Object Detection Workflow via API¶
How to evaluate common workflows via the checkmaite API
This notebook demonstrates a full end to end workflow of the checkmaite backend. The general steps are:
- Create wrapper objects as necessary (not all wrappers are needed for every tool)
- Create a model wrapper
- Create a dataset wrapper
- Create a metric wrapper
- Create configuration to define analyses to run
- Generate "capability" object(s)
- Run analyses
All checkmaite wrappers are maite-compliant (https://mit-ll-ai-technology.github.io/maite/).
IMPORTANT:
- Since we are using mini test datasets, the values presented in the results may not have significant meaning. Use full datasets with models trained on similar data to view accurate results.
- This notebook requires a dev install since it utilizes data from the test suite.
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from checkmaite import cache_path
from checkmaite._docs import create_expandable_output
import json
import logging
import os
from pathlib import Path
import torch
import warnings
logger = logging.getLogger(__name__)
BASE_DIR = Path.cwd().parents[1]
EXAMPLE_DATA_DIR = BASE_DIR / "tests/data_for_tests"
EXAMPLE_COCO_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('coco_resized_val2017').resolve()
EXAMPLE_YOLO_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('yolo_dataset').resolve()
EXAMPLE_VISDRONE_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('visdrone_dataset')
warnings.filterwarnings("ignore", category=UserWarning)
from checkmaite import cache_path
from checkmaite._docs import create_expandable_output
import json
import logging
import os
from pathlib import Path
import torch
import warnings
logger = logging.getLogger(__name__)
BASE_DIR = Path.cwd().parents[1]
EXAMPLE_DATA_DIR = BASE_DIR / "tests/data_for_tests"
EXAMPLE_COCO_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('coco_resized_val2017').resolve()
EXAMPLE_YOLO_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('yolo_dataset').resolve()
EXAMPLE_VISDRONE_DATASET_DIR = EXAMPLE_DATA_DIR.joinpath('visdrone_dataset')
warnings.filterwarnings("ignore", category=UserWarning)
/home/runner/work/checkmaite/checkmaite/.venv/lib/python3.10/site-packages/xaitk_saliency/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources
Set cache path¶
Checkmaite has a default output cache path, but it can be overriden as needed. Note, the cache will only be written if the argument use_prediction_and_evaluation_cache=True is passed into the capability run.
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print(f'The default cache path is {cache_path()}')
# set the cache path
cache_path('tscache')
print(f'The current cache path is {cache_path()}')
print(f'The default cache path is {cache_path()}')
# set the cache path
cache_path('tscache')
print(f'The current cache path is {cache_path()}')
The default cache path is /home/runner/.cache/checkmaite The current cache path is /home/runner/work/checkmaite/checkmaite/docs/get-started/tscache
Define task¶
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task = 'object_detection'
task = 'object_detection'
Create a model wrapper¶
Model wrappers hold the model object, weights and metadata.
Checkmaite has two object detection model wrappers - torchvision and visdrone. Below are examples of each of the available configuration options.
Torchvision OD Model using default weights from Torchvision¶
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from checkmaite.core.object_detection.models import TorchvisionODModel
model_name = "ssdlite320_mobilenet_v3_large"
model_wrapper = TorchvisionODModel(model_name=model_name)
from checkmaite.core.object_detection.models import TorchvisionODModel
model_name = "ssdlite320_mobilenet_v3_large"
model_wrapper = TorchvisionODModel(model_name=model_name)
Downloading: "https://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pth" to /home/runner/.cache/torch/hub/checkpoints/ssdlite320_mobilenet_v3_large_coco-a79551df.pth
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Torchvision OD Model using custom weights and config¶
Note: The configuration file is a JSON formatted text file that contains index2label as a key with its value being a dictionary of {index: category label} for the model e.g. {0: '__background__', 1: 'person', 2: 'bicycle' ...}.
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# save metadata and state_dict from previous cell to disk
config_path = cache_path() / "my_model.json"
pickle_path = cache_path() / "my_model.pt"
os.makedirs(os.path.dirname(config_path), exist_ok=True)
with open(config_path, "w") as f:
json.dump({"index2label": model_wrapper.index2label}, f)
_ = torch.save(model_wrapper.model.state_dict(), pickle_path)
# extra kwargs to pass through to torchvision model object
kwargs = {}
torch_model_wrapper = TorchvisionODModel(
model_name=model_name,
weights_path=pickle_path,
config_path=config_path,
model_id="arbitraryidnumber",
**kwargs,
)
# save metadata and state_dict from previous cell to disk
config_path = cache_path() / "my_model.json"
pickle_path = cache_path() / "my_model.pt"
os.makedirs(os.path.dirname(config_path), exist_ok=True)
with open(config_path, "w") as f:
json.dump({"index2label": model_wrapper.index2label}, f)
_ = torch.save(model_wrapper.model.state_dict(), pickle_path)
# extra kwargs to pass through to torchvision model object
kwargs = {}
torch_model_wrapper = TorchvisionODModel(
model_name=model_name,
weights_path=pickle_path,
config_path=config_path,
model_id="arbitraryidnumber",
**kwargs,
)
VisDrone OD Model using weights from Kitware's data server¶
Visdrone architecture options are resnet18, resnet50, and res2net50. Custom weights on these models are not currently supported.
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from checkmaite.core.object_detection.models import VisdroneODModel
# specify directory for the model weights to be downloaded
download_weights_dir = 'visdrone_model_weights'
visdrone_model_wrapper = VisdroneODModel(
arch='resnet18',
model_pickle_dir=download_weights_dir,
model_id='1234',
)
from checkmaite.core.object_detection.models import VisdroneODModel
# specify directory for the model weights to be downloaded
download_weights_dir = 'visdrone_model_weights'
visdrone_model_wrapper = VisdroneODModel(
arch='resnet18',
model_pickle_dir=download_weights_dir,
model_id='1234',
)
/home/runner/work/checkmaite/checkmaite/.venv/lib/python3.10/site-packages/smqtk_detection/impls/detect_image_objects/centernet.py:897: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
@autocast()
Create dataset wrapper¶
Dataset wrappers control the access to the dataset and contain metadata about the dataset and about individual images.
Checkmaite has three dataset wrappers for object detection - coco, yolo and visdrone. Below are examples of loading each type.
