train_sa.py

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import pytorch_lightning.loggers as pl_loggers
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import LearningRateMonitor

from slot_attention.data import PandaPushDataModule
from slot_attention.method import SlotAttentionMethod
from slot_attention.slot_attention_model import SlotAttentionModel
from slot_attention.slate_model import SLATE
from slot_attention.params import (
    merge_namespaces,
    training_params,
    slot_attention_params,
    slate_params,
)
from slot_attention.utils import ImageLogCallback


def main(params=None):
    if params is None:
        params = training_params
        if params.model_type == "slate":
            params = merge_namespaces(params, slate_params)
        elif params.model_type == "sa":
            params = merge_namespaces(params, slot_attention_params)

    assert params.num_slots > 1, "Must have at least 2 slots."
    params.neg_1_to_pos_1_scale = params.model_type == "sa"

    datamodule = PandaPushDataModule(
        data_root=params.data_root,
        train_batch_size=params.batch_size,
        val_batch_size=params.val_batch_size,
        num_workers=params.num_workers,
        neg_1_to_pos_1_scale=params.neg_1_to_pos_1_scale,
    )

    print(
        f"Training set size (images must have {params.num_slots - 1} objects):",
        len(datamodule.train_dataset),
    )

    if params.model_type == "sa":
        model = SlotAttentionModel(
            resolution=params.resolution,
            num_slots=params.num_slots,
            num_iterations=params.num_iterations,
            slot_size=params.slot_size,
            use_separation_loss=params.use_separation_loss,
            use_area_loss=params.use_area_loss,
        )
    elif params.model_type == "slate":
        model = SLATE(
            num_slots=params.num_slots,
            vocab_size=params.vocab_size,
            d_model=params.d_model,
            resolution=params.resolution,
            num_iterations=params.num_iterations,
            slot_size=params.slot_size,
            mlp_hidden_size=params.mlp_hidden_size,
            num_heads=params.num_heads,
            dropout=params.dropout,
            num_dec_blocks=params.num_dec_blocks,
        )

    method = SlotAttentionMethod(model=model, datamodule=datamodule, params=params)

    logger = pl_loggers.WandbLogger(project=f'{params.model_type}-{params.dataset}')

    callbacks = [LearningRateMonitor("step")]
    callbacks.append(ImageLogCallback())

    trainer = Trainer(
        logger=logger if params.is_logger_enabled else False,
        accelerator=params.accelerator,
        num_sanity_val_steps=params.num_sanity_val_steps,
        devices=params.devices,
        max_epochs=params.max_epochs,
        max_steps=params.max_steps,
        accumulate_grad_batches=params.accumulate_grad_batches,
        gradient_clip_val=params.gradient_clip_val,
        log_every_n_steps=50,
        callbacks=callbacks if params.is_logger_enabled else [],
    )
    trainer.fit(method, datamodule=datamodule)


if __name__ == "__main__":
    main()