SolverConfig#

Note

All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model SolverConfig[source]#

Config related to solver aka optimizer.

Show JSON schema

{
   "title": "SolverConfig",
   "description": "Config related to solver aka optimizer.",
   "type": "object",
   "properties": {
      "lr": {
         "default": 0.0001,
         "description": "Learning rate.",
         "exclusiveMinimum": 0.0,
         "title": "Lr",
         "type": "number"
      },
      "num_epochs": {
         "default": 10,
         "description": "Number of epochs (ie. sweeps through the whole training set).",
         "exclusiveMinimum": 0,
         "title": "Num Epochs",
         "type": "integer"
      },
      "sync_interval": {
         "default": 1,
         "description": "The interval in epochs for each sync to the cloud.",
         "exclusiveMinimum": 0,
         "title": "Sync Interval",
         "type": "integer"
      },
      "batch_sz": {
         "default": 32,
         "description": "Batch size.",
         "exclusiveMinimum": 0,
         "title": "Batch Sz",
         "type": "integer"
      },
      "one_cycle": {
         "default": true,
         "description": "If True, use triangular LR scheduler with a single cycle across all epochs with start and end LR being lr/10 and the peak being lr.",
         "title": "One Cycle",
         "type": "boolean"
      },
      "multi_stage": {
         "default": [],
         "description": "List of epoch indices at which to divide LR by 10.",
         "items": {
            "type": "integer"
         },
         "title": "Multi Stage",
         "type": "array"
      },
      "class_loss_weights": {
         "anyOf": [
            {
               "items": {
                  "type": "number"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Class weights for weighted loss.",
         "title": "Class Loss Weights"
      },
      "ignore_class_index": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "If specified, this index is ignored when computing the loss. See pytorch documentation for nn.CrossEntropyLoss for more details. This can also be negative, in which case it is treated as a negative slice index i.e. -1 = last index, -2 = second-last index, and so on.",
         "title": "Ignore Class Index"
      },
      "external_loss_def": {
         "anyOf": [
            {
               "$ref": "#/$defs/ExternalModuleConfig"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "If specified, the loss will be built from the definition from this external source, using Torch Hub."
      },
      "type_hint": {
         "const": "solver",
         "default": "solver",
         "enum": [
            "solver"
         ],
         "title": "Type Hint",
         "type": "string"
      }
   },
   "$defs": {
      "ExternalModuleConfig": {
         "additionalProperties": false,
         "description": "Config describing an object to be loaded via Torch Hub.",
         "properties": {
            "uri": {
               "anyOf": [
                  {
                     "minLength": 1,
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Local uri of a zip file, or local uri of a directory,or remote uri of zip file.",
               "title": "Uri"
            },
            "github_repo": {
               "anyOf": [
                  {
                     "pattern": ".+/.+",
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "<repo-owner>/<repo-name>[:tag]",
               "title": "Github Repo"
            },
            "name": {
               "anyOf": [
                  {
                     "minLength": 1,
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Name of the folder in which to extract/copy the definition files.",
               "title": "Name"
            },
            "entrypoint": {
               "description": "Name of a Callable present in ``hubconf.py``. See docs for ``torch.hub`` for details.",
               "minLength": 1,
               "title": "Entrypoint",
               "type": "string"
            },
            "entrypoint_args": {
               "default": [],
               "description": "Args to pass to the entrypoint. Must be serializable.",
               "items": {},
               "title": "Entrypoint Args",
               "type": "array"
            },
            "entrypoint_kwargs": {
               "default": {},
               "description": "Keyword args to pass to the entrypoint. Must be serializable.",
               "title": "Entrypoint Kwargs",
               "type": "object"
            },
            "force_reload": {
               "default": false,
               "description": "Force reload of module definition.",
               "title": "Force Reload",
               "type": "boolean"
            },
            "type_hint": {
               "const": "external-module",
               "default": "external-module",
               "enum": [
                  "external-module"
               ],
               "title": "Type Hint",
               "type": "string"
            }
         },
         "required": [
            "entrypoint"
         ],
         "title": "ExternalModuleConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid
validate_assignment: bool = True

Fields:

batch_sz (int)
class_loss_weights (Optional[Sequence[float]])
external_loss_def (rastervision.pytorch_learner.learner_config.ExternalModuleConfig | None)
ignore_class_index (int | None)
lr (float)
multi_stage (list[int])
num_epochs (int)
one_cycle (bool)
sync_interval (int)
type_hint (Literal['solver'])

Validators:

check_no_loss_opts_if_external » all fields

field batch_sz: PosInt = 32#

Batch size.

Constraints:

gt = 0

Validated by:

check_no_loss_opts_if_external

field class_loss_weights: Sequence[float] | None = None#

Class weights for weighted loss.

Validated by:

check_no_loss_opts_if_external

field external_loss_def: ExternalModuleConfig | None = None#

If specified, the loss will be built from the definition from this external source, using Torch Hub.