COCO dataset¶
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from checkmaite.core.object_detection.dataset_loaders import CocoDetectionDataset
coco_dataset_wrapper = CocoDetectionDataset(
root=str(EXAMPLE_COCO_DATASET_DIR),
ann_file=str(EXAMPLE_COCO_DATASET_DIR.joinpath('instances_val2017_resized_6.json')),
)
coco_dataset_wrapper_single = CocoDetectionDataset(
root=str(EXAMPLE_COCO_DATASET_DIR),
ann_file=str(EXAMPLE_COCO_DATASET_DIR.joinpath('single_image.json')),
)
from checkmaite.core.object_detection.dataset_loaders import CocoDetectionDataset
coco_dataset_wrapper = CocoDetectionDataset(
root=str(EXAMPLE_COCO_DATASET_DIR),
ann_file=str(EXAMPLE_COCO_DATASET_DIR.joinpath('instances_val2017_resized_6.json')),
)
coco_dataset_wrapper_single = CocoDetectionDataset(
root=str(EXAMPLE_COCO_DATASET_DIR),
ann_file=str(EXAMPLE_COCO_DATASET_DIR.joinpath('single_image.json')),
)
YOLO dataset¶
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from checkmaite.core.object_detection.dataset_loaders import YoloDetectionDataset
CURDIR = os.getcwd()
os.chdir(EXAMPLE_YOLO_DATASET_DIR.parents[2]) # the yolo test metadata file is written relative to the root pkg dir
yolo_dataset_wrapper = YoloDetectionDataset(
yaml_dataset=str(EXAMPLE_YOLO_DATASET_DIR.joinpath('dataset.yaml')),
ann_dir=str(EXAMPLE_YOLO_DATASET_DIR.joinpath('ann_dir')),
)
os.chdir(CURDIR)
from checkmaite.core.object_detection.dataset_loaders import YoloDetectionDataset
CURDIR = os.getcwd()
os.chdir(EXAMPLE_YOLO_DATASET_DIR.parents[2]) # the yolo test metadata file is written relative to the root pkg dir
yolo_dataset_wrapper = YoloDetectionDataset(
yaml_dataset=str(EXAMPLE_YOLO_DATASET_DIR.joinpath('dataset.yaml')),
ann_dir=str(EXAMPLE_YOLO_DATASET_DIR.joinpath('ann_dir')),
)
os.chdir(CURDIR)
Visdrone dataset¶
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from checkmaite.core.object_detection.dataset_loaders import VisdroneDetectionDataset
visdrone_dataset_wrapper = VisdroneDetectionDataset(
root=str(EXAMPLE_VISDRONE_DATASET_DIR),
)
from checkmaite.core.object_detection.dataset_loaders import VisdroneDetectionDataset
visdrone_dataset_wrapper = VisdroneDetectionDataset(
root=str(EXAMPLE_VISDRONE_DATASET_DIR),
)
Create metric wrapper¶
Metric wrappers provide standardized access to metric algorithms across the pydata ecosystem.
Checkmaite has one object detection metric wrapper - mean average precision (mAP).
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from checkmaite.core.object_detection.metrics import map50_torch_metric_factory
metric_wrapper = map50_torch_metric_factory()
metric_wrapper.metadata['id'] = metric_wrapper.return_key
from checkmaite.core.object_detection.metrics import map50_torch_metric_factory
metric_wrapper = map50_torch_metric_factory()
metric_wrapper.metadata['id'] = metric_wrapper.return_key
/home/runner/work/checkmaite/checkmaite/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm
Instantiate and run each Capability¶
As each capability is run, we collect slide data for Gradient PowePoint generation and add the results to our markdown report.
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# collect all the report slides in a list (to be convert to ppt after all stages are run)
slides = []
# Collect all the report MD strings in a single object (to be rendered and saved after all stages are executed)
report_md = ""
# collect all the report slides in a list (to be convert to ppt after all stages are run)
slides = []
# Collect all the report MD strings in a single object (to be rendered and saved after all stages are executed)
report_md = ""
MAITE - Baseline evaluation¶
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%%time
from checkmaite.core.object_detection import MaiteEvaluation
# instantiate capability object
capability = MaiteEvaluation()
# run analysis for this capability
maite_eval_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper],
metrics=[metric_wrapper], models=[visdrone_model_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
maite_eval_run.outputs
%%time
from checkmaite.core.object_detection import MaiteEvaluation
# instantiate capability object
capability = MaiteEvaluation()
# run analysis for this capability
maite_eval_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper],
metrics=[metric_wrapper], models=[visdrone_model_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
maite_eval_run.outputs
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Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /home/runner/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth
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MaiteEvaluationOutputs(overall_metric_name='map_50', result={'map_50': 0.16278350353240967}, class_metrics=None)
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# collect the markdown outputs for the final report
_md = maite_eval_run.collect_md_report(threshold=0.5)
report_md += _md
# collect the markdown outputs for the final report
_md = maite_eval_run.collect_md_report(threshold=0.5)
report_md += _md
NRTK¶
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%%time
from checkmaite.core.object_detection import NrtkRobustness, NrtkRobustnessConfig
nrtk_config_dict = {
"name": "natural_robustness_",
"perturber_factory": {
"type": "nrtk.impls.perturb_image_factory.PerturberLinspaceFactory",
"nrtk.impls.perturb_image_factory.PerturberLinspaceFactory": {
"perturber": "nrtk.impls.perturb_image.photometric.enhance.BrightnessPerturber",
