289 lines
12 KiB
Python
289 lines
12 KiB
Python
import argparse
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import json
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from torch.utils.data import DataLoader
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from models import *
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from utils.datasets import *
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from utils.utils import *
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def test(cfg,
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data,
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weights=None,
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batch_size=16,
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img_size=416,
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conf_thres=0.001,
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iou_thres=0.6, # for nms
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save_json=False,
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single_cls=False,
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augment=False,
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model=None,
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dataloader=None):
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# Initialize/load model and set device
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if model is None:
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device = torch_utils.select_device(opt.device, batch_size=batch_size)
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verbose = opt.task == 'test'
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# Remove previous
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for f in glob.glob('test_batch*.png'):
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os.remove(f)
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# Initialize model
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model = Darknet(cfg, img_size)
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# Load weights
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attempt_download(weights)
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if weights.endswith('.pt'): # pytorch format
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model.load_state_dict(torch.load(weights, map_location=device)['model'])
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else: # darknet format
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load_darknet_weights(model, weights)
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# Fuse
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model.fuse()
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model.to(device)
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if device.type != 'cpu' and torch.cuda.device_count() > 1:
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model = nn.DataParallel(model)
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else: # called by train.py
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device = next(model.parameters()).device # get model device
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verbose = False
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# Configure run
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data = parse_data_cfg(data)
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nc = 1 if single_cls else int(data['classes']) # number of classes
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path = data['valid'] # path to test images
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names = load_classes(data['names']) # class names
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iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
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iouv = iouv[0].view(1) # comment for mAP@0.5:0.95
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niou = iouv.numel()
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# Dataloader
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if dataloader is None:
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dataset = LoadImagesAndLabels(path, img_size, batch_size, rect=True, single_cls=opt.single_cls)
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batch_size = min(batch_size, len(dataset))
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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seen = 0
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model.eval()
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_ = model(torch.zeros((1, 3, img_size, img_size), device=device)) if device.type != 'cpu' else None # run once
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coco91class = coco80_to_coco91_class()
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s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
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p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class = [], [], [], []
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for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
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imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
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targets = targets.to(device)
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nb, _, height, width = imgs.shape # batch size, channels, height, width
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whwh = torch.Tensor([width, height, width, height]).to(device)
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# Plot images with bounding boxes
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f = 'test_batch%g.png' % batch_i # filename
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if batch_i < 1 and not os.path.exists(f):
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
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# Disable gradients
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with torch.no_grad():
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# Run model
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t = torch_utils.time_synchronized()
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inf_out, train_out = model(imgs, augment=augment) # inference and training outputs
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t0 += torch_utils.time_synchronized() - t
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# Compute loss
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if hasattr(model, 'hyp'): # if model has loss hyperparameters
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loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls
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# Run NMS
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t = torch_utils.time_synchronized()
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output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) # nms
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t1 += torch_utils.time_synchronized() - t
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# Statistics per image
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for si, pred in enumerate(output):
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labels = targets[targets[:, 0] == si, 1:]
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nl = len(labels)
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tcls = labels[:, 0].tolist() if nl else [] # target class
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seen += 1
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if pred is None:
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if nl:
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stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
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continue
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# Append to text file
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# with open('test.txt', 'a') as file:
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# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
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# Clip boxes to image bounds
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clip_coords(pred, (height, width))
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# Append to pycocotools JSON dictionary
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if save_json:
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# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
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image_id = int(Path(paths[si]).stem.split('_')[-1])
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box = pred[:, :4].clone() # xyxy
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scale_coords(imgs[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
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box = xyxy2xywh(box) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(pred.tolist(), box.tolist()):
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jdict.append({'image_id': image_id,
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'category_id': coco91class[int(p[5])],
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'bbox': [round(x, 3) for x in b],
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'score': round(p[4], 5)})
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# Assign all predictions as incorrect
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correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
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if nl:
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detected = [] # target indices
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tcls_tensor = labels[:, 0]
