_, 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
num_eval_steps = 10 if args.smoke_test else step_size_val
trainer = TFTrainer(model_creator=model_creator,
data_creator=dataset_creator,
num_replicas=args.num_replicas,
use_gpu=args.use_gpu,
verbose=True,
config={
"batch_size": args.batch_size,
"fit_config": {
"steps_per_epoch": num_train_steps,
},
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, evaluate=evaluate))
test_data = test_data.padded_batch(batch_size,
padded_shapes=data_shapes,
padding_values=padding_values)
ssd_model = get_model(hyper_params)
ssd_model_path = io_utils.get_model_path(backbone)
ssd_model.load_weights(ssd_model_path)
prior_boxes = bbox_utils.generate_prior_boxes(
hyper_params["feature_map_shapes"], hyper_params["aspect_ratios"])
ssd_decoder_model = get_decoder_model(ssd_model, prior_boxes, hyper_params)
step_size = train_utils.get_step_size(total_items, batch_size)
pred_bboxes, pred_labels, pred_scores = ssd_decoder_model.predict(
test_data, steps=step_size, verbose=1)
if evaluate:
eval_utils.evaluate_predictions(test_data, pred_bboxes, pred_labels,
pred_scores, labels, batch_size)
else:
drawing_utils.draw_predictions(test_data, pred_bboxes, pred_labels,
pred_scores, labels, batch_size)