"theta_key": "factor",
"start": 0,
"stop": 2,
"num": 11
}
}
}
nrtk_config = NrtkRobustnessConfig(**nrtk_config_dict)
# instantiate capability object
capability = NrtkRobustness()
# run analysis for this capability
nrtk_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper],
metrics=[metric_wrapper], models=[visdrone_model_wrapper], config=nrtk_config)
# view results
nrtk_run.outputs
%%time
from checkmaite.core.object_detection import NrtkRobustness, NrtkRobustnessConfig
nrtk_config_dict = {
"name": "natural_robustness_",
"perturber_factory": {
"type": "nrtk.impls.perturb_image_factory.PerturberLinspaceFactory",
"nrtk.impls.perturb_image_factory.PerturberLinspaceFactory": {
"perturber": "nrtk.impls.perturb_image.photometric.enhance.BrightnessPerturber",
"theta_key": "factor",
"start": 0,
"stop": 2,
"num": 11
}
}
}
nrtk_config = NrtkRobustnessConfig(**nrtk_config_dict)
# instantiate capability object
capability = NrtkRobustness()
# run analysis for this capability
nrtk_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper],
metrics=[metric_wrapper], models=[visdrone_model_wrapper], config=nrtk_config)
# view results
nrtk_run.outputs
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Out[14]:
NrtkRobustnessOutputs(perturbations=[{'map_50': tensor(0.)}, {'map_50': tensor(0.1287)}, {'map_50': tensor(0.1555)}, {'map_50': tensor(0.1583)}, {'map_50': tensor(0.1593)}, {'map_50': tensor(0.1628)}, {'map_50': tensor(0.1634)}, {'map_50': tensor(0.1121)}, {'map_50': tensor(0.1080)}, {'map_50': tensor(0.0740)}, {'map_50': tensor(0.0377)}], return_key='map_50')
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# collect the markdown outputs for the final report
_md = nrtk_run.collect_md_report(threshold=0.15)
report_md += _md
# collect the markdown outputs for the final report
_md = nrtk_run.collect_md_report(threshold=0.15)
report_md += _md
XAITK¶
XAITK should only be run on single images or a datasets with only a few images due to memory constraints
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%%time
from checkmaite.core.object_detection import XaitkExplainable, XaitkExplainableConfig
xaitk_config_dict = {
'name': 'saliency_XAITKApp_0',
'saliency_generator': {
'type': 'xaitk_saliency.impls.gen_object_detector_blackbox_sal.drise.DRISEStack',
'xaitk_saliency.impls.gen_object_detector_blackbox_sal.drise.DRISEStack': {
'n': 20,
's': 7,
'p1': 0.7,
'seed': 42,
'threads': 8,
'fill': [95, 96, 93],
},
},
'img_batch_size': 1,
}
xaitk_config = XaitkExplainableConfig(**xaitk_config_dict)
# instantiate capability object
capability = XaitkExplainable()
# run analysis for this capability
xaitk_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper],
models=[visdrone_model_wrapper], config=xaitk_config) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(xaitk_run.outputs.results)
%%time
from checkmaite.core.object_detection import XaitkExplainable, XaitkExplainableConfig
xaitk_config_dict = {
'name': 'saliency_XAITKApp_0',
'saliency_generator': {
'type': 'xaitk_saliency.impls.gen_object_detector_blackbox_sal.drise.DRISEStack',
'xaitk_saliency.impls.gen_object_detector_blackbox_sal.drise.DRISEStack': {
'n': 20,
's': 7,
'p1': 0.7,
'seed': 42,
'threads': 8,
'fill': [95, 96, 93],
},
},
'img_batch_size': 1,
}
xaitk_config = XaitkExplainableConfig(**xaitk_config_dict)
# instantiate capability object
capability = XaitkExplainable()
# run analysis for this capability
xaitk_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper],
models=[visdrone_model_wrapper], config=xaitk_config) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(xaitk_run.outputs.results)
CPU times: user 17min 36s, sys: 55.2 s, total: 18min 31s Wall time: 4min 42s
Out[16]:
Output: [XaitkExplainableOutputDatum(img=array([[[103, 86, 90, ..., 133, 76, 32],
[ 97, 86, 96...
Show full output
[XaitkExplainableOutputDatum(img=array([[[103, 86, 90, ..., 133, 76, 32],
[ 97, 86, 96, ..., 132, 73, 30],
[ 91, 93, 112, ..., 128, 65, 27],
...,
[142, 145, 149, ..., 71, 70, 58],
[143, 143, 145, ..., 69, 67, 55],
[147, 146, 144, ..., 65, 62, 50]],
[[110, 92, 93, ..., 111, 60, 20],
[104, 92, 99, ..., 110, 57, 18],
[ 99, 99, 116, ..., 106, 49, 15],
...,
[105, 108, 114, ..., 55, 54, 42],
[106, 106, 110, ..., 53, 51, 39],
[110, 109, 109, ..., 49, 46, 34]],
[[ 76, 58, 64, ..., 90, 44, 6],
[ 70, 58, 68, ..., 89, 41, 4],
[ 62, 63, 83, ..., 85, 34, 3],
...,
[ 76, 79, 84, ..., 39, 39, 27],
[ 77, 77, 80, ..., 37, 36, 24],
[ 81, 80, 79, ..., 33, 31, 19]]], dtype=uint8), img_id=397133, sal_maps=array([[[0.73583566, 0.71669788, 0.69797372, ..., 0.81159578,
0.79826973, 0.78532143],
[0.72339502, 0.7039148 , 0.68482894, ..., 0.79709829,
0.78286057, 0.76903759],
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[0.81886433, 0.8236424 , 0.82840042, ..., 0.87173539,
0.87070252, 0.87007717],
[0.81984818, 0.82395273, 0.82803635, ..., 0.8664898 ,
0.86546287, 0.86485729]],
[[0.66021177, 0.65647278, 0.65284615, ..., 0.75884935,
0.75875867, 0.75867055],
[0.65168824, 0.64747126, 0.64338333, ..., 0.77101745,
0.7701616 , 0.76933067],
[0.65567433, 0.65112193, 0.64670306, ..., 0.77769098,
0.77641154, 0.77517044],
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[0.87552573, 0.8661626 , 0.85683803, ..., 0.77732993,
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[0.88711775, 0.87739337, 0.86770975, ..., 0.77055186,
0.76776151, 0.76501185],
[0.89794368, 0.88781088, 0.87772971, ..., 0.76399144,
0.76091728, 0.75790512]]]), boxes=array([[ 1.05104170e+01, 5.15532913e+01, 1.33938751e+01,
