parser.print_help(sys.stderr)
sys.exit(1)
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.")
datasets = []
for dataset_path in args.datasets:
if args.dataset_type == 'voc':
dataset = VOCDataset(dataset_path,
transform=train_transform,
target_transform=target_transform)
label_file = os.path.join(args.checkpoint_folder,
"voc-model-labels.txt")
store_labels(label_file, dataset.class_names)
num_classes = len(dataset.class_names)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(dataset_path,
transform=train_transform,
target_transform=target_transform,
dataset_type="train",
balance_data=args.balance_data)
label_file = os.path.join(args.checkpoint_folder,
"open-images-model-labels.txt")
store_labels(label_file, dataset.class_names)
logging.info(dataset)
recall = true_positive / num_true_cases
if use_2007_metric:
return measurements.compute_voc2007_average_precision(
precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
if __name__ == '__main__':
eval_path = pathlib.Path(args.eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = [name.strip() for name in open(args.label_file).readlines()]
if args.dataset_type == "voc":
dataset = VOCDataset(args.dataset, is_test=True)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(args.dataset, dataset_type="test")
true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(
dataset)
if args.net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif args.net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names),
return measurements.compute_voc2007_average_precision(
precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
COLORS = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)]
FONT = cv2.FONT_HERSHEY_SIMPLEX
if __name__ == '__main__':
eval_path = pathlib.Path(args.eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = [name.strip() for name in open(args.label_file).readlines()]
if args.dataset_type == "voc":
dataset = VOCDataset(args.dataset, is_test=True)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(args.dataset, dataset_type="test")
true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(
dataset)
if args.net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif args.net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names),
# create data transforms for train/test/val
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)
# load datasets (could be multiple)
logging.info("Prepare training datasets.")
datasets = []
for dataset_path in args.datasets:
if args.dataset_type == 'voc':
dataset = VOCDataset(dataset_path,
transform=train_transform,
target_transform=target_transform,
bg_transform=test_transform)
label_file = os.path.join(args.checkpoint_folder, "labels.txt")
store_labels(label_file, dataset.class_names)
num_classes = len(dataset.class_names)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(dataset_path,
transform=train_transform,
target_transform=target_transform,
dataset_type="train",
balance_data=args.balance_data)
label_file = os.path.join(args.checkpoint_folder, "labels.txt")
store_labels(label_file, dataset.class_names)
logging.info(dataset)
num_classes = len(dataset.class_names)
def train_network(dataset_path, model_path, net_type):
args.datasets = dataset_path
args.validation_dataset = dataset_path
args.checkpoint_folder = model_path
args.log_dir = os.path.join(args.checkpoint_folder, 'log')
args.net = net_type
timer = Timer()
logging.info(args)
if args.net == 'slim':
create_net = create_mb_tiny_fd
config = fd_config
elif args.net == 'RFB':
create_net = create_Mb_Tiny_RFB_fd
config = fd_config
else:
logging.fatal("The net type is wrong.")
parser.print_help(sys.stderr)
sys.exit(1)
train_transform = TrainAugmentation(config.image_size, config.image_mean,
config.image_std)
target_transform = MatchPrior(config.priors, config.center_variance,
config.size_variance, args.overlap_threshold)
test_transform = TestTransform(config.image_size, config.image_mean_test,
config.image_std)
if not os.path.exists(args.checkpoint_folder):
os.makedirs(args.checkpoint_folder)
logging.info("Prepare training datasets.")
datasets = []
# voc datasets
dataset = VOCDataset(dataset_path,
transform=train_transform,
target_transform=target_transform)
label_file = os.path.join(args.checkpoint_folder, "voc-model-labels.txt")
store_labels(label_file, dataset.class_names)
num_classes = len(dataset.class_names)
print('num_classes: ', num_classes)
logging.info(f"Stored labels into file {label_file}.")
