211 lines
8.1 KiB
Python
211 lines
8.1 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(
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cfg,
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data_cfg,
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weights=None,
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batch_size=16,
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img_size=416,
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iou_thres=0.5,
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conf_thres=0.001,
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nms_thres=0.5,
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save_json=False,
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model=None
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):
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if model is None:
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device = torch_utils.select_device()
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# Initialize model
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model = Darknet(cfg, img_size).to(device)
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# Load 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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if torch.cuda.device_count() > 1:
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model = nn.DataParallel(model)
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else:
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device = next(model.parameters()).device # get model device
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# Configure run
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data_cfg = parse_data_cfg(data_cfg)
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nc = int(data_cfg['classes']) # number of classes
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test_path = data_cfg['valid'] # path to test images
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names = load_classes(data_cfg['names']) # class names
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# Dataloader
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dataset = LoadImagesAndLabels(test_path, img_size, batch_size)
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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num_workers=4,
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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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coco91class = coco80_to_coco91_class()
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print(('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP', 'F1'))
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loss, p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0., 0.
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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='Computing mAP')):
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targets = targets.to(device)
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imgs = imgs.to(device)
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_, _, height, width = imgs.shape # batch size, channels, height, width
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# Plot images with bounding boxes
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if batch_i == 0 and not os.path.exists('test_batch0.jpg'):
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plot_images(imgs=imgs, targets=targets, fname='test_batch0.jpg')
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# Run model
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inf_out, train_out = model(imgs) # inference and training outputs
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# Compute loss
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if hasattr(model, 'hyp'): # if model has loss hyperparameters
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loss_i, _ = compute_loss(train_out, targets, model)
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loss += loss_i.item()
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# Run NMS
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output = non_max_suppression(inf_out, conf_thres=conf_thres, nms_thres=nms_thres)
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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.Tensor(), torch.Tensor(), tcls))
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continue
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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]) # 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 di, d in enumerate(pred):
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jdict.append({
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'image_id': image_id,
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'category_id': coco91class[int(d[6])],
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'bbox': [float3(x) for x in box[di]],
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'score': float(d[4])
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})
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# Assign all predictions as incorrect
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correct = [0] * len(pred)
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if nl:
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detected = []
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tcls_tensor = labels[:, 0]
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# target boxes
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tbox = xywh2xyxy(labels[:, 1:5])
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tbox[:, [0, 2]] *= width
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tbox[:, [1, 3]] *= height
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# Search for correct predictions
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for i, (*pbox, pconf, pcls_conf, pcls) in enumerate(pred):
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# Break if all targets already located in image
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if len(detected) == nl:
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break
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# Continue if predicted class not among image classes
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if pcls.item() not in tcls:
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continue
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# Best iou, index between pred and targets
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m = (pcls == tcls_tensor).nonzero().view(-1)
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iou, bi = bbox_iou(pbox, tbox[m]).max(0)
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# If iou > threshold and class is correct mark as correct
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if iou > iou_thres and m[bi] not in detected: # and pcls == tcls[bi]:
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correct[i] = 1
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detected.append(m[bi])
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# Append statistics (correct, conf, pcls, tcls)
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stats.append((correct, pred[:, 4].cpu(), pred[:, 6].cpu(), tcls))
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# Compute statistics
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stats = [np.concatenate(x, 0) for x in list(zip(*stats))] # to numpy
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nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
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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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mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
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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), end='\n\n')
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# Print results per class
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if 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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# Save JSON
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if save_json and map and len(jdict):
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in 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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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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cocoGt = COCO('../coco/annotations/instances_val2014.json') # 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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map = cocoEval.stats[1] # update mAP to pycocotools mAP
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# Return results
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maps = np.zeros(nc)
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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 / len(dataloader)), 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('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
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parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
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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('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
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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('--img-size', type=int, default=416, help='inference size (pixels)')
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opt = parser.parse_args()
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print(opt)
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with torch.no_grad():
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mAP = test(
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opt.cfg,
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opt.data_cfg,
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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.iou_thres,
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opt.conf_thres,
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opt.nms_thres,
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opt.save_json
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)
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