common.registry¶
Registry is central source of truth in MMF. Inspired from Redux’s concept of global store, Registry maintains mappings of various information to unique keys. Special functions in registry can be used as decorators to register different kind of classes.
Import the global registry object using
from mmf.common.registry import registry
Various decorators for registry different kind of classes with unique keys
- Register a trainer:
@registry.register_trainer
- Register a dataset builder:
@registry.register_builder
- Register a callback function:
@registry.register_callback
- Register a metric:
@registry.register_metric
- Register a loss:
@registry.register_loss
- Register a fusion technique:
@registery.register_fusion
- Register a model:
@registry.register_model
- Register a processor:
@registry.register_processor
- Register a optimizer:
@registry.register_optimizer
- Register a scheduler:
@registry.register_scheduler
- Register a encoder:
@registry.register_encoder
- Register a decoder:
@registry.register_decoder
- Register a transformer backend:
@registry.register_transformer_backend
- Register a transformer head:
@registry.register_transformer_head
- Register a test reporter:
@registry.register_test_reporter
- Register a pl datamodule:
@registry.register_datamodule
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class
mmf.common.registry.
Registry
[source]¶ Class for registry object which acts as central source of truth for MMF
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classmethod
get
(name, default=None, no_warning=False)[source]¶ Get an item from registry with key ‘name’
Parameters: - name (string) – Key whose value needs to be retrieved.
- default – If passed and key is not in registry, default value will be returned with a warning. Default: None
- no_warning (bool) – If passed as True, warning when key doesn’t exist will not be generated. Useful for MMF’s internal operations. Default: False
Usage:
from mmf.common.registry import registry config = registry.get("config")
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classmethod
register
(name, obj)[source]¶ Register an item to registry with key ‘name’
Parameters: name – Key with which the item will be registered. Usage:
from mmf.common.registry import registry registry.register("config", {})
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classmethod
register_builder
(name)[source]¶ Register a dataset builder to registry with key ‘name’
Parameters: name – Key with which the metric will be registered. Usage:
from mmf.common.registry import registry from mmf.datasets.base_dataset_builder import BaseDatasetBuilder @registry.register_builder("vqa2") class VQA2Builder(BaseDatasetBuilder): ...
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classmethod
register_callback
(name)[source]¶ Register a callback to registry with key ‘name’
Parameters: name – Key with which the callback will be registered. Usage:
from mmf.common.registry import registry from mmf.trainers.callbacks.base import Callback @registry.register_callback("logistic") class LogisticCallback(Callback): ...
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classmethod
register_datamodule
(name)[source]¶ Register a datamodule to registry with key ‘name’
Parameters: name – Key with which the datamodule will be registered. Usage:
from mmf.common.registry import registry import pytorch_lightning as pl @registry.register_datamodule("my_datamodule") class MyDataModule(pl.LightningDataModule): ...
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classmethod
register_decoder
(name)[source]¶ Register a decoder to registry with key ‘name’
Parameters: name – Key with which the decoder will be registered. Usage:
from mmf.common.registry import registry from mmf.utils.text import TextDecoder @registry.register_decoder("nucleus_sampling") class NucleusSampling(TextDecoder): ...
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classmethod
register_encoder
(name)[source]¶ Register a encoder to registry with key ‘name’
Parameters: name – Key with which the encoder will be registered. Usage:
from mmf.common.registry import registry from mmf.modules.encoders import Encoder @registry.register_encoder("transformer") class TransformerEncoder(Encoder): ...
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classmethod
register_fusion
(name)[source]¶ Register a fusion technique to registry with key ‘name’
Parameters: name – Key with which the fusion technique will be registered Usage:
from mmf.common.registry import registry from torch import nn @registry.register_fusion("linear_sum") class LinearSum(): ...
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classmethod
register_iteration_strategy
(name)[source]¶ Register an iteration_strategy to registry with key ‘name’
Parameters: name – Key with which the iteration_strategy will be registered. Usage:
from dataclasses import dataclass from mmf.common.registry import registry from mmf.datasets.iterators import IterationStrategy @registry.register_iteration_strategy("my_iteration_strategy") class MyStrategy(IterationStrategy): @dataclass class Config: name: str = "my_strategy" def __init__(self, config, dataloader): ...
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classmethod
register_loss
(name)[source]¶ Register a loss to registry with key ‘name’
Parameters: name – Key with which the loss will be registered. Usage:
from mmf.common.registry import registry from torch import nn @registry.register_task("logit_bce") class LogitBCE(nn.Module): ...
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classmethod
register_metric
(name)[source]¶ Register a metric to registry with key ‘name’
Parameters: name – Key with which the metric will be registered. Usage:
from mmf.common.registry import registry from mmf.modules.metrics import BaseMetric @registry.register_metric("r@1") class RecallAt1(BaseMetric): ...
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classmethod
register_model
(name)[source]¶ Register a model to registry with key ‘name’
Parameters: name – Key with which the model will be registered. Usage:
from mmf.common.registry import registry from mmf.models.base_model import BaseModel @registry.register_task("pythia") class Pythia(BaseModel): ...
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classmethod
register_processor
(name)[source]¶ Register a processor to registry with key ‘name’
Parameters: name – Key with which the processor will be registered. Usage:
from mmf.common.registry import registry from mmf.datasets.processors import BaseProcessor @registry.register_task("glove") class GloVe(BaseProcessor): ...
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classmethod
register_trainer
(name)[source]¶ Register a trainer to registry with key ‘name’
Parameters: name – Key with which the trainer will be registered. Usage:
from mmf.common.registry import registry from mmf.trainers.custom_trainer import CustomTrainer @registry.register_trainer("custom_trainer") class CustomTrainer(): ...
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classmethod