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,