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Source code for flash.pointcloud.segmentation.model

# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# limitations under the License.
from typing import Any, Dict, Iterable, Optional, Tuple, Union

import torch
from torch import Tensor, nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, Sampler

import flash
from flash.core.classification import ClassificationTask
from flash.core.data.io.input import DataKeys, InputBase
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.utilities.collate import wrap_collate
from flash.core.registry import FlashRegistry
from flash.core.utilities.imports import _TM_GREATER_EQUAL_0_10_0, _TOPIC_POINTCLOUD_AVAILABLE
from flash.core.utilities.stability import beta
from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
from flash.pointcloud.segmentation.backbones import POINTCLOUD_SEGMENTATION_BACKBONES

if _TOPIC_POINTCLOUD_AVAILABLE:
    from open3d._ml3d.torch.modules.losses.semseg_loss import filter_valid_label
    from open3d.ml.torch.dataloaders import TorchDataloader

if _TM_GREATER_EQUAL_0_10_0:
    from torchmetrics.classification import MulticlassJaccardIndex as JaccardIndex
else:
    from torchmetrics import JaccardIndex



[docs]@beta("Point cloud segmentation is currently in Beta.")
class PointCloudSegmentation(ClassificationTask):
    """The ``PointCloudClassifier`` is a :class:`~flash.core.classification.ClassificationTask` that classifies
    pointcloud data.

    Args:
        num_classes: The number of classes (outputs) for this :class:`~flash.core.model.Task`.
        backbone: The backbone name (or a tuple of ``nn.Module``, output size) to use.
        backbone_kwargs: Any additional kwargs to pass to the backbone constructor.
        head: a `nn.Module` to use on top of the backbone. The output dimension should match the `num_classes`
            argument. If not set will default to a single linear layer.
        loss_fn: The loss function to use. If ``None``, a default will be selected by the
            :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
        optimizer: Optimizer to use for training.
        lr_scheduler: The LR scheduler to use during training.
        metrics: Any metrics to use with this :class:`~flash.core.model.Task`. If ``None``, a default will be selected
            by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
        learning_rate: The learning rate for the optimizer.
        multi_label: If ``True``, this will be treated as a multi-label classification problem.

    """

    backbones: FlashRegistry = POINTCLOUD_SEGMENTATION_BACKBONES

    required_extras: str = "pointcloud"

    def __init__(
        self,
        num_classes: int,
        backbone: Union[str, Tuple[nn.Module, int]] = "randlanet",
        backbone_kwargs: Optional[Dict] = None,
        head: Optional[nn.Module] = None,
        loss_fn: LOSS_FN_TYPE = torch.nn.functional.cross_entropy,
        optimizer: OPTIMIZER_TYPE = "Adam",
        lr_scheduler: LR_SCHEDULER_TYPE = None,
        metrics: METRICS_TYPE = None,
        learning_rate: Optional[float] = None,
        multi_label: bool = False,
    ):
        import flash

        if metrics is None:
            metrics = JaccardIndex(num_classes=num_classes)

        super().__init__(
            model=None,
            loss_fn=loss_fn,
            optimizer=optimizer,
            lr_scheduler=lr_scheduler,
            metrics=metrics,
            learning_rate=learning_rate,
            multi_label=multi_label,
        )

        self.save_hyperparameters()

        if not backbone_kwargs:
            backbone_kwargs = {"num_classes": num_classes}

        if isinstance(backbone, tuple):
            self.backbone, out_features = backbone
        else:
            self.backbone, out_features, collate_fn = self.backbones.get(backbone)(**backbone_kwargs)
            self.collate_fn = wrap_collate(collate_fn)
            # replace latest layer
            if not flash._IS_TESTING:
                self.backbone.fc = nn.Identity()

        self.head = nn.Identity() if flash._IS_TESTING else (head or nn.Linear(out_features, num_classes))

    def apply_filtering(self, labels, scores):
        scores, labels = filter_valid_label(scores, labels, self.hparams.num_classes, [0], self.device)
        return labels, scores

    def to_metrics_format(self, x: Tensor) -> Tensor:
        return F.softmax(self.to_loss_format(x), dim=-1)

