def load_EPFL_dataset(args): """ Load EPFLDataset. Rerturns: validation dataset training dataset """ train_transform = TrainAugmentation(config.image_size, config.image_mean, config.image_std) val_transform = TestTransform(config.image_size, config.image_mean, config.image_std)#gebruiken voor validatie dataset target_transform = net.MatchPrior(config.priors, config.center_variance, config.size_variance, 0.5) train_dataset = EPFLDataset(args.datasets, args.cache_path, transform=train_transform, target_transform=target_transform, batch_size=args.batch_size) val_dataset = EPFLDataset(args.datasets, args.cache_path, transform=val_transform, target_transform=target_transform, batch_size=args.batch_size, is_val=True) return train_dataset, val_dataset
model_dict = pred_dec.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_net_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
pred_dec.load_state_dict(model_dict)
if __name__ == '__main__':
timer = Timer()
logging.info(args)
config = mobilenetv1_ssd_config #config file for priors etc.
train_transform = TrainAugmentation(config.image_size, config.image_mean, config.image_std)
target_transform = MatchPrior(config.priors, config.center_variance,
config.size_variance, 0.5)
test_transform = TestTransform(config.image_size, config.image_mean, config.image_std)
logging.info("Prepare training datasets.")
train_dataset = VIDDataset(args.datasets, transform=train_transform,
target_transform=target_transform)
label_file = os.path.join("models/", "vid-model-labels.txt")
store_labels(label_file, train_dataset._classes_names)
num_classes = len(train_dataset._classes_names)
logging.info(f"Stored labels into file {label_file}.")
logging.info("Train dataset size: {}".format(len(train_dataset)))
train_loader = DataLoader(train_dataset, args.batch_size,
num_workers=args.num_workers,
