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 input_fn(self):
transform_fn = lambda value: ObjectDetectorJson.transform_fn(
value, self.train_transformer, cache_type=self.cache_type)
def transform_batch_fn(value):
images = []
annotations = []
for val in value:
img, annot = transform_fn(val)
images.append(img)
annotations.append(annot)
return images, annotations
map_fn_batch = lambda value: tf.py_func(transform_batch_fn, [value],
(tf.float32, tf.string))
dataset = self.train_dataset.apply(
tf.contrib.data.shuffle_and_repeat(buffer_size=self.dataset_size))
dataset = dataset.batch(self.batch_size).map(
map_fn_batch, num_parallel_calls=self.num_parallel_calls)
dataset = dataset.prefetch(self.prefetch_size)
images, annotation = dataset.make_one_shot_iterator().get_next()
images.set_shape([self.batch_size] + list(self.input_shape))
return images, annotation
def sample_data(json_path, num_samples, input_shape, classes, seed=666):
if num_samples == 0:
return None
data, _ = ObjectDetectorJson.json_iterator(json_path, classes)
data = [x for x in data()]
# data = ObjectDetectorJson.convert_coco_to_toolbox_format(COCO(json_path), classes)
ObjectDetectorJson.init_cache(json_path, cache_type='NONE', classes=classes)
rng = random.Random(seed)
selected_items = rng.sample(range(len(data)), num_samples)
_, transform_param = MobileNetSSD.create_transform_parameters(*input_shape[:2])
transformer = AnnotatedDataTransformer(transform_param, is_training=False)
transform_fn = lambda value: ObjectDetectorJson.transform_fn(value, transformer, add_original_image=True)
return [transform_fn(data[i]) for i in selected_items]
