ray.init(num_cpus=2) else: ray.init(address=args.address) if args.backbone == "mobilenet_v2": from models.ssd_mobilenet_v2 import get_model, init_model else: from models.ssd_vgg16 import get_model, init_model ssd_log_path = io_utils.get_log_path(args.backbone) ssd_model_path = io_utils.get_model_path(args.backbone) hyper_params = train_utils.get_hyper_params(args.backbone) _, info = data_utils.get_dataset("voc/2007", "train+validation") _, voc_2012_info = data_utils.get_dataset("voc/2012", "train+validation") voc_2012_total_items = data_utils.get_total_item_size( voc_2012_info, "train+validation") train_total_items = data_utils.get_total_item_size(info, "train+validation") val_total_items = data_utils.get_total_item_size(info, "test") if args.with_voc12: train_total_items += voc_2012_total_items labels = data_utils.get_labels(info) labels = ["bg"] + labels hyper_params["total_labels"] = len(labels) step_size_train = train_utils.get_step_size(train_total_items, args.batch_size) step_size_val = train_utils.get_step_size(val_total_items, args.batch_size) num_train_steps = 10 if args.smoke_test else step_size_train
batch_size = 32 evaluate = False use_custom_images = False custom_image_path = "data/images/" backbone = args.backbone io_utils.is_valid_backbone(backbone) # if backbone == "mobilenet_v2": from models.ssd_mobilenet_v2 import get_model, init_model else: from models.ssd_vgg16 import get_model, init_model # hyper_params = train_utils.get_hyper_params(backbone) # test_data, info = data_utils.get_dataset("voc/2007", "test") total_items = data_utils.get_total_item_size(info, "test") labels = data_utils.get_labels(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)
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_split = "train[80%:]" test_data, info = data_utils.get_dataset("the300w_lp", test_split) total_items = data_utils.get_total_item_size(info, test_split) 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) model = blazeface.get_model(hyper_params) model_path = io_utils.get_model_path() model.load_weights(model_path) prior_boxes = bbox_utils.generate_prior_boxes( hyper_params["feature_map_shapes"], hyper_params["aspect_ratios"]) variances = hyper_params["variances"]
import random args = io_utils.handle_args() if args.handle_gpu: io_utils.handle_gpu_compatibility() batch_size = 32 epochs = 150 load_weights = False hyper_params = train_utils.get_hyper_params() train_split = "train[:80%]" val_split = "train[80%:]" train_data, info = data_utils.get_dataset("the300w_lp", train_split) val_data, _ = data_utils.get_dataset("the300w_lp", val_split) train_total_items = data_utils.get_total_item_size(info, train_split) val_total_items = data_utils.get_total_item_size(info, val_split) # 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(batch_size * 12).padded_batch( batch_size, padded_shapes=data_shapes, padding_values=padding_values) val_data = val_data.padded_batch(batch_size, padded_shapes=data_shapes,