Audio(
    data_or_path, sample_rate=None, caption=None
)

@classmethod
durations(
    audio_list
)

resolve_ref()

@classmethod
sample_rates(
    audio_list
)

BoundingBoxes2D(
    val: dict,
    key: str
) -> None

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))

class_labels = {0: "person", 1: "car", 2: "road", 3: "building"}

img = wandb.Image(
    image,
    boxes={
        "predictions": {
            "box_data": [
                {
                    # 기본 상대/분수 도메인으로 표현된 상자 하나
                    "position": {
                        "minX": 0.1,
                        "maxX": 0.2,
                        "minY": 0.3,
                        "maxY": 0.4,
                    },
                    "class_id": 1,
                    "box_caption": class_labels[1],
                    "scores": {"acc": 0.2, "loss": 1.2},
                },
                {
                    # 픽셀 도메인으로 표현된 다른 상자
                    "position": {
                        "middle": [150, 20],
                        "width": 68,
                        "height": 112,
                    },
                    "domain": "pixel",
                    "class_id": 3,
                    "box_caption": "a building",
                    "scores": {"acc": 0.5, "loss": 0.7},
                },
                # 필요한 만큼 상자를 로깅합니다.
            ],
            "class_labels": class_labels,
        }
    },
)

run.log({"driving_scene": img})

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))

class_labels = {0: "person", 1: "car", 2: "road", 3: "building"}

class_set = wandb.Classes(
    [
        {"name": "person", "id": 0},
        {"name": "car", "id": 1},
        {"name": "road", "id": 2},
        {"name": "building", "id": 3},
    ]
)

img = wandb.Image(
    image,
    boxes={
        "predictions": {
            "box_data": [
                {
                    # 기본 상대/분수 도메인으로 표현된 상자 하나
                    "position": {
                        "minX": 0.1,
                        "maxX": 0.2,
                        "minY": 0.3,
                        "maxY": 0.4,
                    },
                    "class_id": 1,
                    "box_caption": class_labels[1],
                    "scores": {"acc": 0.2, "loss": 1.2},
                },
                {
                    # 픽셀 도메인으로 표현된 다른 상자
                    "position": {
                        "middle": [150, 20],
                        "width": 68,
                        "height": 112,
                    },
                    "domain": "pixel",
                    "class_id": 3,
                    "box_caption": "a building",
                    "scores": {"acc": 0.5, "loss": 0.7},
                },
                # 필요한 만큼 상자를 로깅합니다.
            ],
            "class_labels": class_labels,
        }
    },
    classes=class_set,
)

table = wandb.Table(columns=["image"])
table.add_data(img)
run.log({"driving_scene": table})

@classmethod
type_name() -> str

validate(
    val: dict
) -> bool

Graph(
    format="keras"
)

add_edge(
    from_node, to_node
)

add_node(
    node=None, **node_kwargs
)

@classmethod
from_keras(
    model
)

pprint()

__getitem__(
    nid
)

Histogram(
    sequence: Optional[Sequence] = None,
    np_histogram: Optional['NumpyHistogram'] = None,
    num_bins: int = 64
) -> None

wandb.Histogram([1, 2, 3])

hist = np.histogram(data)
wandb.Histogram(np_histogram=hist)

Html(
    data: Union[str, 'TextIO'],
    inject: bool = (True)
) -> None

inject_head() -> None

Image(
    data_or_path: "ImageDataOrPathType",
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[Union['Classes', Sequence[dict]]] = None,
    boxes: Optional[Union[Dict[str, 'BoundingBoxes2D'], Dict[str, dict]]] = None,
    masks: Optional[Union[Dict[str, 'ImageMask'], Dict[str, dict]]] = None,
    file_type: Optional[str] = None
) -> None

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}")
        examples.append(image)
    run.log({"examples": examples})

import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(
            low=0, high=256, size=(100, 100, 3), dtype=np.uint8
        )
        pil_image = PILImage.fromarray(pixels, mode="RGB")
        image = wandb.Image(pil_image, caption=f"random field {i}")
        examples.append(image)
    run.log({"examples": examples})

