nemo_deploy.utils

Module Contents

Functions

typedict2tensor	Converts a type dictionary class into a tuple of PyTriton Tensor objects.
str_list2numpy	Converts a list of strings to a numpy array of UTF-8 encoded bytes.
str_ndarray2list	Converts a numpy array of UTF-8 encoded bytes back to a list of strings.
ndarray2img	Converts a numpy array of images to a list of PIL Image objects.
cast_output	Casts input data to a numpy array with the required dtype.
broadcast_list	Broadcasts a list of text data to all processes.

API

nemo_deploy.utils.typedict2tensor(

typedict_class,

overwrite_kwargs: Optional[Dict[str, Any]] = None,

defaults: Optional[Dict[str, Any]] = None,

)

Converts a type dictionary class into a tuple of PyTriton Tensor objects.

This function takes a class with type hints and converts each typed field into a PyTriton Tensor specification, handling nested list types and mapping Python types to numpy dtypes.

Parameters:

• typedict_class – A class with type hints that will be converted to Tensor specs

• overwrite_kwargs – Optional dictionary of kwargs to override default Tensor parameters

• defaults – Optional dictionary of default values (unused)

Returns:

A tuple of PyTriton Tensor objects, one for each typed field in the input class

Return type:

tuple

Raises:

Exception – If an unsupported type is encountered during type mapping

nemo_deploy.utils.str_list2numpy(str_list: List[str]) → numpy.ndarray

Converts a list of strings to a numpy array of UTF-8 encoded bytes.

Takes a list of strings and converts it to a numpy array with an additional dimension, then encodes the strings as UTF-8 bytes.

Parameters:

str_list (List[str]) – List of strings to convert

Returns:

Numpy array of UTF-8 encoded bytes with shape (N, 1) where N is the length of the input list

Return type:

np.ndarray

nemo_deploy.utils.str_ndarray2list(str_ndarray: numpy.ndarray) → List[str]

Converts a numpy array of UTF-8 encoded bytes back to a list of strings.

Takes a numpy array of UTF-8 encoded bytes and decodes it back to strings, removing any extra dimensions, and returns the result as a Python list.

Parameters:

str_ndarray (np.ndarray) – Numpy array of UTF-8 encoded bytes, typically with shape (N, 1) where N is the length of the resulting list

Returns:

List of decoded strings

Return type:

List[str]

nemo_deploy.utils.ndarray2img(

img_ndarray: numpy.ndarray,

) → List[PIL.Image.Image]

Converts a numpy array of images to a list of PIL Image objects.

Takes a numpy array containing one or more images and converts each image to a PIL Image object using Image.fromarray().

Parameters:

img_ndarray (np.ndarray) – Numpy array of images, where each image is a 2D or 3D array representing pixel values

Returns:

List of PIL Image objects created from the input array

Return type:

List[Image.Image]

nemo_deploy.utils.cast_output(data, required_dtype)

Casts input data to a numpy array with the required dtype.

Takes input data that may be a torch.Tensor, numpy array, or other sequence type and converts it to a numpy array with the specified dtype. For string dtypes, the data is encoded as UTF-8 bytes. The output array is ensured to have at least 2 dimensions.

Parameters:

• data – Input data to cast. Can be a torch.Tensor, numpy array, or sequence type that can be converted to a numpy array.

• required_dtype – The desired numpy dtype for the output array.

Returns:

A numpy array containing the input data cast to the required dtype, with at least 2 dimensions.

Return type:

np.ndarray

nemo_deploy.utils.broadcast_list(data, src=0, group=None)

Broadcasts a list of text data to all processes.

Parameters:

• data (list) – List of strings to broadcast.

• src (int, optional) – Source rank. Defaults to 0.

• group (ProcessGroup, optional) – The process group to work on. If None, the default process group will be used.