dlk.core.models package

Submodules

dlk.core.models.basic module

class dlk.core.models.basic.BasicModel(config: dlk.core.models.basic.BasicModelConfig, checkpoint)

Bases: dlk.core.base_module.BaseModel

Basic & General Model

check_keys_are_provided(provide: List[str] = []) → None

check this all the submodules required key are provided

Returns: None

Raises: PermissionError

forward(inputs: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

do forward on a mini batch

Parameters

batch – a mini batch inputs

Returns: the outputs

predict_step(inputs: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

do predict for one batch

Parameters

inputs – one mini-batch inputs

Returns: the predicts outputs

provide_keys() → List[str]

return all keys of the dict of the model returned

This method may no use, so we will remove this.

Returns: all keys

test_step(inputs: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

do test for one batch

Parameters

inputs – one mini-batch inputs

Returns: the test outputs

training: bool

training_step(inputs: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

do training for one batch

Parameters

inputs – one mini-batch inputs

Returns: the training outputs

validation_step(inputs: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

do validation for one batch

Parameters

inputs – one mini-batch inputs

Returns: the validation outputs

class dlk.core.models.basic.BasicModelConfig(config)

Bases: dlk.utils.config.BaseConfig

Config for BasicModel

Config Example:

>>> {
>>>     embedding: {
>>>         _base: "static"
>>>         config: {
>>>             embedding_file: "*@*", //the embedding file, must be saved as numpy array by pickle
>>>             embedding_dim: "*@*",
>>>             //if the embedding_file is a dict, you should provide the dict trace to embedding
>>>             embedding_trace: ".", //default the file itself is the embedding
>>>             /*embedding_trace: "embedding", //this means the <embedding = pickle.load(embedding_file)["embedding"]>*/
>>>             /*embedding_trace: "meta.embedding", //this means the <embedding = pickle.load(embedding_file)['meta']["embedding"]>*/
>>>             freeze: false, // is freeze
>>>             dropout: 0, //dropout rate
>>>             output_map: {},
>>>         },
>>>     },
>>>     decoder: {
>>>         _base: "linear",
>>>         config: {
>>>             input_size: "*@*",
>>>             output_size: "*@*",
>>>             pool: null,
>>>             dropout: "*@*", //the decoder output no need dropout
>>>             output_map: {}
>>>         },
>>>     },
>>>     encoder: {
>>>         _base: "lstm",
>>>         config: {
>>>             output_map: {},
>>>             hidden_size: "*@*",
>>>             input_size: *@*,
>>>             output_size: "*@*",
>>>             num_layers: 1,
>>>             dropout: "*@*", // dropout between layers
>>>         },
>>>     },
>>>     "initmethod": {
>>>         "_base": "range_norm"
>>>     },
>>>     "config": {
>>>         "embedding_dim": "*@*",
>>>         "dropout": "*@*",
>>>         "embedding_file": "*@*",
>>>         "embedding_trace": "token_embedding",
>>>     },
>>>     _link: {
>>>         "config.embedding_dim": ["embedding.config.embedding_dim",
>>>                                  "encoder.config.input_size",
>>>                                  "encoder.config.output_size",
>>>                                  "encoder.config.hidden_size",
>>>                                  "decoder.config.output_size",
>>>                                  "decoder.config.input_size"
>>>                                 ],
>>>         "config.dropout": ["encoder.config.dropout", "decoder.config.dropout", "embedding.config.dropout"],
>>>         "config.embedding_file": ['embedding.config.embedding_file'],
>>>         "config.embedding_trace": ['embedding.config.embedding_trace']
>>>     }
>>>     _name: "basic"
>>> }

get_decoder(config)

return the Decoder and DecoderConfig

Parameters

config – the decoder config

Returns

Decoder, DecoderConfig

get_embedding(config: Dict)

return the Embedding and EmbeddingConfig

Parameters

config – the embedding config

Returns

Embedding, EmbeddingConfig

get_encoder(config: Dict)

return the Encoder and EncoderConfig

Parameters

config – the encoder config

Returns

Encoder, EncoderConfig

get_init_method(config: Dict)

return the InitMethod and InitMethodConfig

Parameters

config – the init method config

Returns

InitMethod, InitMethodConfig

Module contents

models

dlk.core.models.import_models(models_dir, namespace)