ailia package¶
Classes¶
- class ailia.Net(stream=None, weight=None, env_id=- 1, num_thread=0, memory_mode=None, debug_log=False, enable_layer_fusion=True)¶
Bases:
object
wrapper class for ailia network model instance
- __init__(stream=None, weight=None, env_id=- 1, num_thread=0, memory_mode=None, debug_log=False, enable_layer_fusion=True)¶
constructor of ailia network model instance.
- Parameters
stream (str, numpy.ndarray) – network model file path (ex. “foobar.prototxt”) network model data for use onnx file, please specify None.
weight (str, numpy.ndarray) – network weight file path (ex. “foobar.caffemodel” “foobar.onnx” ) network weight data
env_id (int, optional, default:ENVIRONMENT_AUTO(-1)) –
environment id of ailia excecution. To retrieve env_id value, use
get_environment_count() / get_environment() pair
- or
get_gpu_environment_id() .
num_thread (int, optional, default: MULTITHREAD_AUTO(0)) –
number of threads. valid values:
MULTITHREAD_AUTO=0 [means systems’s logical processor count], 1 to 32.
memory_mode (int or None, optional, default: None) – memory management mode of ailia excecution. To retrieve memory_mode value, use get_memory_mode() .
debug_log (bool, optional, default: False) – enable trace logging and verbose log for ailia.
enable_layer_fusion (bool, optional, default: True) – enable layer fusion optimization.
- copy_blob_data(dst_idx, src_idx, src_net)¶
copy blobs inter Net object
- Parameters
dst_idx (int or str) – The destination blob index (int) or blob name (str).
src_idx (int or str) – The source blob index (int) or blob name (str).
src_net (Net or None) – If specifyed Net object, copy blob from src_net. Otherwise, copy from self.
- find_blob_index_by_name(name)¶
retrieve blob index by name.
- Parameters
name (str) – blob name.
- Returns
blob index.
- Return type
int
- get_blob_count()¶
get blob count.
- Returns
blob count.
- Return type
int
- get_blob_data(idx, buffer=None)¶
get the data of the blob specified by idx.
- Parameters
idx (int or str) – blob index (int) or blob name (str). valid values (int) : range(0, a.get_blob_count()) .
buffer (numpy.ndarray, optional) –
output blob buffer. (for the performance freak) if used, ailia doesn’t create new output buffer every call. requirements :
buffer.dtype is numpy.float32 . buffer.flags[‘C_CONTIGUOUS’] is True . buffer.shape is the same as a.get_blob_shape(idx) .
- Returns
output blob data.
- Return type
numpy.ndarray
- get_blob_name(idx)¶
get the name of the blob specified by idx.
- Parameters
idx (int) – blob index. valid values : range(0, a.get_blob_count()) .
- Returns
blob name.
- Return type
str
- get_blob_shape(idx)¶
get the shape of the blob specified by idx.
- Parameters
idx (int or str) – blob index (int) or blob name (str). valid values (int) : range(0, a.get_blob_count()) .
- Returns
blob shape.
- Return type
tuple of ints
- get_error_detail()¶
get error detail.
- Returns
error detail.
- Return type
str
- get_input_blob_list()¶
get input blob indices list.
- Returns
input blob indices.
- Return type
list(int)
- get_input_shape()¶
get input blob shape.
- Returns
input blob shape. (same as numpy’s shape)
- Return type
tuple of ints
- get_output_blob_list()¶
get output blob indices list.
- Returns
output blob indices.
- Return type
list(int)
- get_output_shape()¶
get output blob shape.
- Returns
output blob shape. (same as numpy’s shape)
- Return type
tuple of ints
- get_raw_pointer()¶
get raw instance of ailia
- Returns
raw instance of ailia.
- Return type
ctypes.c_void_p
- get_results()¶
get the list of output blobs data.
- Returns
output blob data.
- Return type
list(numpy.ndarray)
- get_selected_environment()¶
get current execution environment.
