def __init__(self,
batch_size,
input_shape,
json_path,
classes,
num_parallel_calls=2,
prefetch_size=2):
self.batch_size = batch_size
self.input_shape = input_shape
self.json_path = json_path
self.num_parallel_calls = num_parallel_calls
self.prefetch_size = prefetch_size
ObjectDetectorJson.init_cache(self.json_path,
cache_type='NONE',
classes=classes)
dataset, self.dataset_size = ObjectDetectorJson.create_dataset(
self.json_path, classes=classes)
_, self.transform_param = MobileNetSSD.create_transform_parameters(
*input_shape[:2])
self.transformer = AnnotatedDataTransformer(self.transform_param,
is_training=False)
print('Total evaluation steps: {}'.format(
math.ceil(self.dataset_size / self.batch_size)))
transform_fn = lambda value: ObjectDetectorJson.transform_fn(
value, self.transformer)
map_fn = lambda value: tf.py_func(transform_fn, [value],
(tf.float32, tf.string))
self.dataset = dataset.map(map_fn,
num_parallel_calls=num_parallel_calls)
self.dataset = self.dataset.batch(
self.batch_size).prefetch(prefetch_size)
def __init__(self,
batch_size,
input_shape,
json_path,
cache_type='NONE',
classes=['bg'],
fill_with_current_image_mean=True,
num_parallel_calls=4,
prefetch_size=16):
self.batch_size = batch_size
self.input_shape = input_shape
self.json_path = json_path
self.cache_type = cache_type
self.num_parallel_calls = num_parallel_calls
self.prefetch_size = prefetch_size
self.classes = classes
ObjectDetectorJson.init_cache(self.json_path,
cache_type,
classes=classes)
self.train_dataset, self.dataset_size = ObjectDetectorJson.create_dataset(
self.json_path, classes)
self.train_transform_param, _ = MobileNetSSD.create_transform_parameters(
input_shape[0], input_shape[1], fill_with_current_image_mean)
self.train_transformer = AnnotatedDataTransformer(
self.train_transform_param, is_training=True)
