View Source Bumblebee.Vision (Bumblebee v0.5.3)

High-level tasks related to vision.

Summary

Types

image()

A term representing an image.

image_classification_input()

image_classification_output()

image_classification_prediction()

image_embedding_input()

image_embedding_output()

image_to_text_input()

image_to_text_output()

image_to_text_result()

Functions

image_classification(model_info, featurizer, opts \\ [])

Builds serving for image classification.

image_embedding(model_info, featurizer, opts \\ [])

Builds serving for image embeddings.

image_to_text(model_info, featurizer, tokenizer, generation_config, opts \\ [])

Builds serving for image-to-text generation.

Types

image()

@type image() :: Nx.Container.t()

A term representing an image.

Either Nx.Tensor or a struct implementing Nx.Container and resolving to a tensor, with the following properties:

HWC order
RGB color channels
alpha channel may be present, but it's usually stripped out
integer type (:s or :u)

image_classification_input()

@type image_classification_input() :: image()

image_classification_output()

@type image_classification_output() :: %{
  predictions: [image_classification_prediction()]
}

image_classification_prediction()

@type image_classification_prediction() :: %{score: number(), label: String.t()}

image_embedding_input()

@type image_embedding_input() :: image()

image_embedding_output()

@type image_embedding_output() :: %{embedding: Nx.Tensor.t()}

image_to_text_input()

@type image_to_text_input() ::
  image() | %{:image => image(), optional(:seed) => integer() | nil}

image_to_text_output()

@type image_to_text_output() :: %{results: [image_to_text_result()]}

image_to_text_result()

@type image_to_text_result() :: %{text: String.t()}

Functions

image_classification(model_info, featurizer, opts \\ [])

@spec image_classification(
  Bumblebee.model_info(),
  Bumblebee.Featurizer.t(),
  keyword()
) :: Nx.Serving.t()

Builds serving for image classification.

The serving accepts image_classification_input/0 and returns image_classification_output/0. A list of inputs is also supported.

Options

:top_k - the number of top predictions to include in the output. If the configured value is higher than the number of labels, all labels are returned. Defaults to 5
:compile - compiles all computations for predefined input shapes during serving initialization. Should be a keyword list with the following keys:
- :batch_size - the maximum batch size of the input. Inputs are optionally padded to always match this batch size
It is advised to set this option in production and also configure a defn compiler using :defn_options to maximally reduce inference time.
:scores_function - the function to use for converting logits to scores. Should be one of :softmax, :sigmoid, or :none. Defaults to :softmax
:defn_options - the options for JIT compilation. Defaults to []
:preallocate_params - when true, explicitly allocates params on the device configured by :defn_options. You may want to set this option when using partitioned serving, to allocate params on each of the devices. When using this option, you should first load the parameters into the host. This can be done by passing backend: {EXLA.Backend, client: :host} to load_model/1 and friends. Defaults to false

Examples

{:ok, resnet} = Bumblebee.load_model({:hf, "microsoft/resnet-50"})
{:ok, featurizer} = Bumblebee.load_featurizer({:hf, "microsoft/resnet-50"})

serving = Bumblebee.Vision.image_classification(resnet, featurizer)

image = StbImage.read_file!(path)
Nx.Serving.run(serving, image)
#=> %{
#=>   predictions: [
#=>     %{label: "Egyptian cat", score: 0.979233980178833},
#=>     %{label: "tabby, tabby cat", score: 0.00679466687142849},
#=>     %{label: "tiger cat", score: 0.005290505941957235},
#=>     %{label: "lynx, catamount", score: 0.004550771787762642},
#=>     %{label: "Siamese cat, Siamese", score: 1.1611092486418784e-4}
#=>   ]
#=> }

image_embedding(model_info, featurizer, opts \\ [])

@spec image_embedding(
  Bumblebee.model_info(),
  Bumblebee.Featurizer.t(),
  keyword()
) :: Nx.Serving.t()

Builds serving for image embeddings.

The serving accepts image_embedding_input/0 and returns image_embedding_output/0. A list of inputs is also supported.

Options

:output_attribute - the attribute of the model output map to retrieve. When the output is a single tensor (rather than a map), this option is ignored. Defaults to :pooled_state
:embedding_processor - a post-processing step to apply to the embedding. Supported values: :l2_norm. By default the output is returned as is
:compile - compiles all computations for predefined input shapes during serving initialization. Should be a keyword list with the following keys:
- :batch_size - the maximum batch size of the input. Inputs are optionally padded to always match this batch size
It is advised to set this option in production and also configure a defn compiler using :defn_options to maximally reduce inference time.
:defn_options - the options for JIT compilation. Defaults to []
:preallocate_params - when true, explicitly allocates params on the device configured by :defn_options. You may want to set this option when using partitioned serving, to allocate params on each of the devices. When using this option, you should first load the parameters into the host. This can be done by passing backend: {EXLA.Backend, client: :host} to load_model/1 and friends. Defaults to false

Examples

{:ok, clip} =
  Bumblebee.load_model({:hf, "openai/clip-vit-base-patch32"},
    module: Bumblebee.Vision.ClipVision
  )
{:ok, featurizer} = Bumblebee.load_featurizer({:hf, "openai/clip-vit-base-patch32"})
serving = Bumblebee.Vision.image_embedding(clip, featurizer)
image = StbImage.read_file!(path)
Nx.Serving.run(serving, image)
#=> %{
#=>   embedding: #Nx.Tensor<
#=>     f32[768]
#=>     [-0.43403682112693787, 0.09786412119865417, -0.7233262062072754, -0.7707743644714355, 0.5550824403762817, -0.8923342227935791, 0.2687447965145111, 0.9633643627166748, 0.3520320951938629, 0.43195801973342896, 2.1438512802124023, -0.6542983651161194, -1.9736307859420776, 0.1611439287662506, 0.24555791914463043, 0.16985465586185455, 0.9012499451637268, 1.0657984018325806, 1.087411642074585, -0.5864712595939636, 0.3314521908760071, 0.8396108150482178, 0.3906593322753906, 0.13463366031646729, 0.2605385184288025, -0.07457947731018066, 0.4735124707221985, -0.41367805004119873, 0.18244807422161102, 1.4741417169570923, -5.807061195373535, 0.38920706510543823, 0.057687126100063324, 0.060301072895526886, 0.9680367708206177, 0.9670255184173584, 1.3876476287841797, -0.15498873591423035, -0.969764232635498, -0.38127464056015015, 0.05450016260147095, 2.2317700386047363, -0.07926210761070251, -0.11876475065946579, -1.5408644676208496, 0.7505669593811035, 0.9280041456222534, -0.3571934103965759, -1.1390857696533203, ...]
#=>   >
#=> }

image_to_text(model_info, featurizer, tokenizer, generation_config, opts \\ [])

@spec image_to_text(
  Bumblebee.model_info(),
  Bumblebee.Featurizer.t(),
  Bumblebee.Tokenizer.t(),
  Bumblebee.Text.GenerationConfig.t(),
  keyword()
) :: Nx.Serving.t()

Builds serving for image-to-text generation.

The serving accepts image_to_text_input/0 and returns image_to_text_output/0. A list of inputs is also supported.

Options

:compile - compiles all computations for predefined input shapes during serving initialization. Should be a keyword list with the following keys:
- :batch_size - the maximum batch size of the input. Inputs are optionally padded to always match this batch size
It is advised to set this option in production and also configure a defn compiler using :defn_options to maximally reduce inference time.
:defn_options - the options for JIT compilation. Defaults to []
:preallocate_params - when true, explicitly allocates params on the device configured by :defn_options. You may want to set this option when using partitioned serving, to allocate params on each of the devices. When using this option, you should first load the parameters into the host. This can be done by passing backend: {EXLA.Backend, client: :host} to load_model/1 and friends. Defaults to false

Examples

{:ok, blip} = Bumblebee.load_model({:hf, "Salesforce/blip-image-captioning-base"})
{:ok, featurizer} = Bumblebee.load_featurizer({:hf, "Salesforce/blip-image-captioning-base"})
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Salesforce/blip-image-captioning-base"})

{:ok, generation_config} =
  Bumblebee.load_generation_config({:hf, "Salesforce/blip-image-captioning-base"})

serving =
  Bumblebee.Vision.image_to_text(blip, featurizer, tokenizer, generation_config,
    defn_options: [compiler: EXLA]
  )

image = StbImage.read_file!(path)
Nx.Serving.run(serving, image)
#=> %{results: [%{text: "a cat sitting on a chair"}]}