- Returns
id : int, used for create() function’s env_id argument. type : str, one of the following :
’CPU’, ‘BLAS’, ‘GPU’
name : str, detail description of execution environment. backend : str, one of the following :
’NONE’, ‘CUDA’, ‘MPS’, ‘VULKAN’
- propslist(str), is empty or contains one or more following:
’LOWPOWER’, ‘FP16’
- Return type
namedtuple(“Environment”)
- get_summary()¶
get, as a string, a summary of all layers and blobs.
- Returns
summary string.
- Return type
str
- predict(input, output=None)¶
run ailia network model.
- Parameters
input (numpy.ndarray or dict(str, numpy.ndarray) or sequence( numpy.ndarray )) –
input blob data. requirements :
input.shape is the same as a.get_input_shape() .
output (numpy.ndarray or sequence( numpy.ndarray ), optional) –
output blob buffer. (for the performance freak) if used, ailia doesn’t create new output buffer every call and get only top N (len(output)) blobs of model. requirements :
output.dtype is numpy.float32 . output.flags[‘C_CONTIGUOUS’] is True . output.shape is the same as a.get_output_shape() .
- Returns
output blob data. when input is a ndarray, return is single ndarray (return output[0]) when input is dict|sequence, return is list( ndarray )
- Return type
numpy.ndarray or list( numpy.ndarray )
- run(input, output=None)¶
run ailia network model.
- Parameters
input (numpy.ndarray or dict(str, numpy.ndarray) or sequence( numpy.ndarray )) –
input blob data. requirements :
input.shape is the same as a.get_input_shape() .
output (numpy.ndarray or sequence( numpy.ndarray ), optional) –
output blob buffer. (for the performance freak) if used, ailia doesn’t create new output buffer every call and get only top N (len(output)) blobs of model. requirements :
output.dtype is numpy.float32 . output.flags[‘C_CONTIGUOUS’] is True . output.shape is the same as a.get_output_shape() .
- Returns
list of output blob data. return is a list in any case.
- Return type
list( numpy.ndarray )
- set_input_blob_data(input, idx)¶
set input blob data. (for multiple input network model)
- Parameters
input (numpy.ndarray) –
input data. requirements :
input.shape is model’s acceptable shape .
idx (int or str) – blob index (int) or blob name (str). valid values (int) : range(0, a.get_blob_count())
See also
- set_input_blob_shape(shape, idx)¶
set input blob shape. (for multiple input network model)
- Parameters
shape (tuple of ints) – new input blob shape.
idx (int or str) – blob index (int) or blob name (str). valid values (int) : range(0, a.get_blob_count())
See also
- set_input_shape(shape)¶
change input blob shape.
- Parameters
shape (tuple of ints) – new input layer shape.
- set_profile_mode(mode=1)¶
change profile mode.
- Parameters
mode (int, optional, default=PROFILE_AVERAGE(1)) –
- valid values:
PROFILE_DISABLE(0) PROFILE_AVERAGE(1)
- update()¶
run ailia network model with pre stored input data.
using with a.set_input_blob_data() / a.get_results() ex.
# set input [A] idx = a.find_blob_index_by_name(net, “input_a”) a.set_input_blob_data(data_a, idx) # set other input [B] idx = a.find_blob_index_by_name(net, “input_b”) a.set_input_blob_data(data_b, idx) # run ailia network model a.update(t) # get result list_of_outputs = a.get_results()
- class ailia.Detector(stream_path, weight_path, category_count, env_id=- 1, num_thread=0, format=1, channel=0, range=3, algorithm=0, flags=0, debug_log=False, enable_layer_fusion=True, memory_mode=None)¶
Bases:
ailia.wrapper.Net
wrapper class for ailia object detector (YOLO style) instance
- __init__(stream_path, weight_path, category_count, env_id=- 1, num_thread=0, format=1, channel=0, range=3, algorithm=0, flags=0, debug_log=False, enable_layer_fusion=True, memory_mode=None)¶
constructor of ailia object detector instance.