5.60910454e+01],
[ 1.09937372e+01, 5.11520309e+01, 1.32135525e+01,
5.55736732e+01],
[ 1.13124838e+01, 5.11054840e+01, 1.29004135e+01,
5.46808853e+01],
[-8.64006579e-03, 5.23186264e+01, 2.04660940e+00,
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[ 1.07265129e+01, 5.12717590e+01, 1.31815729e+01,
5.57578697e+01],
[ 8.39193573e+01, 4.27776260e+01, 8.93475342e+01,
5.45522614e+01],
[ 6.46544993e-02, 3.50821724e+01, 2.57715416e+00,
4.44285889e+01],
[ 3.13385606e+00, 4.64070854e+01, 8.90424252e+00,
5.65555382e+01],
[ 1.05104170e+01, 5.15532913e+01, 1.33938751e+01,
5.60910454e+01],
[ 2.38158054e+01, 4.89004974e+01, 2.57173271e+01,
5.46983910e+01]], dtype=float32), labels=array([10, 1, 2, 4, 3, 1, 4, 4, 7, 1], dtype=int32), scores=array([0.12683488, 0.11664329, 0.1119276 , 0.09387628, 0.09288689,
0.09023701, 0.08923897, 0.08018478, 0.06184567, 0.05857491],
dtype=float32)), XaitkExplainableOutputDatum(img=array([[[ 29, 43, 24, ..., 83, 74, 77],
[ 32, 49, 30, ..., 73, 66, 68],
[ 39, 56, 40, ..., 86, 75, 73],
...,
[ 92, 108, 110, ..., 157, 155, 153],
[ 90, 107, 109, ..., 158, 157, 156],
[ 89, 106, 108, ..., 154, 154, 154]],
[[ 28, 42, 24, ..., 76, 67, 70],
[ 31, 48, 30, ..., 65, 55, 60],
[ 36, 56, 41, ..., 75, 62, 62],
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[ 93, 109, 111, ..., 160, 158, 156],
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[ 90, 107, 109, ..., 157, 157, 157]],
[[ 0, 14, 0, ..., 50, 41, 44],
[ 0, 18, 2, ..., 42, 33, 37],
[ 3, 22, 9, ..., 55, 43, 42],
...,
[ 97, 113, 115, ..., 165, 163, 161],
[ 95, 112, 114, ..., 166, 165, 164],
[ 94, 111, 113, ..., 162, 162, 162]]], dtype=uint8), img_id=174482, sal_maps=array([[[0.8374786 , 0.81162551, 0.78662887, ..., 0.75765337,
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[0.53082199, 0.52807454, 0.5253384 , ..., 0.75300307,
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0.74706069, 0.74771459]],
[[0.92350457, 0.91433343, 0.90523945, ..., 0.85672022,
0.85898898, 0.86119343],
[0.92486504, 0.91578918, 0.90675655, ..., 0.85472548,
0.85696683, 0.85914289],
[0.92470818, 0.91581069, 0.90692 , ..., 0.84988839,
0.85208422, 0.85421426],
...,
[0.79937761, 0.7973916 , 0.79541376, ..., 0.89983865,
0.89966726, 0.8997583 ],
[0.80032981, 0.79858678, 0.79685105, ..., 0.89722483,
0.89657382, 0.89621856],
[0.80302218, 0.801716 , 0.80041647, ..., 0.89419156,
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[[0.60672692, 0.61311512, 0.61932595, ..., 0.57513053,
0.57067001, 0.56633595],
[0.61243118, 0.61998729, 0.62733016, ..., 0.56734949,
0.56393556, 0.56062107],
[0.61037758, 0.61913241, 0.62764426, ..., 0.56141627,
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...,
[0.60838742, 0.60963671, 0.61088085, ..., 0.61177576,
0.62018984, 0.62831112],
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0.6221834 , 0.63038833],
[0.60309622, 0.60508264, 0.60705893, ..., 0.61462929,
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...,
[[0.92012428, 0.91031198, 0.90061109, ..., 0.81064169,
0.80528888, 0.80008782],
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0.83377848, 0.82812145],
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0.81353705, 0.8082093 ],
...,
[0.72846182, 0.71767833, 0.70693924, ..., 0.68891756,
0.66936446, 0.64972884],
[0.74103557, 0.73163194, 0.72226774, ..., 0.68778982,
0.66830273, 0.64875961],
[0.75385607, 0.74589228, 0.73796909, ..., 0.68653003,
0.66704304, 0.64750774]],
[[0.92607822, 0.9143119 , 0.90264228, ..., 0.63533442,
0.63785031, 0.64029488],
[0.93131431, 0.92011899, 0.90895912, ..., 0.61332298,
0.61682664, 0.62022826],
[0.93406828, 0.92297764, 0.91187881, ..., 0.60368558,
0.60742017, 0.61104284],
...,
[0.71359517, 0.71162776, 0.70966845, ..., 0.84122619,
0.84361331, 0.84612255],
[0.70132495, 0.69981788, 0.69831713, ..., 0.84411462,
0.84600758, 0.84806583],
[0.69320599, 0.69261855, 0.6920341 , ..., 0.84578134,
0.84685555, 0.84813193]],
[[0.83131049, 0.82282217, 0.81478991, ..., 0.92914179,
0.92293183, 0.91689791],
[0.83326798, 0.82440523, 0.81602269, ..., 0.93737908,
0.93074492, 0.924304 ],
[0.83969953, 0.8309802 , 0.82273855, ..., 0.93947462,
0.93295546, 0.92663166],
...,
[0.8679461 , 0.87006598, 0.87217713, ..., 0.87053695,
0.87666252, 0.88270368],
[0.86937829, 0.87166977, 0.87395164, ..., 0.86785051,
0.87360199, 0.87923508],
[0.86936171, 0.8716586 , 0.87394377, ..., 0.86569635,
0.87122172, 0.87660477]]]), boxes=array([[34.01518 , 46.95242 , 47.390068 , 73.2211 ],
[46.030727 , 68.799385 , 48.809704 , 75.4235 ],
[48.14583 , 11.77381 , 63.57277 , 20.897865 ],
[39.330173 , 12.487972 , 40.83822 , 13.6109295 ],
[41.800766 , 12.510104 , 48.049355 , 13.9037075 ],
[23.255257 , 12.378244 , 30.335093 , 17.775957 ],
[-0.27954406, 24.023792 , 0.8058734 , 34.593338 ],
[39.30819 , 12.535889 , 40.542027 , 13.274016 ],
[39.330173 , 12.487972 , 40.83822 , 13.6109295 ],
[82.73774 , 29.655819 , 99.29725 , 36.440285 ]],
dtype=float32), labels=array([ 9, 1, 5, 10, 4, 5, 4, 2, 1, 4], dtype=int32), scores=array([0.1598954 , 0.13264188, 0.10606429, 0.09237047, 0.09229495,
0.09128813, 0.08791782, 0.08738072, 0.07688579, 0.06960505],
dtype=float32)), XaitkExplainableOutputDatum(img=array([[[ 92, 97, 104, ..., 102, 90, 68],
[ 98, 103, 109, ..., 90, 62, 49],
[104, 109, 115, ..., 73, 56, 65],
...,
[109, 101, 147, ..., 16, 19, 26],
[109, 92, 137, ..., 14, 17, 23],
[111, 85, 129, ..., 16, 18, 24]],
[[102, 107, 113, ..., 104, 92, 70],
[108, 113, 118, ..., 92, 64, 51],
