dest='lock_base', help='Lock base') parser.add_argument("--weights_only", action='store_true', default=False, dest='weights_only', help='Resume only weights from loaded model') parser.add_argument("--loc", default=None, type=str, help="Dataset location") args = parser.parse_args() height = 300 width = 300 cocoEvalSet = cd.CocoDetection(root=args.loc + "images/", annFile=args.loc + "annotations/instances_val2014.json", preprocess=Crop(size=(height, width))) cocoEvalLoader = DataLoader(cocoEvalSet, args.batch, shuffle=False, num_workers=8, pin_memory=True, collate_fn=cd.collate) cocoTrainSet = cd.CocoDetection(root=args.loc + "images/", annFile=args.loc + "annotations/instances_train2014.json", preprocess=Augment(size=(height, width))) cocoTrainLoader = DataLoader(cocoTrainSet,
print("Preparing validation dataset") width = 300 height = 300 import json with open('ssd/lib/data/coco_labels.json') as f: coco = json.load(f)["categories"] coco = [x["name"] for x in coco] small = ["person", "bicycle", "car", "motorcycle", "bus", "train", "truck"] head = ["head"] # surveillance dataset for SSD cocoEvalSet = cd.CocoDetection(root=args.loc + "images/", annFile=args.loc + "annotations/instances_val2014.json", preprocess=Crop(size=(height, width)), classes=small) evalLoader = DataLoader(cocoEvalSet, 1, shuffle=False, num_workers=1, pin_memory=True, collate_fn=cd.collate) # HollywoodHead dataset for SSDTC # hheadsTrainSet = hhd.HHeadsDetection(root=args.loc + "JPEGImages/", # annFile=args.loc+"annotations/val.json", # preprocess=CropChunk(size=(height, width), eval=True), # chunk=5,