > 1:
- # model = nn.DataParallel(model)
- # Half
- half = device.type != 'cpu' # half precision only supported on CUDA
- if half:
- model.half()
- # Configure
- model.eval()
- is_coco = data.endswith('coco.yaml') # is COCO dataset
- with open(data) as f:
- data = yaml.load(f, Loader=yaml.FullLoader) # model dict
- check_dataset(data) # check
- nc = 1 if single_cls else int(data['nc']) # number of classes
- iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
- niou = iouv.numel()
- # Logging
- log_imgs, wandb = min(log_imgs, 100), None # ceil
- try:
- import wandb # Weights & Biases
- except ImportError:
- log_imgs = 0
- # Dataloader
- if not training:
- img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
- _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
- path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
- dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
- seen = 0
- confusion_matrix = ConfusionMatrix(nc=nc)
- names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
- coco91class = coco80_to_coco91_class()
- s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- img = img.to(device, non_blocking=True)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- targets = targets.to(device)
- nb, _, height, width = img.shape # batch size, channels, height, width
- with torch.no_grad():
- # Run model
- t = time_synchronized()
- inf_out, train_out = model(img, augment=augment) # inference and training outputs
- t0 += time_synchronized() - t
- # Compute loss
- if training:
- loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls
- # Run NMS
- targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
- lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
- t = time_synchronized()
- output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
- t1 += time_synchronized() - t
- # Statistics per image
- for si, pred in enumerate(output):
- labels = targets[targets[:, 0] == si, 1:]
- nl = len(labels)
- tcls = labels[:, 0].tolist() if nl else [] # target class
- path = Path(paths[si])
- seen += 1
- if len(pred) == 0:
- if nl:
- stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
- continue
- # Predictions
- predn = pred.clone()
- scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
- # Append to text file
- if save_txt:
- gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
- for *xyxy, conf, cls in predn.tolist():
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
- # W&B logging
- if plots and len(wandb_images) < log_imgs:
- box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
- "class_id": int(cls),
- "box_caption": "%s %.3f" % (names[cls], conf),
- "scores": {"class_score": conf},
- "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
- wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
- # Append to pycocotools JSON dictionary
- if save_json:
- # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
- image_id = int(path.stem) if path.stem.isnumeric() else path.stem
- box = xyxy2xywh(predn[:, :4]) # xywh
- box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
- for p, b in zip(pred.tolist(), box.tolist()):
- jdict.append({'image_id': image_id,
- 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
- 'bbox': [round(x, 3) for x in b],
- 'score': round(p[4], 5)})
- # Assign all predictions as incorrect
- correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
- if nl:
- detected = [] # target indices
- tcls_tensor = labels[:, 0]
- # target boxes
- tbox = xywh2xyxy(labels[:, 1:5])
- scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
- if plots:
- confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1))
- # Per target class
- for cls in torch.unique(tcls_tensor):
- ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
- pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
- # Search for detections
- if pi.shape[0]:
- # Prediction to target ious
- ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
- # Append detections
- detected_set = set()
- for j in (ious > iouv[0]).nonzero(as_tuple=False):
- d = ti[i[j]] # detected target
- if d.item() not in detected_set:
- detected_set.add(d.item())
- detected.append(d)
- correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
- if len(detected) == nl: # all targets already located in image
- break
- # Append statistics (correct, conf, pcls, tcls)
- stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
- # Plot images
- if plots and batch_i < 3:
- f = save_dir / f'test_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
- Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start()
- # Compute statistics
- stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
- if len(stats) and stats[0].any():
- p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
- p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95]
- mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
- else:
- nt = torch.zeros(1)
- # Print results
- pf = '%20s' + '%12.3g' * 6 # print format
- print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
- # Print results per class
- if (verbose or (nc <= 20 and not training)) and nc > 1 and len(stats):
- for i, c in enumerate(ap_class):
- print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
- # Print speeds
- t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
- if not training:
- print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
- # Plots
- if plots:
- confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
- if wandb and wandb.run:
- wandb.log({"Images": wandb_images})
- wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]})
- # Save JSON
- if save_json and len(jdict):
- w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
- anno_json = '../coco/annotations/instances_val2017.json' # annotations json
- pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
- print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
- with open(pred_json, 'w') as f:
- json.dump(jdict, f)
- try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
- anno = COCO(anno_json) # init annotations api
- pred = anno.loadRes(pred_json) # init predictions api
- eval = COCOeval(anno, pred, 'bbox')
- if is_coco:
- eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate
- eval.evaluate()
- eval.accumulate()
- eval.summarize()
- map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
- except Exception as e:
- print(f'pycocotools unable to run: {e}')
- # Return results
- if not training:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- print(f"Results saved to {save_dir}{s}")
- model.float() # for training
- maps = np.zeros(nc) + map
- for i, c in enumerate(ap_class):
- maps[c] = ap[i]
- return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
- if __name__ == '__main__':
- parser = argparse.ArgumentParser(prog='test.py')
- parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
- parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
- parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
- parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--verbose', action='store_true', help='report mAP by class')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- 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('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- opt = parser.parse_args()
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.data = check_file(opt.data) # check file
- print(opt)
- check_requirements()
- if opt.task in ['val', 'test']: # run normally
- test(opt.data,
- opt.weights,
- opt.batch_size,
- opt.img_size,
- opt.conf_thres,
- opt.iou_thres,
- opt.save_json,
- opt.single_cls,
- opt.augment,
- opt.verbose,
- save_txt=opt.save_txt | opt.save_hybrid,
- save_hybrid=opt.save_hybrid,
- save_conf=opt.save_conf,
- )
- elif opt.task == 'study': # run over a range of settings and save/plot
- for weights in opt.weights: # ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
- f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
- x = list(range(320, 1344, 64)) # x axis
- y = [] # y axis
- for i in x: # img-size
- print('\nRunning %s point %s...' % (f, i))
- r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
- plots=False)
- y.append(r + t) # results and times
- np.savetxt(f, y, fmt='%10.4g') # save
- os.system('zip -r study.zip study_*.txt')
- plot_study_txt(f, x) # plot
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