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,
args.cache_path,
transform=train_transform,
target_transform=target_transform,
batch_size=args.batch_size)
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,
shuffle=True)
# logging.info("Prepare Validation datasets.")
# val_dataset = VIDDataset(args.datasets, args.cache_path, transform=test_transform,
# target_transform=target_transform, is_val=True)
# logging.info(val_dataset)
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,
args.cache_path,
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,
shuffle=True)
logging.info("Prepare Validation datasets.")
val_dataset = VIDDataset(args.datasets,
args.cache_path,
transform=test_transform,
target_transform=target_transform,
test_transform = TestTransform(
config.image_size, config.image_mean, config.image_std
)
# elif args.feature == "vgg19" or "resnet18":
# train_transform = TrainAugmentation(224, config.image_mean, config.image_std)
# target_transform = MatchPrior(
# config.priors, config.center_variance, config.size_variance, 0.5
# )
# test_transform = TestTransform(224, config.image_mean, config.image_std)
logging.info("Prepare training datasets.")
train_dataset = VIDDataset(
args.datasets,
args.cache_path,
transform=train_transform,
target_transform=target_transform,
batch_size=args.batch_size,
)
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, shuffle=True
)
logging.info("Prepare Validation datasets.")
val_dataset = VIDDataset(
args.datasets,
args.cache_path,