def dataset_creator(config):
opt = config["opt"]
hyper_params = config["hyper_params"]
train_data, _ = data_utils.get_dataset("voc/2007", "train+validation")
val_data, _ = data_utils.get_dataset("voc/2007", "test")
if opt.with_voc12:
voc_2012_data, _ = data_utils.get_dataset("voc/2012",
"train+validation")
train_data = train_data.concatenate(voc_2012_data)
img_size = hyper_params["img_size"]
train_data = train_data.map(lambda x: data_utils.preprocessing(
x, img_size, img_size, augmentation.apply))
val_data = val_data.map(
lambda x: data_utils.preprocessing(x, img_size, img_size))
data_shapes = data_utils.get_data_shapes()
padding_values = data_utils.get_padding_values()
train_data = train_data.shuffle(opt.batch_size * 4).padded_batch(
opt.batch_size,
padded_shapes=data_shapes,
padding_values=padding_values)
val_data = val_data.padded_batch(opt.batch_size,
padded_shapes=data_shapes,
padding_values=padding_values)
prior_boxes = bbox_utils.generate_prior_boxes(
hyper_params["feature_map_shapes"], hyper_params["aspect_ratios"])
ssd_train_feed = train_utils.generator(train_data, prior_boxes,
hyper_params)
ssd_val_feed = train_utils.generator(val_data, prior_boxes, hyper_params)
return ssd_train_feed, ssd_val_feed
if backbone == "mobilenet_v2":
from models.rpn_mobilenet_v2 import get_model
else:
from models.rpn_vgg16 import get_model
hyper_params = train_utils.get_hyper_params(backbone)
test_data, dataset_info = data_utils.get_dataset("voc/2007", "test")
labels = data_utils.get_labels(dataset_info)
labels = ["bg"] + labels
hyper_params["total_labels"] = len(labels)
img_size = hyper_params["img_size"]
data_types = data_utils.get_data_types()
data_shapes = data_utils.get_data_shapes()
padding_values = data_utils.get_padding_values()
if use_custom_images:
img_paths = data_utils.get_custom_imgs(custom_image_path)
total_items = len(img_paths)
test_data = tf.data.Dataset.from_generator(
lambda: data_utils.custom_data_generator(img_paths, img_size, img_size
), data_types, data_shapes)
else:
test_data = test_data.map(
lambda x: data_utils.preprocessing(x, img_size, img_size))
test_data = test_data.padded_batch(batch_size,
padded_shapes=data_shapes,
padding_values=padding_values)