Logger

An abstract logger interface for logging metrics and other information.

minnt.Logger

log_audio(label: str, audio: AnyArray, sample_rate: int, epoch: int) -> Self

Log the given audio with the given label at the given epoch.

Parameters:

label (str) –

The label of the logged audio.
audio (AnyArray) –
The audio to log, represented as an array with any of the following shapes:
- (L,) of (L, 1) for mono audio,
- (L, 2) for stereo audio.
If the sample values are floating-point numbers, they are expected to be in the [-1, 1] range; otherwise, they are assumed to be in the [-32_768, 32_767] range.
sample_rate (int) –

The sample rate of the audio.
epoch (int) –

The epoch number at which the audio is logged.

log_config(config: dict[str, Any], epoch: int) -> Self

Log the given configuration dictionary at the given epoch.

Parameters:

config (dict[str, Any]) –

A JSON-serializable dictionary representing the configuration to log.
epoch (int) –

The epoch number at which the configuration is logged.

log_epoch(
    logs: dict[str, float],
    epoch: int,
    epochs: int | None = None,
    elapsed: float | None = None,
) -> Self

Log metrics collected during a given epoch.

Parameters:

logs (dict[str, float]) –

A dictionary of logged metrics for the epoch.
epoch (int) –

The epoch number at which the logs were collected.
epochs (int | None, default: None ) –

The total number of epochs, if known.
elapsed (float | None, default: None ) –

The time elapsed during the epoch, in seconds, if known.

log_figure(
    label: str,
    figure: Any,
    epoch: int,
    tight_layout: bool = True,
    close: bool = True,
) -> Self

Log the given matplotlib Figure with the given label at the given epoch.

Parameters:

label (str) –

The label of the logged image.
figure (Any) –

A matplotlib Figure.
epoch (int) –

The epoch number at which the image is logged.
tight_layout (bool, default: True ) –

Whether to apply tight layout to the figure before logging it.
close (bool, default: True ) –

Whether to close the figure after logging it.

log_graph(graph: Module, data: TensorOrTensors, epoch: int) -> Self

Log the given computation graph by tracing it with the given data.

Alternatively, loggers may choose to log the graph using TorchScript or other mechanisms.

Parameters:

graph (Module) –

The computation graph to log, represented as a PyTorch module.
data (TensorOrTensors) –

The input data to use for tracing the computation graph.
epoch (int) –

The epoch number at which the computation graph is logged.

log_image(label: str, image: AnyArray, epoch: int) -> Self

Log the given image with the given label at the given epoch.

Parameters:

label (str) –

The label of the logged image.
image (AnyArray) –
The image to log, represented as an array, which can have any of the following shapes:
- (H, W) or (H, W, 1) for grayscale images,
- (H, W, 2) for grayscale images with alpha channel,
- (H, W, 3) for RGB images,
- (H, W, 4) for RGBA images.
If the pixel values are floating-point numbers, they are expected to be in the [0, 1] range; otherwise, they are assumed to be in the [0, 255] range.
epoch (int) –

The epoch number at which the image is logged.

log_text(label: str, text: str, epoch: int) -> Self

Log the given text with the given label at the given epoch.

Parameters: