flair.embeddings.image

class flair.embeddings.image.ImageEmbeddingsView on GitHub

Bases: Embeddings[Image]

property embedding_type: str

to_params()View on GitHub

Return type:

dict[str, Any]

classmethod from_params(params)View on GitHub

Return type:

Embeddings

class flair.embeddings.image.IdentityImageEmbeddings(transforms)View on GitHub

Bases: ImageEmbeddings

property embedding_length: int

Returns the length of the embedding vector.

embeddings_name: str = 'IdentityImageEmbeddings'

class flair.embeddings.image.PrecomputedImageEmbeddings(url2tensor_dict, name)View on GitHub

Bases: ImageEmbeddings

property embedding_length: int

Returns the length of the embedding vector.

embeddings_name: str = 'PrecomputedImageEmbeddings'

class flair.embeddings.image.NetworkImageEmbeddings(name, pretrained=True, transforms=None)View on GitHub

Bases: ImageEmbeddings

property embedding_length: int

Returns the length of the embedding vector.

embeddings_name: str = 'NetworkImageEmbeddings'

class flair.embeddings.image.ConvTransformNetworkImageEmbeddings(feats_in, convnet_parms, posnet_parms, transformer_parms)View on GitHub

Bases: ImageEmbeddings

forward(x)View on GitHub

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

property embedding_length

Returns the length of the embedding vector.

embeddings_name: str = 'ConvTransformNetworkImageEmbeddings'