- Parameters
stream_path (str,) – network model file path (ex. “foobar.prototxt”) for use onnx file, please specify None.
weight_path (str,) – network weight file path (ex. “foobar.caffemodel”)
category_count (int) – use same value as model training time.
env_id (int, optional, default:ENVIRONMENT_AUTO(-1)) –
environment id of ailia excecution. To retrieve env_id value, use
get_environment_count() / get_environment() pair
- or
get_gpu_environment_id() .
num_thread (int, optional, default: MULTITHREAD_AUTO(0)) –
number of threads. valid values:
MULTITHREAD_AUTO=0 [means systems’s logical processor count], 1 to 32.
format (int, optional, default=NETWORK_IMAGE_FORMAT_RGB(1)) –
channel (int, optional, default=NETWORK_IMAGE_CHANNEL_FIRST(0)) –
range (int, optional, default=NETWORK_IMAGE_RANGE_S_FP32(3)) – use network model’s expected input data format.
algorithm (int, optional, default=DETECTOR_ALGORITHM_YOLOV1(0)) –
- algorithm selector, use
DETECTOR_ALGORITHM_YOLOV1(0)
- or
DETECTOR_ALGORITHM_YOLOV2(1)
- or
DETECTOR_ALGORITHM_YOLOV3(2) .
- or
DETECTOR_ALGORITHM_YOLOV4(3) .
- or
DETECTOR_ALGORITHM_YOLOX(4) .
- or
DETECTOR_ALGORITHM_SSD(8) .
flags (int, optional, default=DETECTOR_FLAGS_NORMAL(0)) – reserved for future use.
debug_log (bool, optional, default: False) – enable trace logging and verbose log for ailia.
enable_layer_fusion (bool, optional, default: True) – enable layer fusion optimization.
memory_mode (int or None, optional, default: None) – memory management mode of ailia excecution. To retrieve memory_mode value, use get_memory_mode() .
- compute(image, threshold, iou)¶
run the ailia object detector with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
threshold (float) – object recognition threshold. an object whose probability is less than the threshold is undetected.
iou (float) – intersection over union threshold, used for non-maximum suppression.
- get_object(idx)¶
get a detected object detail specified by idx.
- Parameters
idx (int) – object index. vaild values : range(0, a.get_object_count())
- Returns
category : int, object category index. prob : float, object probability. x : float, object rectangle’s top-left x coordinate. y : float, object rectangle’s top-left y coordinate. w : float, object rectangle’s width. h : float, object rectangle’s height.
- Return type
namedtuple(“DetectedObject”)
- get_object_count()¶
get detected object count.
- Returns
number of objects.
- Return type
int
- run(image, threshold, iou)¶
run the ailia object detector with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
threshold (float) – object recognition threshold. an object whose probability is less than the threshold is undetected.
iou (float) – intersection over union threshold, used for non-maximum suppression.
- Returns
- numpy structured array(“DetectorObject”)
category : int, object category index. prob : float, object probability. box : numpy structured array (“DetecorRectangle”)
x : float, object rectangle’s top-left x coordinate. y : float, object rectangle’s top-left y coordinate. w : float, object rectangle’s width. h : float, object rectangle’s height.
- Return type
array of numpy structured array(“DetectorObject”)
- set_anchors(data)¶
sets the anchor information for YOLOv2 or other models.
- Parameters
data (numpy.ndarray) – extra data. anchors or biasis, etc.
- set_input_shape(input_width, input_height)¶
set model input image size (for YOLOv3)
- Parameters
input_width (int, input model image width) –
input_height (int, input model image height) –
- class ailia.Classifier(stream_path, weight_path, env_id=- 1, num_thread=0, format=0, channel=0, range=3, debug_log=False, enable_layer_fusion=True, memory_mode=None)¶
Bases:
ailia.wrapper.Net
wrapper class for ailia image classifier instance
- __init__(stream_path, weight_path, env_id=- 1, num_thread=0, format=0, channel=0, range=3, debug_log=False, enable_layer_fusion=True, memory_mode=None)¶
constructor of ailia image classifier instance.
