altametris.sara.core.base_model¶

Base model class for all ML models.

Provides a common interface for model loading, saving, and configuration across different ML frameworks and architectures.

Example

>>> class MyModel(BaseModel):
...     def forward(self, x):
...         return self.model(x)
...     def load_weights(self, path):
...         self.model.load_state_dict(torch.load(path))

Attributes¶

logger

Classes¶

BaseModel

Abstract base class for all ML models.

Module Contents¶

altametris.sara.core.base_model.logger¶

class altametris.sara.core.base_model.BaseModel(config: dict[str, Any] | None = None, device: str = 'auto')¶

Bases: abc.ABC, torch.nn.Module

Abstract base class for all ML models.

Provides common functionality for model lifecycle: - Model initialization and configuration - Weight loading and saving - Device management (CPU, CUDA, MPS) - Forward pass abstraction

All concrete model implementations must inherit from this class and implement the abstract methods.

Parameters:

config – Model configuration dictionary
device – Device to run model on (“cpu”, “cuda”, “mps”, or “auto”)

Example

>>> class YoloModel(BaseModel):
...     def forward(self, x):
...         return self.model(x)
...     def load_weights(self, path):
...         self.model = YOLO(path)
...     def save_weights(self, path):
...         self.model.save(path)

config¶

_device¶

_is_initialized = False¶

_resolve_device(device: str) → torch.device¶

Resolve device string to torch.device.

Parameters:: device – Device string (“cpu”, “cuda”, “mps”, “auto”)
Returns:: Resolved torch.device
Raises:: ModelError – If device is invalid or not available

property device: torch.device¶: Get current device.

to_device(device: str | torch.device) → BaseModel¶

Move model to specified device.

Parameters:: device – Target device
Returns:: Self for chaining

Example

>>> model.to_device("cuda")

validate_config(config: dict[str, Any]) → None¶

Validate model configuration.

Parameters:: config – Configuration to validate
Raises:: ConfigurationError – If configuration is invalid

Example

>>> model.validate_config({"input_size": 640})

abstract forward(x: torch.Tensor, **kwargs: Any) → Any¶

Forward pass through the model.

Parameters:

x – Input tensor
**kwargs – Additional arguments

Returns:

Model output

Note

Must be implemented by subclasses

abstract load_weights(path: str | pathlib.Path, **kwargs: Any) → None¶

Load model weights from file.

Parameters:

path – Path to weights file
**kwargs – Additional loading arguments

Raises:

ModelError – If weights cannot be loaded

Note

Must be implemented by subclasses

abstract save_weights(path: str | pathlib.Path, **kwargs: Any) → None¶

Save model weights to file.

Parameters:

path – Path to save weights
**kwargs – Additional saving arguments

Raises:

ModelError – If weights cannot be saved

Note

Must be implemented by subclasses

get_num_parameters() → int¶

Get total number of model parameters.

Returns:: Total parameter count

Example

>>> num_params = model.get_num_parameters()
>>> print(f"Model has {num_params:,} parameters")

get_trainable_parameters() → int¶

Get number of trainable parameters.

Returns:: Trainable parameter count

freeze() → None¶

Freeze all model parameters (set requires_grad=False).

Example

>>> model.freeze()
>>> # Model parameters won't be updated during training

unfreeze() → None¶

Unfreeze all model parameters (set requires_grad=True).

Example

>>> model.unfreeze()
>>> # Model parameters will be updated during training

summary() → dict[str, Any]¶

Get model summary with key statistics.

Returns:: Dictionary with model info

Example

>>> summary = model.summary()
>>> print(f"Parameters: {summary['total_parameters']}")

__repr__() → str¶: String representation of model.