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# target boxes
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tbox = xywh2xyxy(labels[:, 1:5]) * whwh
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# Per target class
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for cls in torch.unique(tcls_tensor):
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ti = (cls == tcls_tensor).nonzero().view(-1) # prediction indices
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pi = (cls == pred[:, 5]).nonzero().view(-1) # target indices
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# Search for detections
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if pi.shape[0]:
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# Prediction to target ious
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ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
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# Append detections
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for j in (ious > iouv[0]).nonzero():
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d = ti[i[j]] # detected target
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if d not in detected:
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detected.append(d)
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correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
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if len(detected) == nl: # all targets already located in image
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break
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# Append statistics (correct, conf, pcls, tcls)
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stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
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# Compute statistics
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stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
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if len(stats):
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p, r, ap, f1, ap_class = ap_per_class(*stats)
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if niou > 1:
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p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0] # [P, R, AP@0.5:0.95, AP@0.5]
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mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
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nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
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else:
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nt = torch.zeros(1)
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# Print results
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pf = '%20s' + '%10.3g' * 6 # print format
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print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
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# Print results per class
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if verbose and nc > 1 and len(stats):
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for i, c in enumerate(ap_class):
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print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))
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# Print speeds
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if verbose or save_json:
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t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (img_size, img_size, batch_size) # tuple
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print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
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# Save JSON
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if save_json and map and len(jdict):
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print('\nCOCO mAP with pycocotools...')
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
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with open('results.json', 'w') as file:
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json.dump(jdict, file)
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try:
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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except:
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print('WARNING: missing pycocotools package, can not compute official COCO mAP. See requirements.txt.')
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# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
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cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
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cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
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cocoEval.evaluate()
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cocoEval.accumulate()
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cocoEval.summarize()
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mf1, map = cocoEval.stats[:2] # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)
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# Return results
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maps = np.zeros(nc) + map
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for i, c in enumerate(ap_class):
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maps[c] = ap[i]
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return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(prog='test.py')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
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parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
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parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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opt = parser.parse_args()
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opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
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print(opt)
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# task = 'test', 'study', 'benchmark'
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if opt.task == 'test': # (default) test normally
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test(opt.cfg,
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opt.data,
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opt.weights,
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opt.batch_size,
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opt.img_size,
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opt.conf_thres,
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opt.iou_thres,
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opt.save_json,
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opt.single_cls,
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opt.augment)
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elif opt.task == 'benchmark': # mAPs at 320-608 at conf 0.5 and 0.7
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y = []
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for i in [320, 416, 512, 608]: # img-size
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for j in [0.5, 0.7]: # iou-thres
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t = time.time()
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r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, i, opt.conf_thres, j, opt.save_json)[0]
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y.append(r + (time.time() - t,))
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np.savetxt('benchmark.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt')
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elif opt.task == 'study': # Parameter study
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y = []
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x = np.arange(0.4, 0.9, 0.05) # iou-thres
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for i in x:
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t = time.time()
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r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, opt.img_size, opt.conf_thres, i, opt.save_json)[0]
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y.append(r + (time.time() - t,))
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np.savetxt('study.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt')
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# Plot
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fig, ax = plt.subplots(3, 1, figsize=(6, 6))
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y = np.stack(y, 0)
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ax[0].plot(x, y[:, 2], marker='.', label='mAP@0.5')
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ax[0].set_ylabel('mAP')
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ax[1].plot(x, y[:, 3], marker='.', label='mAP@0.5:0.95')
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ax[1].set_ylabel('mAP')
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ax[2].plot(x, y[:, -1], marker='.', label='time')
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ax[2].set_ylabel('time (s)')
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for i in range(3):
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ax[i].legend()
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ax[i].set_xlabel('iou_thr')
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fig.tight_layout()
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plt.savefig('study.jpg', dpi=200)
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