    def to_loss_format(self, x: Tensor) -> Tensor:
        return x.reshape(-1, x.shape[-1])

    def training_step(self, batch: Any, batch_idx: int) -> Any:
        batch = (batch[DataKeys.INPUT], batch[DataKeys.INPUT]["labels"].view(-1))
        return super().training_step(batch, batch_idx)

    def validation_step(self, batch: Any, batch_idx: int) -> Any:
        batch = (batch[DataKeys.INPUT], batch[DataKeys.INPUT]["labels"].view(-1))
        return super().validation_step(batch, batch_idx)

    def test_step(self, batch: Any, batch_idx: int) -> Any:
        batch = (batch[DataKeys.INPUT], batch[DataKeys.INPUT]["labels"].view(-1))
        return super().test_step(batch, batch_idx)

    def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
        batch[DataKeys.PREDS] = self(batch[DataKeys.INPUT])
        batch[DataKeys.TARGET] = batch[DataKeys.INPUT]["labels"]
        # drop sub-sampled pointclouds
        batch[DataKeys.INPUT] = batch[DataKeys.INPUT]["xyz"][0]
        return batch


[docs]    def forward(self, x) -> Tensor:
        """First call the backbone, then the model head."""
        # hack to enable backbone to work properly.
        self.backbone.device = self.device
        x = self.backbone(x)
        if self.head is not None:
            x = self.head(x)
        return x


    def _patch_dataset(self, dataset: InputBase):
        if not isinstance(dataset.dataset, TorchDataloader):
            dataset.dataset = TorchDataloader(
                dataset.dataset,
                preprocess=self.backbone.preprocess,
                transform=self.backbone.transform,
                use_cache=False,
            )

    def process_train_dataset(
        self,
        dataset: InputBase,
        batch_size: int,
        num_workers: int = 0,
        pin_memory: bool = False,
        shuffle: bool = True,
        drop_last: bool = True,
        sampler: Optional[Sampler] = None,
        persistent_workers: bool = False,
        input_transform: Optional[InputTransform] = None,
        trainer: Optional["flash.Trainer"] = None,
    ) -> DataLoader:
        self._patch_dataset(dataset)
        return super().process_train_dataset(
            dataset,
            batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
            persistent_workers=persistent_workers,
            input_transform=input_transform,
            trainer=trainer,
        )

    def process_val_dataset(
        self,
        dataset: InputBase,
        batch_size: int,
        num_workers: int = 0,
        pin_memory: bool = False,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
        persistent_workers: bool = False,
        input_transform: Optional[InputTransform] = None,
        trainer: Optional["flash.Trainer"] = None,
    ) -> DataLoader:
        self._patch_dataset(dataset)
        return super().process_val_dataset(
            dataset,
            batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
            persistent_workers=persistent_workers,
            input_transform=input_transform,
            trainer=trainer,
        )

    def process_test_dataset(
        self,
        dataset: InputBase,
        batch_size: int,
        num_workers: int = 0,
        pin_memory: bool = False,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
        persistent_workers: bool = False,
        input_transform: Optional[InputTransform] = None,
        trainer: Optional["flash.Trainer"] = None,
    ) -> DataLoader:
        self._patch_dataset(dataset)
        return super().process_test_dataset(
            dataset,
            batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
            persistent_workers=persistent_workers,
            input_transform=input_transform,
            trainer=trainer,
        )

    def process_predict_dataset(
        self,
        dataset: InputBase,
        batch_size: int,
        num_workers: int = 0,
        pin_memory: bool = False,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
        persistent_workers: bool = False,
        input_transform: Optional[InputTransform] = None,
        trainer: Optional["flash.Trainer"] = None,
    ) -> DataLoader:
        self._patch_dataset(dataset)
        return super().process_predict_dataset(
            dataset,
            batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
            persistent_workers=persistent_workers,
            input_transform=input_transform,
            trainer=trainer,
        )


[docs]    def modules_to_freeze(self) -> Union[nn.Module, Iterable[Union[nn.Module, Iterable]]]:
        """Return the module attributes of the model to be frozen."""
        return list(self.backbone.children())

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