archai.algos.xnas package

Submodules

archai.algos.xnas.xnas_arch_trainer module

class archai.algos.xnas.xnas_arch_trainer.XnasArchTrainer(conf_train: archai.common.config.Config, model: archai.nas.model.Model, checkpoint: Optional[archai.common.checkpoint.CheckPoint])[source]

Bases: archai.nas.arch_trainer.ArchTrainer

create_optimizer(conf_optim: archai.common.config.Config, params) → torch.optim.optimizer.Optimizer[source]
post_epoch(train_dl: torch.utils.data.dataloader.DataLoader, val_dl: Optional[torch.utils.data.dataloader.DataLoader]) → None[source]
post_fit(train_dl: torch.utils.data.dataloader.DataLoader, val_dl: Optional[torch.utils.data.dataloader.DataLoader]) → None[source]
pre_epoch(train_dl: torch.utils.data.dataloader.DataLoader, val_dl: Optional[torch.utils.data.dataloader.DataLoader]) → None[source]
pre_fit(train_dl: torch.utils.data.dataloader.DataLoader, val_dl: Optional[torch.utils.data.dataloader.DataLoader]) → None[source]
pre_step(x: torch.Tensor, y: torch.Tensor) → None[source]
update_checkpoint(checkpoint: archai.common.checkpoint.CheckPoint) → None[source]

archai.algos.xnas.xnas_exp_runner module

class archai.algos.xnas.xnas_exp_runner.XnasExperimentRunner(config_filename: str, base_name: str, clean_expdir=False)[source]

Bases: archai.nas.exp_runner.ExperimentRunner

model_desc_builder()archai.algos.xnas.xnas_model_desc_builder.XnasModelDescBuilder[source]
trainer_class() → Optional[Type[archai.nas.arch_trainer.ArchTrainer]][source]

archai.algos.xnas.xnas_model_desc_builder module

class archai.algos.xnas.xnas_model_desc_builder.XnasModelDescBuilder[source]

Bases: archai.nas.model_desc_builder.ModelDescBuilder

build_nodes(stem_shapes: List[List[int]], conf_cell: archai.common.config.Config, cell_index: int, cell_type: archai.nas.model_desc.CellType, node_count: int, in_shape: List[int], out_shape: List[int]) → Tuple[List[List[int]], List[archai.nas.model_desc.NodeDesc]][source]
pre_build(conf_model_desc: archai.common.config.Config) → None[source]

archai.algos.xnas.xnas_op module

class archai.algos.xnas.xnas_op.XnasOp(op_desc: archai.nas.model_desc.OpDesc, arch_params: Optional[archai.nas.arch_params.ArchParams], affine: bool)[source]

Bases: archai.nas.operations.Op

The output of XnasOp is weighted output of all allowed primitives.

PRIMITIVES = ['max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5', 'none']
can_drop_path() → bool[source]
finalize() → Tuple[archai.nas.model_desc.OpDesc, Optional[float]][source]

for trainable op, return final op and its rank

forward(x)[source]

Defines 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.

update_alphas(eta: float, current_t: int, total_t: int, grad_clip: float)[source]

Module contents