139 lines
5.6 KiB
Python
139 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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parser = argparse.ArgumentParser()
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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='path to model config file')
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parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
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parser.add_argument('-weights_path', type=str, default='weights/yolov3.pt', help='path to weights file')
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parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label 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('-n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
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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)
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cuda = torch.cuda.is_available()
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device = torch.device('cuda:0' if cuda else 'cpu')
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def main(opt):
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# Configure run
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data_config = parse_data_config(opt.data_config_path)
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nC = int(data_config['classes']) # number of classes (80 for COCO)
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if platform == 'darwin': # MacOS (local)
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test_path = data_config['valid']
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else: # linux (cloud, i.e. gcp)
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test_path = '../coco/5k.part'
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# Initiate model
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model = Darknet(opt.cfg, opt.img_size)
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# Load weights
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if opt.weights_path.endswith('.weights'): # darknet format
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load_weights(model, opt.weights_path)
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elif opt.weights_path.endswith('.pt'): # pytorch format
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checkpoint = torch.load(opt.weights_path, map_location='cpu')
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model.load_state_dict(checkpoint['model'])
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del checkpoint
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model.to(device).eval()
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# Get dataloader
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# dataset = load_images_with_labels(test_path)
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# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
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dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
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print('Compute mAP...')
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mAP = 0
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outputs, mAPs, 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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imgs = imgs.to(device)
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with torch.no_grad():
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output = model(imgs)
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output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
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# Compute average precision for each sample
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for sample_i in range(len(targets)):
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correct = []
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# Get labels for sample where width is not zero (dummies)
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annotations = targets[sample_i]
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# Extract detections
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detections = output[sample_i]
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if detections is None:
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# If there are no detections but there are annotations mask as zero AP
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if annotations.size(0) != 0:
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mAPs.append(0)
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continue
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# Get detections sorted by decreasing confidence scores
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detections = detections[np.argsort(-detections[:, 4])]
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# If no annotations add number of detections as incorrect
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if annotations.size(0) == 0:
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# correct.extend([0 for _ in range(len(detections))])
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mAPs.append(0)
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continue
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else:
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target_cls = annotations[:, 0]
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# Extract target boxes as (x1, y1, x2, y2)
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target_boxes = xywh2xyxy(annotations[:, 1:5])
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target_boxes *= opt.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] > opt.iou_thres and obj_pred == annotations[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 = 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 for this image
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mAP = AP.mean()
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# Append image mAP to list
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mAPs.append(mAP)
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mean_mAP = np.mean(mAPs)
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# Print image mAP and running mean mAP
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print('Image %d/%d AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, mean_mAP))
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# Print mAP per class
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classes = load_classes(opt.class_path) # 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]))
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# Print mAP
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print('Mean Average Precision: %.4f' % mean_mAP)
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return mean_mAP
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if __name__ == '__main__':
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mAP = main(opt)
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