Validated by:

check_no_loss_opts_if_external

field ignore_class_index: int | None = None#

If specified, this index is ignored when computing the loss. See pytorch documentation for nn.CrossEntropyLoss for more details. This can also be negative, in which case it is treated as a negative slice index i.e. -1 = last index, -2 = second-last index, and so on.

Validated by:

check_no_loss_opts_if_external

field lr: PositiveFloat = 0.0001#

Learning rate.

Constraints:

gt = 0

Validated by:

check_no_loss_opts_if_external

field multi_stage: list[int] = []#

List of epoch indices at which to divide LR by 10.

Validated by:

check_no_loss_opts_if_external

field num_epochs: PosInt = 10#

Number of epochs (ie. sweeps through the whole training set).

Constraints:

gt = 0

Validated by:

check_no_loss_opts_if_external

field one_cycle: bool = True#

If True, use triangular LR scheduler with a single cycle across all epochs with start and end LR being lr/10 and the peak being lr.

Validated by:

check_no_loss_opts_if_external

field sync_interval: PosInt = 1#

The interval in epochs for each sync to the cloud.

Constraints:

gt = 0

Validated by:

check_no_loss_opts_if_external

field type_hint: Literal['solver'] = 'solver'#

Validated by:

check_no_loss_opts_if_external

build()#

Build an instance of the corresponding type of object using this config.

For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.

build_epoch_scheduler(optimizer: Optimizer, last_epoch: int = -1, **kwargs) → torch.optim.lr_scheduler._LRScheduler | None[source]#

Returns an LR scheduler that changes the LR each epoch.

This is used to divide the learning rate by 10 at certain epochs.

Parameters:

optimizer (optim.Optimizer) – Optimizer to build scheduler for.
last_epoch (int) – Last epoch. Defaults to -1.
**kwargs – Extra args for the scheduler constructor.

Returns:

An epoch scheduler, if applicable. Otherwise, None.

Return type:

torch.optim.lr_scheduler._LRScheduler | None

build_loss(num_classes: int, save_dir: str | None = None, hubconf_dir: str | None = None) → Callable[[...], Tensor][source]#

Build and return a loss function based on the config.

Parameters:

num_classes (int) – Number of classes.
save_dir (str | None) – Used for building external_loss_def if specified. Defaults to None.
hubconf_dir (str | None) – Used for building external_loss_def if specified. Defaults to None.

Returns:

Loss function.

Return type:

Callable[[…], Tensor]

build_optimizer(model: Module, **kwargs) → Adam[source]#

Build and return an Adam optimizer for the given model.

Parameters:

model (nn.Module) – Model to be trained.
**kwargs – Extra args for the optimizer constructor.

Returns:

An Adam optimizer instance.

Return type:

Adam

build_step_scheduler(optimizer: Optimizer, train_ds_sz: int, last_epoch: int = -1, **kwargs) → torch.optim.lr_scheduler._LRScheduler | None[source]#

Returns an LR scheduler that changes the LR each step.

This is used to implement the “one cycle” schedule popularized by FastAI.

Parameters:

optimizer (optim.Optimizer) – Optimizer to build scheduler for.
train_ds_sz (int) – Size of the training dataset.
last_epoch (int) – Last epoch. Defaults to -1.
**kwargs – Extra args for the scheduler constructor.

Returns:

A step scheduler, if applicable. Otherwise, None.

Return type:

torch.optim.lr_scheduler._LRScheduler | None

validator check_no_loss_opts_if_external » all fields[source]#

Return type:: Self

classmethod deserialize(inp: str | dict | Config) → Self#

Deserialize Config from a JSON file or dict, upgrading if possible.

If inp is already a Config, it is returned as is.

Parameters:: inp (str | dict | Config) – a URI to a JSON file or a dict.
Return type:: Self

classmethod from_dict(cfg_dict: dict) → Self#

Deserialize Config from a dict.

Parameters:: cfg_dict (dict) – Dict to deserialize.
Return type:: Self

classmethod from_file(uri: str) → Self#

Deserialize Config from a JSON file, upgrading if possible.

Parameters:: uri (str) – URI to load from.
Return type:: Self

recursive_validate_config()#

Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.

revalidate()#

Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

to_file(uri: str, with_rv_metadata: bool = True) → None#

Save a Config to a JSON file, optionally with RV metadata.

Parameters:

uri (str) – URI to save to.
with_rv_metadata (bool) – If True, inject Raster Vision metadata such as plugin_versions, so that the config can be upgraded when loaded.

Return type:

None

update(*args, **kwargs)#

Update any fields before validation.

Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.

validate_config()#

Validate fields that should be checked after update is called.

This is to complement the builtin validation that Pydantic performs at the time of object construction.

validate_list(field: str, valid_options: list[str])#

Validate a list field.

Parameters:

field (str) – name of field to validate
valid_options (list[str]) – values that field is allowed to take

Raises:

ConfigError – if field is invalid