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}", file_type="jpg")
        examples.append(image)
    run.log({"examples": examples})

@classmethod
all_boxes(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

@classmethod
all_captions(
    images: Sequence['Media']
) -> Union[bool, Sequence[Optional[str]]]

@classmethod
all_masks(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

guess_mode(
    data: "np.ndarray"
) -> str

@classmethod
to_uint8(
    data: "np.ndarray"
) -> "np.ndarray"

ImageMask(
    val: dict,
    key: str
) -> None

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {
            "mask_data": predicted_mask,
            "class_labels": class_labels,
        },
        "ground_truth": {
            "mask_data": ground_truth_mask,
            "class_labels": class_labels,
        },
    },
)
run.log({"img_with_masks": masked_image})

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

class_set = wandb.Classes(
    [
        {"name": "person", "id": 0},
        {"name": "tree", "id": 1},
        {"name": "car", "id": 2},
        {"name": "road", "id": 3},
    ]
)

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {
            "mask_data": predicted_mask,
            "class_labels": class_labels,
        },
        "ground_truth": {
            "mask_data": ground_truth_mask,
            "class_labels": class_labels,
        },
    },
    classes=class_set,
)

table = wandb.Table(columns=["image"])
table.add_data(masked_image)
run.log({"random_field": table})

@classmethod
type_name() -> str

validate(
    val: dict
) -> bool

Molecule(
    data_or_path: Union[str, 'TextIO'],
    caption: Optional[str] = None,
    **kwargs
) -> None

@classmethod
from_rdkit(
    data_or_path: "RDKitDataType",
    caption: Optional[str] = None,
    convert_to_3d_and_optimize: bool = (True),
    mmff_optimize_molecule_max_iterations: int = 200
) -> "Molecule"

@classmethod
from_smiles(
    data: str,
    caption: Optional[str] = None,
    sanitize: bool = (True),
    convert_to_3d_and_optimize: bool = (True),
    mmff_optimize_molecule_max_iterations: int = 200
) -> "Molecule"

Object3D(
    data_or_path: Union['np.ndarray', str, 'TextIO', dict],
    **kwargs
) -> None

@classmethod
from_file(
    data_or_path: Union['TextIO', str],
    file_type: Optional['FileFormat3D'] = None
) -> "Object3D"

@classmethod
from_numpy(
    data: "np.ndarray"
) -> "Object3D"

@classmethod
from_point_cloud(
    points: Sequence['Point'],
    boxes: Sequence['Box3D'],
    vectors: Optional[Sequence['Vector3D']] = None,
    point_cloud_type: "PointCloudType" = "lidar/beta"
) -> "Object3D"

Plotly(
    val: Union['plotly.Figure', 'matplotlib.artist.Artist']
)

@classmethod
make_plot_media(
    val: Union['plotly.Figure', 'matplotlib.artist.Artist']
) -> Union[Image, 'Plotly']

Table(
    columns=None, data=None, rows=None, dataframe=None, dtype=None, optional=(True),
    allow_mixed_types=(False)
)

add_column(
    name, data, optional=(False)
)

add_computed_columns(
    fn
)

add_data(
    *data
)

add_row(
    *row
)

cast(
    col_name, dtype, optional=(False)
)

get_column(
    name, convert_to=None
)

get_dataframe()

get_index()

index_ref(
    index
)

iterrows()

set_fk(
    col_name, table, table_col
)

set_pk(
    col_name
)

Video(
    data_or_path: Union['np.ndarray', str, 'TextIO', 'BytesIO'],
    caption: Optional[str] = None,
    fps: Optional[int] = None,
    format: Optional[str] = None
)

import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8)
run.log({"video": wandb.Video(frames, fps=4)})

encode(
    fps: int = 4
) -> None

WBTraceTree(
    root_span: Span,
    model_dict: typing.Optional[dict] = None
)