Shortcuts

Source code for flash.pointcloud.segmentation.model

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# 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())

© Copyright 2020-2021, PyTorch Lightning. Revision a374dd4f.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: latest
Versions
latest
stable
0.8.2
0.8.1.post0
0.8.1
0.8.0
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.2
0.5.1
0.5.0
0.4.0
0.3.2
0.3.1
0.3.0
0.2.3
0.2.2
0.2.1
0.2.0
0.1.0post1
Downloads
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.