- Parameters
stream_path (str,) – network model file path (ex. “foobar.prototxt”) for use onnx file, please specify None.
weight_path (str,) – network weight file path (ex. “foobar.caffemodel”)
env_id (int, optional, default:ENVIRONMENT_AUTO(-1)) –
environment id of ailia excecution. To retrieve env_id value, use
get_environment_count() / get_environment() pair
- or
get_gpu_environment_id() .
num_thread (int, optional, default: MULTITHREAD_AUTO(0)) –
number of threads. valid values:
MULTITHREAD_AUTO=0 [means systems’s logical processor count], 1 to 32.
format (int, optional, default=NETWORK_IMAGE_FORMAT_BGR(0)) –
channel (int, optional, default=NETWORK_IMAGE_CHANNEL_FIRST(0)) –
range (int, optional, default=NETWORK_IMAGE_RANGE_S_FP32(3)) – use network model’s expected input data format.
debug_log (bool, optional, default: False) – enable trace logging and verbose log for ailia.
enable_layer_fusion (bool, optional, default: True) – enable layer fusion optimization.
memory_mode (int or None, optional, default: None) – memory management mode of ailia excecution. To retrieve memory_mode value, use get_memory_mode() .
- compute(image, max_class_count=1)¶
run the ailia image classifier with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
max_class_count (int, optional, default=1) – maximum number of listed classes. finds N classes in decending order of probability.
- get_class(idx)¶
get a class information specified by idx.
- Parameters
idx (int) – class index. vaild values : range(0, a.get_class_count())
- Returns
category : int, class category index. prob : float, class probability.
- Return type
namedtuple(“ClassifierClass”)
- get_class_count()¶
get listed classes count.
- Returns
number of classes.
- Return type
int
- run(image, max_class_count=1)¶
run the ailia image classifier with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
max_class_count (int, optional, default=1) – maximum number of listed classes. finds N classes in decending order of probability.
- Returns
- numpy structured array(“ClassifierClass”)
category : int, class category index. prob : float, class probability.
- Return type
array of numpy structured array(“ClassifierClass”)
- class ailia.PoseEstimator(stream_path, weight_path, env_id=- 1, num_thread=0, algorithm=0, memory_mode=None, debug_log=False, enable_layer_fusion=True)¶
Bases:
ailia.wrapper.Net
ailia pose estimator instance wrapper class
- __init__(stream_path, weight_path, env_id=- 1, num_thread=0, algorithm=0, memory_mode=None, debug_log=False, enable_layer_fusion=True)¶
constructor of ailia pose stimator instance.
- Parameters
stream_path (str,) – network model file path (ex. “foobar.prototxt”) for use onnx file, please specify None.
weight_path (str,) – network weight file path (ex. “foobar.caffemodel”)
env_id (int, optional, default:ENVIRONMENT_AUTO(-1)) –
environment id of ailia excecution. To retrieve env_id value, use
get_environment_count() / get_environment() pair
- or
get_gpu_environment_id() .
num_thread (int, optional, default: MULTITHREAD_AUTO(0)) –
number of threads. valid values:
MULTITHREAD_AUTO=0 [means systems’s logical processor count], 1 to 32.
algorithm (int, optional, default=POSE_ALGORITHM_ACCULUS_POSE(0)) – algorithm selector, use ALGORITHM_*
debug_log (bool, optional, default: False) – enable trace logging and verbose log for ailia.
enable_layer_fusion (bool, optional, default: True) – enable layer fusion optimization.
memory_mode (int or None, optional, default: None) – memory management mode of ailia excecution. To retrieve memory_mode value, use get_memory_mode() .
- compute(image)¶
run the ailia pose stimator with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
- get_object_count()¶
get detected object count.
- Returns
number of objects.
- Return type
int
- get_object_hand(idx)¶
get a detected object detail specified by idx.
