types#

Reference for common types used throughout the composer library.

composer.core.types.Batch[source]#

Alias to type Any. A batch of data can be represented in several formats, depending on the application.

composer.core.types.PyTorchScheduler[source]#

Alias for base class of learning rate schedulers such as torch.optim.lr_scheduler.ConstantLR.

composer.core.types.JSON[source]#

JSON Data.

Type: str | float | int | None | List[’JSON’] | Dict[str, ’JSON’]

composer.core.types.Dataset[source]#

Alias for torch.utils.data.Dataset.

Classes

`MemoryFormat`	Enum class to represent different memory formats.
`_LRScheduler`	Module `_LRScheduler`.

Attributes

class composer.core.types.MemoryFormat(value)[source]#

Enum class to represent different memory formats.

See torch.torch.memory_format for more details.

CONTIGUOUS_FORMAT#: Default PyTorch memory format represnting a tensor allocated with consecutive dimensions sequential in allocated memory.

CHANNELS_LAST#: This is also known as NHWC. Typically used for images with 2 spatial dimensions (i.e., Height and Width) where channels next to each other in indexing are next to each other in allocated memory. For example, if C[0] is at memory location M_0 then C[1] is at memory location M_1, etc.

CHANNELS_LAST_3D#: This can also be referred to as NTHWC. Same as CHANNELS_LAST but for videos with 3 spatial dimensions (i.e., Time, Height and Width).

PRESERVE_FORMAT#: A way to tell operations to make the output tensor to have the same memory format as the input tensor.

class composer.core.types.TrainerMode(value)[source]#

Enum to represent which mode the Trainer is in.