[114, 119, 124, ..., 75, 58, 67],
...,
[ 94, 88, 134, ..., 17, 20, 27],
[ 94, 79, 124, ..., 15, 18, 24],
[ 96, 72, 116, ..., 17, 19, 25]],
[[ 94, 99, 108, ..., 99, 87, 65],
[100, 105, 113, ..., 87, 59, 46],
[106, 111, 119, ..., 72, 55, 64],
...,
[ 89, 82, 128, ..., 9, 12, 19],
[ 89, 73, 118, ..., 7, 10, 16],
[ 91, 66, 110, ..., 9, 11, 17]]], dtype=uint8), img_id=403385, sal_maps=array([[[0.73430499, 0.71593428, 0.69822975, ..., 0.89555223,
0.89492622, 0.89431795],
[0.71907527, 0.70029384, 0.68219188, ..., 0.90818407,
0.90622183, 0.90431674],
[0.72837077, 0.71041523, 0.69309059, ..., 0.9077762 ,
0.9060401 , 0.90435601],
...,
[0.8875474 , 0.8898669 , 0.89217686, ..., 0.78488733,
0.77930832, 0.77392524],
[0.89331744, 0.89486295, 0.89640198, ..., 0.78793357,
0.78259667, 0.77749079],
[0.89693374, 0.89746394, 0.89799143, ..., 0.79142111,
0.7865033 , 0.78184613]],
[[0.86290631, 0.85890814, 0.85503256, ..., 0.89844269,
0.9008067 , 0.90310368],
[0.85593998, 0.85242939, 0.84902291, ..., 0.89070932,
0.89275247, 0.89473612],
[0.8480686 , 0.84489978, 0.84182195, ..., 0.8838439 ,
0.88536218, 0.88683497],
...,
[0.73470238, 0.72965381, 0.72462602, ..., 0.85798943,
0.85746163, 0.85728383],
[0.73254032, 0.72754304, 0.72256671, ..., 0.85886822,
0.85811197, 0.85772885],
[0.73436446, 0.72986925, 0.72539695, ..., 0.85859375,
0.85729056, 0.85637527]],
[[0.84501806, 0.84150315, 0.83816718, ..., 0.71518372,
0.71603035, 0.71685298],
[0.84910702, 0.84500949, 0.84111381, ..., 0.71430502,
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0.71520932, 0.71438032],
...,
[0.70711586, 0.7021312 , 0.69716707, ..., 0.76046259,
0.76017057, 0.75998527],
[0.70107955, 0.69659399, 0.69212723, ..., 0.75768681,
0.75705379, 0.75653988],
[0.69419095, 0.69013567, 0.68610105, ..., 0.7552791 ,
0.75443059, 0.75371378]],
...,
[[0.88148771, 0.87774834, 0.87416935, ..., 0.83668394,
0.83779753, 0.83887956],
[0.88004361, 0.87648123, 0.87307448, ..., 0.83640855,
0.83738227, 0.83832763],
[0.87928084, 0.87566206, 0.87220038, ..., 0.83676652,
0.83733152, 0.83787958],
...,
[0.8734404 , 0.87151365, 0.86959483, ..., 0.80774301,
0.80614939, 0.80462527],
[0.87433458, 0.8723723 , 0.87041824, ..., 0.80725831,
0.80522604, 0.80328785],
[0.87387945, 0.87174462, 0.86962068, ..., 0.80709598,
0.80475336, 0.80252788]],
[[0.56584791, 0.56752612, 0.56936682, ..., 0.57815105,
0.56756761, 0.55728421],
[0.57284036, 0.57522654, 0.57777052, ..., 0.57095013,
0.56145876, 0.55224386],
[0.57250491, 0.57569467, 0.57903586, ..., 0.56595374,
0.55752828, 0.54935531],
...,
[0.66350441, 0.6589035 , 0.65432152, ..., 0.61360692,
0.62188689, 0.62993061],
[0.66033545, 0.65599364, 0.65167003, ..., 0.61152091,
0.61958929, 0.62740496],
[0.65907683, 0.65521973, 0.65138229, ..., 0.60885743,
0.61650869, 0.62389695]],
[[0.88813966, 0.88413067, 0.88037338, ..., 0.81095923,
0.81087721, 0.81079749],
[0.88455023, 0.88113587, 0.87796268, ..., 0.81842456,
0.81764987, 0.81689775],
[0.87608133, 0.87282842, 0.8698214 , ..., 0.83353873,
0.83137109, 0.82926841],
...,
[0.83720264, 0.83074815, 0.82432024, ..., 0.84534535,
0.84955844, 0.85418548],
[0.83749441, 0.83134956, 0.82523048, ..., 0.84188121,
0.84516922, 0.84887535],
[0.83729184, 0.83142979, 0.82559761, ..., 0.83860138,
0.84106724, 0.84395809]]]), boxes=array([[36.386284, 46.55569 , 45.687042, 71.72837 ],
[39.2736 , 63.00829 , 40.58273 , 66.21602 ],
[22.33765 , 69.80428 , 23.7634 , 73.18231 ],
[36.379147, 46.900017, 45.971405, 72.31116 ],
[81.565704, 12.413587, 86.06958 , 12.727262],
[66.44861 , 80.71587 , 75.88858 , 87.241066],
[94.45952 , 67.15327 , 96.32519 , 71.75887 ],
[37.072685, 46.715473, 45.89576 , 71.56147 ],
[54.814392, 12.415692, 59.081673, 12.731646],
[81.29707 , 12.442648, 85.08391 , 12.702289]], dtype=float32), labels=array([6, 1, 1, 9, 4, 5, 1, 4, 4, 6], dtype=int32), scores=array([0.16991429, 0.11433139, 0.11175622, 0.10948152, 0.08980269,
0.08690882, 0.07337615, 0.07067224, 0.06658924, 0.06560928],
dtype=float32))]
In [17]:
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# collect the markdown outputs for the final report
_md = xaitk_run.collect_md_report(threshold=0)
report_md += _md
# collect the markdown outputs for the final report
_md = xaitk_run.collect_md_report(threshold=0)
report_md += _md
Dataeval - Bias¶
In [18]:
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%%time
from checkmaite.core.object_detection import DataevalBias
# instantiate capability object
capability = DataevalBias()
# run analysis for this test stage
bias_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
%%time
from checkmaite.core.object_detection import DataevalBias
# instantiate capability object
capability = DataevalBias()
# run analysis for this test stage
bias_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
CPU times: user 1.66 s, sys: 765 ms, total: 2.42 s Wall time: 1.19 s
In [19]:
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# collect the markdown outputs for the final report
_md = bias_run.collect_md_report(threshold=0)
report_md += _md
# collect the markdown outputs for the final report
_md = bias_run.collect_md_report(threshold=0)
report_md += _md
In [20]:
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# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(bias_run.outputs.balance)
# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(bias_run.outputs.balance)
Out[20]:
Output: balance=shape: (7, 2)
┌───────────────┬───────────────┐
│ Factor ┆ Balance Score │
│ --- ...
Show full output
balance=shape: (7, 2) ┌───────────────┬───────────────┐ │ Factor ┆ Balance Score │ │ --- ┆ --- │ │ cat ┆ f64 │ ╞═══════════════╪═══════════════╡ │ class_label ┆ 1.0 │ │ coco_url ┆ 0.768982 │ │ date_captured ┆ 0.768982 │ │ flickr_url ┆ 0.768982 │ │ height ┆ 0.0 │ │ license ┆ 0.722007 │ │ width ┆ 0.0 │ └───────────────┴───────────────┘ factors=shape: (30, 4) ┌──────────┬───────────────┬──────────┬───────────────┐ │ factor1 ┆ factor2 ┆ mi_value ┆ is_correlated │ │ --- ┆ --- ┆ --- ┆ --- │ │ cat ┆ cat ┆ f64 ┆ bool │ ╞══════════╪═══════════════╪══════════╪═══════════════╡ │ coco_url ┆ date_captured ┆ 1.0 ┆ true │ │ coco_url ┆ flickr_url ┆ 1.0 ┆ true │ │ coco_url ┆ height ┆ 0.0 ┆ false │ │ coco_url ┆ license ┆ 1.0 ┆ true │ │ coco_url ┆ width ┆ 0.0 ┆ false │ │ … ┆ … ┆ … ┆ … │ │ width ┆ coco_url ┆ 0.0 ┆ false │ │ width ┆ date_captured ┆ 0.0 ┆ false │ │ width ┆ flickr_url ┆ 0.0 ┆ false │ │ width ┆ height ┆ 0.0 ┆ false │ │ width ┆ license ┆ 0.0 ┆ false │ └──────────┴───────────────┴──────────┴───────────────┘ classwise=shape: (168, 4) ┌────────────┬───────────────┬──────────┬───────────────┐ │ class_name ┆ factor_name ┆ mi_value ┆ is_imbalanced │ │ --- ┆ --- ┆ --- ┆ --- │ │ cat ┆ cat ┆ f64 ┆ bool │ ╞════════════╪═══════════════╪══════════╪═══════════════╡ │ 0 ┆ class_label ┆ 1.0 ┆ true │ │ 0 ┆ coco_url ┆ 0.536974 ┆ true │ │ 0 ┆ date_captured ┆ 0.536974 ┆ true │ │ 0 ┆ flickr_url ┆ 0.536974 ┆ true │ │ 0 ┆ height ┆ 0.0 ┆ false │ │ … ┆ … ┆ … ┆ … │ │ truck ┆ date_captured ┆ 0.254577 ┆ false │ │ truck ┆ flickr_url ┆ 0.254577 ┆ false │ │ truck ┆ height ┆ 0.0 ┆ false │ │ truck ┆ license ┆ 0.179137 ┆ false │ │ truck ┆ width ┆ 0.0 ┆ false │ └────────────┴───────────────┴──────────┴───────────────┘ image_classwise=image_metadata=
In [21]:
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create_expandable_output(bias_run.outputs.coverage)
create_expandable_output(bias_run.outputs.coverage)
Out[21]:
Output: total=6 uncovered_indices=array([5]) critical_value_radii=array([1.19614583, 1.32189666, 1.20200342,...
Show full output
total=6 uncovered_indices=array([5]) critical_value_radii=array([1.19614583, 1.32189666, 1.20200342, 1.16464018, 1.18295105,
1.48536802]) coverage_radius=1.4036323373164372 image=
In [22]:
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create_expandable_output(bias_run.outputs.diversity)
create_expandable_output(bias_run.outputs.diversity)
Out[22]:
Output: factors=shape: (7, 2)
┌───────────────┬─────────────────┐
│ Factor ┆ Diversity Index │
│ --- ...
Show full output
factors=shape: (7, 2) ┌───────────────┬─────────────────┐ │ Factor ┆ Diversity Index │ │ --- ┆ --- │ │ cat ┆ f64 │ ╞═══════════════╪═════════════════╡ │ class_label ┆ 0.377201 │ │ coco_url ┆ 0.766635 │ │ date_captured ┆ 0.766635 │ │ flickr_url ┆ 0.766635 │ │ height ┆ 0.0 │ │ license ┆ 0.834304 │ │ width ┆ 0.0 │ └───────────────┴─────────────────┘ classwise=shape: (144, 4) ┌────────────┬───────────────┬─────────────────┬──────────────────┐ │ class_name ┆ factor_name ┆ diversity_value ┆ is_low_diversity │ │ --- ┆ --- ┆ --- ┆ --- │ │ cat ┆ cat ┆ f64 ┆ bool │ ╞════════════╪═══════════════╪═════════════════╪══════════════════╡ │ 0 ┆ coco_url ┆ 0.145455 ┆ true │ │ 0 ┆ date_captured ┆ 0.145455 ┆ true │ │ 0 ┆ flickr_url ┆ 0.145455 ┆ true │ │ 0 ┆ height ┆ 0.0 ┆ true │ │ 0 ┆ license ┆ 0.316742 ┆ true │ │ … ┆ … ┆ … ┆ … │ │ bear ┆ date_captured ┆ 0.0 ┆ true │ │ bear ┆ flickr_url ┆ 0.0 ┆ true │ │ bear ┆ height ┆ 0.0 ┆ true │ │ bear ┆ license ┆ 0.0 ┆ true │ │ bear ┆ width ┆ 0.0 ┆ true │ └────────────┴───────────────┴─────────────────┴──────────────────┘ image=
Dataeval - Feasibility¶
NOTE: Currently non-functional (checkmaite Issue #181)
In [23]:
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# %%time
# from checkmaite.object_detection.test_stages import DatasetFeasibilityTestStage
# # instantiate test stage object
# test_stage = DatasetFeasibilityTestStage()
# # populate test_stage with data
# test_stage.load_dataset(visdrone_dataset_wrapper, dataset_id='dataset1')
# test_stage.load_threshold(0.5)
# # run analysis for this test stage
# feasibility_run = test_stage.run(use_stage_cache=False) # NOTE: `use_stage_cache=True` will bypass the compute and load cached results if available
# # collect the slides for the final report
# stage_slides = test_stage.collect_report_consumables()
# # add to overall slide list
# slides += stage_slides
# # view results
# feasibility_run.outputs
# %%time
# from checkmaite.object_detection.test_stages import DatasetFeasibilityTestStage
# # instantiate test stage object
# test_stage = DatasetFeasibilityTestStage()
# # populate test_stage with data
# test_stage.load_dataset(visdrone_dataset_wrapper, dataset_id='dataset1')
# test_stage.load_threshold(0.5)
# # run analysis for this test stage
# feasibility_run = test_stage.run(use_stage_cache=False) # NOTE: `use_stage_cache=True` will bypass the compute and load cached results if available
# # collect the slides for the final report
# stage_slides = test_stage.collect_report_consumables()
# # add to overall slide list
# slides += stage_slides
# # view results
# feasibility_run.outputs
Dataeval - Cleaning¶
In [24]:
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%%time
from checkmaite.core.object_detection import DataevalCleaning
# instantiate capability object
capability = DataevalCleaning()
# run analysis for this capability
cleaning_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
create_expandable_output(cleaning_run.outputs)
%%time
from checkmaite.core.object_detection import DataevalCleaning
# instantiate capability object
capability = DataevalCleaning()
# run analysis for this capability
cleaning_run = capability.run(use_cache=False, datasets=[visdrone_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
create_expandable_output(cleaning_run.outputs)
CPU times: user 282 ms, sys: 3.91 ms, total: 286 ms Wall time: 285 ms
Out[24]:
Output: duplicates=DataevalCleaningDuplicatesOutputs(exact=None, near=None) image_outliers={} image_stats=Da...