- Parameters
idx (int) – object index. vaild values : range(0, a.get_object_count())
- Returns
r –
- pointslist of namedtuple(“PoseEstimatorKeypoint”)
- namedtuple(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability.
- Return type
namedtuple(“PoseEstimatorObjectHand”)
- get_object_pose(idx)¶
get a detected object detail specified by idx.
- Parameters
idx (int) – object index. vaild values : range(0, a.get_object_count())
- Returns
- pointslist of namedtuple(“PoseEstimatorKeypoint”)
- namedtuple(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability. num_valid_points : int, number of valid key points id : int, person id angle_x : float, object angle angle_y : float, object angle angle_z : float, object angle
- Return type
namedtuple(“PoseEstimatorObjectPose”)
- get_object_up_pose(idx)¶
get a detected object detail specified by idx.
- Parameters
idx (int) – object index. vaild values : range(0, a.get_object_count())
- Returns
- pointslist of namedtuple(“PoseEstimatorKeypoint”)
- namedtuple(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability. num_valid_points : int, number of valid key points id : int, person id angle_x : float, object angle angle_y : float, object angle angle_z : float, object angle
- Return type
namedtuple(“PoseEstimatorObjectUpPose”)
- run(image, max_class_count=1)¶
run the ailia pose stimator with a input image.
- Parameters
image (numpy.ndarray) – input image data. expect a result of cv2.imread(img_path, cv2.IMREAD_UNCHANGED) .
- Returns
algorithm==POSE_ALGORITHM_ACCULUS_POSE or algorithm==POSE_ALGORITHM_OPEN_POSE
or algorithm==POSE_ALGORITHM_LW_HUMAN_POSE or algorithm==POSE_ALGORITHM_OPEN_POSE_SINGLE_SCALE
array of numpy structured array(“PoseEstimatorObjectPose”) –
- numpy structured array(“PoseEstimatorObjectPose”)
- pointsarray of numpy structured array(“PoseEstimatorKeypoint”)
- numpy strucuted array(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability. num_valid_points : int, number of valid key points id : int, person id angle_x : float, object angle angle_y : float, object angle angle_z : float, object angle
algorithm==POSE_ALGORITHM_ACCULUS_UPPOSE or algorithm==POSE_ALGORITHM_ACCULUS_UPPOSE_FPGA
array of numpy structured array(“PoseEstimatorObjectUpPose”) –
- numpy structured array(“PoseEstimatorObjectUpPose”)
- pointsarray of numpy structured array(“PoseEstimatorKeypoint”)
- numpy strucuted array(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability. num_valid_points : int, number of valid key points id : int, person id angle_x : float, object angle angle_y : float, object angle angle_z : float, object angle
algorithm==POSE_ALGORITHM_ACCULUS_HAND
array of numpy structured array(“PoseEstimatorObjectHand”) –
- numpy structured array(“PoseEstimatorObjectHand”)
- pointsarray of numpy structured array(“PoseEstimatorKeypoint”)
- numpy strucuted array(“PoseEstimatorKeypoint”)
x : float, keypoint position y : float, keypoint position z_local : float, keypoint position score : float, keypoint probablity interpolated : int, 0 or 1
total_score : float, sum of object probability.
- set_threshold(thre)¶
Functions¶
- ailia.set_temporary_cache_path(path)¶
set system cache path.
- ailia.get_environment_count()¶
get available environments count.
- Returns
available execution environments count
- Return type
int
See also
get_environmnet
- ailia.get_environment(idx)¶
get environment detail.
- Parameters
idx (int) – env_id. 0 to (get_environment_count()-1).
- Returns
execution environment detail. id : int, used for create() function’s env_id argument. type : str, one of the following :
’CPU’, ‘BLAS’, ‘GPU’
name : str, detail description of execution environment. backend : str, one of the following :
’NONE’, ‘CUDA’, ‘MPS’, ‘VULKAN’
- propslist(str), is empty or contains one or more following:
’LOWPOWER’, ‘FP16’
- Return type
namedtuple(“Environment”)
See also
- ailia.get_environment_list()¶
get environment detail.
