211 lines
8.8 KiB
Python
211 lines
8.8 KiB
Python
import argparse
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import json
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from pathlib import Path
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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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save_json=False
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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)
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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, seen = 0.0, 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, jdict = \
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[], [], [], [], [], [], [], [], []
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AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
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coco91class = coco80_to_coco91_class()
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for batch_i, (imgs, targets, paths, shapes) 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 si, (labels, detections) in enumerate(zip(targets, output)):
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seen += 1
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if detections is None:
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# If there are labels but no detections mark 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 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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box = torch.from_numpy(detections[:, :4]).clone() # xyxy
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scale_coords(img_size, 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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# add to json dictionary
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for di, d in enumerate(detections):
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jdict.append({
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'image_id': int(Path(paths[si]).stem.split('_')[-1]),
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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': float3(d[4] * d[5])
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})
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# If no labels add number of detections as incorrect
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correct = []
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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,
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conf=detections[:, 4],
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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) % (seen, 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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# Save JSON
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if save_json:
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.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 detections 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().accumulate().summarize()
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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.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=0.3, 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('--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='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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opt.save_json
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)
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# Image Total P R mAP # YOLOv3 320
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# 32 5000 0.66 0.597 0.591
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# 64 5000 0.664 0.62 0.604
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# 96 5000 0.653 0.627 0.614
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# 128 5000 0.639 0.623 0.607
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# 160 5000 0.642 0.63 0.616
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# 192 5000 0.651 0.636 0.621
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# Image Total P R mAP # YOLOv3 416
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# 32 5000 0.635 0.581 0.57
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# 64 5000 0.63 0.591 0.578
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# 96 5000 0.661 0.632 0.622
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# 128 5000 0.659 0.632 0.623
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# 160 5000 0.665 0.64 0.633
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# 192 5000 0.66 0.637 0.63
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# Image Total P R mAP # YOLOv3 608
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# 32 5000 0.653 0.606 0.591
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# 64 5000 0.653 0.635 0.625
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# 96 5000 0.655 0.642 0.633
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# 128 5000 0.667 0.651 0.642
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# 160 5000 0.663 0.645 0.637
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# 192 5000 0.663 0.643 0.634
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