Show full output
duplicates=DataevalCleaningDuplicatesOutputs(exact=None, near=None) image_outliers={} image_stats=DataevalCleaningStatsOutputs(source_index=[SourceIndex(0), SourceIndex(1), SourceIndex(2)], object_count=[0, 0, 0], image_count=3, invalid_box_count=[0, 0, 0], dim_stats=DataevalCleaningDimensionStatsOutputs(offset_x=array([0., 0., 0.], dtype=float32), offset_y=array([0., 0., 0.], dtype=float32), width=array([455., 455., 455.], dtype=float32), height=array([256., 256., 256.], dtype=float32), channels=array([3, 3, 3]), size=array([116480., 116480., 116480.], dtype=float32), aspect_ratio=array([0.43736264, 0.43736264, 0.43736264], dtype=float32), depth=array([8, 8, 8]), center=array([[227.5, 128. ],
[227.5, 128. ],
[227.5, 128. ]], dtype=float32), distance_center=array([0., 0., 0.], dtype=float32), distance_edge=array([0., 0., 0.], dtype=float32), invalid_box=array([False, False, False])), vis_stats=DataevalCleaningVisualStatsOutputs(brightness=array([0.3254902, 0.2784314, 0.4 ], dtype=float32), contrast=array([2.059774 , 2.1144278, 1.8110795], dtype=float32), darkness=array([0.62352943, 0.6392157 , 0.7764706 ], dtype=float32), sharpness=array([51.474777, 50.25128 , 52.113888], dtype=float32), percentiles=array([[0. , 0.3254902 , 0.47843137, 0.62352943, 1. ],
[0. , 0.2784314 , 0.44705883, 0.6392157 , 1. ],
[0. , 0.4 , 0.58431375, 0.7764706 , 1. ]],
dtype=float32), missing=array([0., 0., 0.], dtype=float32), zeros=array([0.00159684, 0.00123054, 0.00318223], dtype=float32)), ratio_stats=None) label_stats=DataevalCleaningLabelStatsOutputs(label_counts_per_class={4: 33, 10: 93, 2: 68, 1: 24, 8: 19, 7: 5, 3: 6, 0: 4, 6: 1, 5: 3}, label_counts_per_image=[128, 78, 50], image_counts_per_class={4: 3, 10: 3, 2: 3, 1: 3, 8: 2, 7: 3, 3: 3, 0: 3, 6: 1, 5: 2}, image_indices_per_class={4: [0, 1, 2], 10: [0, 1, 2], 2: [0, 1, 2], 1: [0, 1, 2], 8: [0, 1], 7: [0, 1, 2], 3: [0, 1, 2], 0: [0, 1, 2], 6: [0], 5: [0, 1]}, image_count=3, class_count=10, label_count=256, class_names=['ignored regions', 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'motor']) box_outliers={} box_stats=DataevalCleaningStatsOutputs(source_index=[SourceIndex(0, 0), SourceIndex(0, 1), SourceIndex(0, 2), SourceIndex(0, 3), SourceIndex(0, 4), SourceIndex(0, 5), SourceIndex(0, 6), SourceIndex(0, 7), SourceIndex(0, 8), SourceIndex(0, 9), 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invalid_box_count=[0, 0, 0], dim_stats=DataevalCleaningDimensionStatsOutputs(offset_x=array([206., 224., 207., 230., 231., 98., 105., 101., 95., 98., 82.,
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0.2421875 , 0.26953125, 0.30859375, 0.34375 , 0.2890625 ,
0.30859375, 0.35546875, 0.32421875, 0.33203125, 0.375 ,
0.42578125, 0.4296875 , 0.42578125, 0.4296875 , 0.4140625 ,
0.390625 , 0.23076923, 0.23956044, 0.45703125, 0.47265625,
0.47265625, 0.4765625 , 0.4140625 , 0.41015625, 0.40625 ,
0.4140625 , 0.41015625, 0.3984375 , 0.484375 , 0.484375 ,
0.46875 , 0.42578125, 0.40234375, 0.38671875, 0.33203125,
0.2265625 , 0.1953125 , 0.1953125 , 0.18359375, 0.1640625 ,
0.16015625, 0.16015625, 0.06640625, 0.078125 , 0.125 ,
0.125 , 0.13671875, 0.00390625, 0.015625 , 0.04296875,
0.0625 , 0.3671875 , 0.359375 , 0.390625 , 0.375 ,
0.40234375, 0.41015625, 0.46875 , 0.484375 , 0.47265625,
0.31640625, 0.296875 , 0.265625 , 0.2109375 , 0.16796875,
0.2578125 , 0.23828125, 0.3046875 , 0.2890625 , 0.20703125,
0.19921875, 0.2421875 , 0.23828125, 0.23046875, 0.23046875,
0.1953125 , 0.18359375, 0.23046875, 0.23046875, 0.2265625 ,
0.19921875, 0.15625 , 0.16015625, 0.01171875, 0.0078125 ,
0.03125 , 0.0234375 , 0.08203125, 0.1015625 , 0.09765625,
0.09765625, 0.10546875, 0.125 , 0.0859375 , 0.078125 ,
0.05078125, 0.05078125, 0.04296875, 0.11328125, 0.10546875,
0.1015625 , 0.09375 , 0.08984375, 0.08203125, 0.08203125,
0.08984375, 0.08984375, 0.08203125, 0.0703125 , 0.078125 ,
0.11328125, 0.109375 , 0.109375 , 0.32421875, 0.28125 ,
0.00390625, 0.38671875, 0.43359375, 0.3125 , 0.1875 ,
0.01171875, 0.0625 , 0.00390625, 0.1640625 , 0.359375 ,
0.390625 , 0.14453125, 0.140625 , 0.15234375, 0.09765625,
0.0390625 , 0.08131868, 0.00659341, 0.02857143, 0.04175824,
0.12109375, 0.13671875, 0.109375 , 0.125 , 0.10546875,
0.09765625, 0.08984375, 0.08203125, 0.20703125, 0.21875 ,
0.22265625, 0.22265625, 0.21875 , 0.22265625, 0.203125 ,
0.203125 , 0.20703125, 0.203125 , 0.2109375 , 0.2265625 ,
0.27734375, 0.24609375, 0.265625 , 0.3046875 , 0.2890625 ,
0.2265625 , 0.21484375, 0.21875 , 0.328125 , 0.34765625,
0.4140625 , 0.42578125, 0.33984375, 0.328125 , 0.3359375 ,
0.3359375 , 0.37109375, 0.390625 , 0.35546875, 0.00390625,
0.3984375 , 0.0234375 , 0.1875 , 0.05859375, 0.09765625,
0.0703125 , 0.09375 , 0.1171875 , 0.125 , 0.12890625,
0.1328125 , 0.0625 , 0.0703125 , 0.0625 , 0.0703125 ,
0.07421875, 0. , 0.1875 , 0.1953125 , 0.16796875,
0.3203125 , 0.3125 , 0.05859375, 0.046875 , 0.10546875,
0.16015625, 0.1640625 , 0.359375 , 0.2578125 , 0.24609375,
0.0078125 , 0.00390625, 0.0234375 , 0.08984375, 0.43359375,
0.4609375 , 0.3984375 , 0.42578125, 0.43359375, 0.3984375 ,
0.47265625, 0.4765625 , 0.3828125 , 0.4453125 , 0.4375 ,
0.41015625, 0.421875 , 0.45703125, 0.45703125, 0.44921875,