- Returns
- namedtuple(“Environment”)
execution environment detail. id : int, used for create() function’s env_id argument. type : str, one of the following :
’CPU’, ‘BLAS’, ‘GPU’
name : str, detail description of execution environment. backend : str, one of the following :
’NONE’, ‘CUDA’, ‘MPS’, ‘VULKAN’
- propslist(str), is empty or contains one or more following:
’LOWPOWER’, ‘FP16’
- Return type
list of namedtuple(“Environment”)
- ailia.get_version()¶
get version string of ailia library.
- Returns
version string.
- Return type
str
- ailia.get_gpu_environment_id()¶
utility function to get GPU execution environment.
- Returns
first env_id of GPU execution environment.
- Return type
int
Note
if there were no GPU execution environment, this function returns ENVIRONMENT_AUTO(-1).
- ailia.get_memory_mode(reduce_constant=False, ignore_input_with_initializer=False, reduce_interstage=False, reuse_interstage=False)¶
get memory management mode combined argument.
- Returns
memory management mode. ( for ailia.Net() )
- Return type
int
- Parameters
reduce_constant (bool, optional, default=False) – free a constant intermediate blob.
ignore_input_with_initializer (bool, optional, default=False) – consider all initializer as constant. (even if they overlap with input)
reduce_interstage (bool, optional, default=False) – free an intermediate blob.
reuse_interstage (bool, optional, default=False) – reuse an available intermediate blob.
Objects¶
- class ailia.ClassifierClass(category, prob)¶
Bases:
tuple
- property category¶
Alias for field number 0
- property prob¶
Alias for field number 1
- class ailia.DetectorObject(category, prob, x, y, w, h)¶
Bases:
tuple
- property category¶
Alias for field number 0
- property h¶
Alias for field number 5
- property prob¶
Alias for field number 1
- property w¶
Alias for field number 4
- property x¶
Alias for field number 2
- property y¶
Alias for field number 3
- class ailia.Environment(id, type, name, backend, props)¶
Bases:
tuple
- property backend¶
Alias for field number 3
- property id¶
Alias for field number 0
- property name¶
Alias for field number 2
- property props¶
Alias for field number 4
- property type¶
Alias for field number 1
- class ailia.PoseEstimatorKeypoint(x, y, z_local, score, interpolated)¶
Bases:
tuple
- property interpolated¶
Alias for field number 4
- property score¶
Alias for field number 3
- property x¶
Alias for field number 0
- property y¶
Alias for field number 1
- property z_local¶
Alias for field number 2
- class ailia.PoseEstimatorObjectHand(points, total_score)¶
Bases:
tuple
- property points¶
Alias for field number 0
- property total_score¶
Alias for field number 1
- class ailia.PoseEstimatorObjectPose(points, total_score, num_valid_points, id, angle_x, angle_y, angle_z)¶
Bases:
tuple
- property angle_x¶
Alias for field number 4
- property angle_y¶
Alias for field number 5
- property angle_z¶
Alias for field number 6
- property id¶
Alias for field number 3
- property num_valid_points¶
Alias for field number 2
- property points¶
Alias for field number 0
- property total_score¶
Alias for field number 1
- class ailia.PoseEstimatorObjectUpPose(points, total_score, num_valid_points, id, angle_x, angle_y, angle_z)¶
Bases:
tuple
- property angle_x¶
Alias for field number 4
- property angle_y¶
Alias for field number 5
- property angle_z¶
Alias for field number 6
- property id¶
Alias for field number 3
- property num_valid_points¶
Alias for field number 2
- property points¶
Alias for field number 0
- property total_score¶
Alias for field number 1
Exceptions¶
- exception ailia.core.AiliaException¶
Bases:
Exception
Base class for exceptions of ailia
- exception ailia.core.AiliaInvalidArgumentException¶
Bases:
ailia.core.AiliaException
Incorrect argument
Please check argument of called API.
- exception ailia.core.AiliaFileIoException¶
Bases:
ailia.core.AiliaException
File access failed.