# train_dataset = ConcatDataset(datasets)
train_dataset = dataset
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 = VOCDataset(args.validation_dataset,
transform=test_transform,
target_transform=target_transform,
is_test=True)
logging.info("validation dataset size: {}".format(len(val_dataset)))
val_loader = DataLoader(val_dataset,
args.batch_size,
num_workers=args.num_workers,
shuffle=False)
logging.info("Build network.")
net = create_net(num_classes)
timer.start("Load Model")
if args.resume:
logging.info(f"Resume from the model {args.resume}")
net.load(args.resume)
logging.info(
f'Took {timer.end("Load Model"):.2f} seconds to load the model.')
# add multigpu_train
if torch.cuda.device_count() >= 1:
cuda_index_list = [int(v.strip()) for v in args.cuda_index.split(",")]
net = nn.DataParallel(net, device_ids=cuda_index_list)
logging.info("use gpu :{}".format(cuda_index_list))
min_loss = -10000.0
last_epoch = -1
base_net_lr = args.base_net_lr if args.base_net_lr is not None else args.lr
extra_layers_lr = args.extra_layers_lr if args.extra_layers_lr is not None else args.lr
params = [{
'params': net.module.base_net.parameters(),
'lr': base_net_lr
}, {
'params':
itertools.chain(net.module.source_layer_add_ons.parameters(),
net.module.extras.parameters()),
'lr':
extra_layers_lr
}, {
'params':
itertools.chain(net.module.regression_headers.parameters(),
net.module.classification_headers.parameters())
}]
net.to(DEVICE)
criterion = MultiboxLoss(config.priors,
iou_threshold=args.iou_threshold,
neg_pos_ratio=5,
center_variance=0.1,
size_variance=0.2,
device=DEVICE,
num_classes=num_classes,
loss_type=args.loss_type)
if args.optimizer_type == "SGD":
optimizer = torch.optim.SGD(params,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer_type == "Adam":
optimizer = torch.optim.Adam(params, lr=args.lr)
logging.info("use Adam optimizer")
else:
logging.fatal(f"Unsupported optimizer: {args.scheduler}.")
parser.print_help(sys.stderr)
sys.exit(1)
logging.info(
f"Learning rate: {args.lr}, Base net learning rate: {base_net_lr}, " +
f"Extra Layers learning rate: {extra_layers_lr}.")
if args.optimizer_type != "Adam":
if args.scheduler == 'multi-step':
logging.info("Uses MultiStepLR scheduler.")
milestones = [int(v.strip()) for v in args.milestones.split(",")]
scheduler = MultiStepLR(optimizer,
milestones=milestones,
gamma=0.1,
last_epoch=last_epoch)
elif args.scheduler == 'poly':
logging.info("Uses PolyLR scheduler.")
else:
logging.fatal(f"Unsupported Scheduler: {args.scheduler}.")
parser.print_help(sys.stderr)
sys.exit(1)
logging.info(f"Start training from epoch {last_epoch + 1}.")
for epoch in range(last_epoch + 1, args.num_epochs):
if args.optimizer_type != "Adam":
if args.scheduler != "poly":
if epoch != 0:
scheduler.step()
train(train_loader,
net,
criterion,
optimizer,
device=DEVICE,
debug_steps=args.debug_steps,
epoch=epoch)
if args.scheduler == "poly":
adjust_learning_rate(optimizer, epoch)
logging.info("epoch: {} lr rate :{}".format(
epoch, optimizer.param_groups[0]['lr']))
if epoch % args.validation_epochs == 0 or epoch == args.num_epochs - 1:
logging.info("validation epoch: {} lr rate :{}".format(
epoch, optimizer.param_groups[0]['lr']))
val_loss, val_regression_loss, val_classification_loss = test(
val_loader, net, criterion, DEVICE)
logging.info(
f"Epoch: {epoch}, " + f"Validation Loss: {val_loss:.4f}, " +
f"Validation Regression Loss {val_regression_loss:.4f}, " +
f"Validation Classification Loss: {val_classification_loss:.4f}"
)
model_path = os.path.join(
args.checkpoint_folder,
f"{args.net}-Epoch-{epoch}-Loss-{val_loss:.4f}.pth")
net.module.save(model_path)
logging.info(f"Saved model {model_path}")