0.47265625, 0.44140625, 0.47265625, 0.46875 , 0.01953125,
0.06593407, 0.26953125, 0.4453125 , 0.44140625, 0.4375 ,
0.44140625, 0.4453125 , 0.44921875, 0.34765625, 0.34765625,
0.3515625 ]), invalid_box=array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False])))
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# collect the markdown outputs for the final report
_md = cleaning_run.collect_md_report(threshold=0)
report_md += _md
# collect the markdown outputs for the final report
_md = cleaning_run.collect_md_report(threshold=0)
report_md += _md
Dataeval - Shift¶
In [26]:
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%%time
from checkmaite.core.object_detection import DataevalShift
# instantiate capability object
capability = DataevalShift()
# run analysis for this capability
shift_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper, coco_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(shift_run.outputs)
%%time
from checkmaite.core.object_detection import DataevalShift
# instantiate capability object
capability = DataevalShift()
# run analysis for this capability
shift_run = capability.run(use_cache=False, datasets=[coco_dataset_wrapper, coco_dataset_wrapper]) # NOTE: `use_cache=True` will bypass the compute and load cached results if available
# view results
# output is large, so expandable outputs are shown instead of raw outputs here
create_expandable_output(shift_run.outputs)
CPU times: user 2.77 s, sys: 38.7 ms, total: 2.81 s Wall time: 887 ms
Out[26]:
Output: drift=DataevalShiftDriftOutputs(mmd=DriftOutput(drifted=False, threshold=0.05, distance=-0.207544207...
Show full output
drift=DataevalShiftDriftOutputs(mmd=DriftOutput(drifted=False, threshold=0.05, distance=-0.207544207572937, metric_name='mmd2', details={'p_val': 0.9900000095367432, 'distance_threshold': 0.16573911905288696}), cvm=DriftOutput(drifted=False, threshold=0.008333333333333333, distance=0.0, metric_name='cvm_distance', details={'p_val': 1.0, 'feature_drift': array([False, False, False, False, False, False]), 'feature_threshold': 0.05, 'p_vals': array([1., 1., 1., 1., 1., 1.], dtype=float32), 'distances': array([0., 0., 0., 0., 0., 0.], dtype=float32)}), ks=DriftOutput(drifted=False, threshold=0.008333333333333333, distance=0.0, metric_name='ks_distance', details={'p_val': 1.0, 'feature_drift': array([False, False, False, False, False, False]), 'feature_threshold': 0.05, 'p_vals': array([1., 1., 1., 1., 1., 1.], dtype=float32), 'distances': array([0., 0., 0., 0., 0., 0.], dtype=float32)})) ood=DataevalShiftOODOutputs(ood_knn=DataevalShiftOODKNNOutput(is_ood=array([False, False, False, False, False, False]), instance_score=array([0.94475585, 0.9435164 , 0.9159414 , 0.88037264, 0.9157885 ,
0.9382111 ], dtype=float32), feature_score=None))
In [27]:
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# collect the markdown outputs for the final report
_md = shift_run.collect_md_report(threshold=0)
report_md += _md
# collect the markdown outputs for the final report
_md = shift_run.collect_md_report(threshold=0)
report_md += _md
Construct final report¶
Finally, we build our reports using the collected capability run outputs.
Below we include cells for both generating PowerPoint and HTML-based reports using the external Gradient capability, and a Markdown-based report using a package-native rendering feature.
In [28]:
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from checkmaite.core.report._markdown import create_markdown_output
# construct MD report with summarized results
report_path = cache_path() / "report"
report_path.mkdir(parents=True, exist_ok=True)
report_filename = 'RI_OD_Sample_Report.md'
report = create_markdown_output(report_md, report_path, report_filename)
from checkmaite.core.report._markdown import create_markdown_output
# construct MD report with summarized results
report_path = cache_path() / "report"
report_path.mkdir(parents=True, exist_ok=True)
report_filename = 'RI_OD_Sample_Report.md'
report = create_markdown_output(report_md, report_path, report_filename)
Export the report as a PDF¶
Optionally, also render the same markdown report as a PDF for sharing or archival. This consumes the same report_md string we already built.
Note: PDF export requires the optional
reportingextra. Install withpip install ".[reporting]"orpoetry install --extras reporting.
In [29]:
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try:
from checkmaite.core.report._markdown import create_pdf_output
pdf_path = create_pdf_output(report_md, report_path, "RI_OD_Sample_Report.pdf")
print(f"PDF report saved to {pdf_path}")
except ImportError as exc:
print(f"PDF export skipped: {exc}")
print("Install the 'reporting' extra to enable: pip install '.[reporting]'")
try:
from checkmaite.core.report._markdown import create_pdf_output
pdf_path = create_pdf_output(report_md, report_path, "RI_OD_Sample_Report.pdf")
print(f"PDF report saved to {pdf_path}")
except ImportError as exc:
print(f"PDF export skipped: {exc}")
print("Install the 'reporting' extra to enable: pip install '.[reporting]'")
PDF report saved to /home/runner/work/checkmaite/checkmaite/docs/get-started/tscache/report/RI_OD_Sample_Report.pdf