144 lines
5.6 KiB
Python
144 lines
5.6 KiB
Python
import argparse
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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,
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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=0.3,
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nms_thres=0.45
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):
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device = torch_utils.select_device()
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# Configure run
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data_cfg_dict = parse_data_cfg(data_cfg)
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nC = int(data_cfg_dict['classes']) # number of classes (80 for COCO)
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test_path = data_cfg_dict['valid']
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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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if weights.endswith('.pt'): # pytorch format
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model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
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else: # darknet format
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load_darknet_weights(model, weights)
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model.to(device).eval()
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# Get dataloader
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# dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size) # pytorch
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dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size)
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mean_mAP, mean_R, mean_P = 0.0, 0.0, 0.0
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
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AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
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for batch_i, (imgs, targets) in enumerate(dataloader):
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output = model(imgs.to(device))
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output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
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# Compute average precision for each sample
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for sample_i, (labels, detections) in enumerate(zip(targets, output)):
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correct = []
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if detections is None:
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# If there are no detections but there are labels mask as zero AP
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if labels.size(0) != 0:
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mAPs.append(0), mR.append(0), mP.append(0)
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continue
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# Get detections sorted by decreasing confidence scores
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detections = detections.cpu().numpy()
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detections = detections[np.argsort(-detections[:, 4])]
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# If no labels add number of detections as incorrect
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if labels.size(0) == 0:
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# correct.extend([0 for _ in range(len(detections))])
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mAPs.append(0), mR.append(0), mP.append(0)
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continue
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else:
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target_cls = labels[:, 0]
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# Extract target boxes as (x1, y1, x2, y2)
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target_boxes = xywh2xyxy(labels[:, 1:5]) * img_size
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detected = []
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for *pred_bbox, conf, obj_conf, obj_pred in detections:
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pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
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# Compute iou with target boxes
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iou = bbox_iou(pred_bbox, target_boxes)
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# Extract index of largest overlap
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best_i = np.argmax(iou)
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# If overlap exceeds threshold and classification is correct mark as correct
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if iou[best_i] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
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correct.append(1)
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detected.append(best_i)
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else:
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correct.append(0)
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# Compute Average Precision (AP) per class
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AP, AP_class, R, P = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6],
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target_cls=target_cls)
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# Accumulate AP per class
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AP_accum_count += np.bincount(AP_class, minlength=nC)
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AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
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# Compute mean AP across all classes in this image, and append to image list
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mAPs.append(AP.mean())
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mR.append(R.mean())
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mP.append(P.mean())
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# Means of all images
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mean_mAP = np.mean(mAPs)
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mean_R = np.mean(mR)
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mean_P = np.mean(mP)
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# Print image mAP and running mean mAP
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print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))
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# Print mAP per class
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')
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classes = load_classes(data_cfg_dict['names']) # Extracts class labels from file
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for i, c in enumerate(classes):
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print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i] + 1E-16)))
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# Return mAP
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return mean_mAP, mean_R, mean_P
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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=32, help='size of each image batch')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
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parser.add_argument('--weights', type=str, default='weights/yolov3.pt', 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=0.3, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
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parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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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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)
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