Please check file is exist or not, and check access permission.
- exception ailia.core.AiliaInvalidVersionException¶
Bases:
ailia.core.AiliaException
Incorrect struct version
Please check struct version that passed with API and please pass correct struct version.
- exception ailia.core.AiliaBrokenDataException¶
Bases:
ailia.core.AiliaException
A corrupt file was passed.
Please check model file are correct or not, and please pass correct model.
- exception ailia.core.AiliaResourceInsufficientException¶
Bases:
ailia.core.AiliaException
Insufficient system resource
Please check usage of system resource (e.g. thread). And please call API after release system resources.
- exception ailia.core.AiliaInvalidStateException¶
Bases:
ailia.core.AiliaException
The internal status of the ailia is incorrect.
Please check API document and API call steps.
- exception ailia.core.AiliaUnsupportNetException¶
Bases:
ailia.core.AiliaException
Unsupported network
Non supported model file was passed to wrapper functions (e.g. Detector). Please check document whether presented models are supported or not.
- exception ailia.core.AiliaInvalidLayerException¶
Bases:
ailia.core.AiliaException
Incorrect layer weight, parameter, or input or output shape
The layer of model had incorrect parameter or so on. Please call ailiaGetErrorDetail() and check detail message. And, please check model.
- exception ailia.core.AiliaInvalidParamException¶
Bases:
ailia.core.AiliaException
The content of the parameter file is invalid.
Please check parameter file are correct or not.
- exception ailia.core.AiliaNotFoundException¶
Bases:
ailia.core.AiliaException
The specified element was not found.
The specified element of passed name/index was not found. Please check the element are exisit on model or not.
- exception ailia.core.AiliaGpuUnsupportLayerException¶
Bases:
ailia.core.AiliaException
A layer parameter not supported by the GPU was given.
The layer or parameter that not supported by the GPU was given. Please check model file are correct or not and contact support desk that described on document.
- exception ailia.core.AiliaGpuErrorException¶
Bases:
ailia.core.AiliaException
Error during processing on the GPU
Please check the GPU driver are latest and VRAM are sufficient or not.
- exception ailia.core.AiliaUnimplementedException¶
Bases:
ailia.core.AiliaException
Unimplemented error
The called API are not available on current environment. Please contact support desk that described on document.
- exception ailia.core.AiliaPermissionDeniedException¶
Bases:
ailia.core.AiliaException
Operation not allowed
The called API are not allowed on this model (e.g. encrypted model are used.). Please check model file and change API call flow.
- exception ailia.core.AiliaExpiredException¶
Bases:
ailia.core.AiliaException
Model Expired
The model file are expired. Please re generate model with ailia_obfuscate_c.
- exception ailia.core.AiliaUnsettledShapeException¶
Bases:
ailia.core.AiliaException
The shape is not yet determined
The shape (e.g. output shape) are not determined. When called API that to get output shape, please set input shape and execute inference, then call API that to get output shape.
- exception ailia.core.AiliaDataRemovedException¶
Bases:
ailia.core.AiliaException
The information was not available from the application
The specified information was removed due to optimization. If you need the information, please disable optimization and call API.
- exception ailia.core.AiliaDataHiddenException¶
- exception ailia.core.AiliaLicenseNotFoundException¶
Bases:
ailia.core.AiliaException
No valid license found
The license file are required for trial version. Please contact support desk that described on document.
- exception ailia.core.AiliaLicenseBrokenException¶
Bases:
ailia.core.AiliaException
License is broken
The license file that are required for trial version are broken. Please contact support desk that described on document.
- exception ailia.core.AiliaLicenseExpiredException¶
Bases:
ailia.core.AiliaException
License expired
The license file that are required for trial version are expired. Please contact support desk that described on document.
- exception ailia.core.AiliaShapeHasExDimException¶
Bases:
ailia.core.AiliaException
Dimension of shape is 5 or more.
The called API are supported up to 4 dimension. Please replace API that described on API document.