Bump pytorch-lightning from 1.6.5 to 1.7.0 in /requirements (#697)

* Bump pytorch-lightning from 1.6.5 to 1.7.0 in /requirements

Bumps [pytorch-lightning](https://github.com/Lightning-AI/lightning) from 1.6.5 to 1.7.0.
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](https://github.com/Lightning-AI/lightning/compare/1.6.5...pl/1.7.0)

---
updated-dependencies:
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* Remove protobuf restrictions

* LightningModule was moved

* Mypy fixes

* Ensure same behavior

* Fix docs

* Silence warnings

* Change error message location

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

.github/dependabot.yml

@@ -7,7 +7,5 @@ updates:
     # Allow up to 2 open pull requests at a time
     open-pull-requests-limit: 2
     ignore:
-      # torch, tensorboard require protobuf < 4
-      - dependency-name: "protobuf"
       # segmentation-models-pytorch requires older timm, can't update
       - dependency-name: "timm"

docs/conf.py

@@ -60,6 +60,7 @@ nitpick_ignore = [
     ("py:class", ".."),
     # TODO: can't figure out why this isn't found
     ("py:class", "LightningDataModule"),
+    ("py:class", "pytorch_lightning.core.module.LightningModule"),
     # Undocumented class
     ("py:class", "torchvision.models.resnet.ResNet"),
     ("py:class", "segmentation_models_pytorch.base.model.SegmentationModel"),

experiments/test_chesapeakecvpr_models.py

@@ -89,8 +89,8 @@ def main(args: argparse.Namespace) -> None:
     trainer = pl.Trainer(
         gpus=[args.device] if torch.cuda.is_available() else None,
         logger=False,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
 
     for experiment_dir in os.listdir(args.input_dir):

pyproject.toml

@@ -71,6 +71,8 @@ filterwarnings = [
     # https://github.com/PyTorchLightning/pytorch-lightning/issues/13256
     # https://github.com/PyTorchLightning/pytorch-lightning/pull/13261
     "ignore:torch.distributed._sharded_tensor will be deprecated:DeprecationWarning:torch.distributed._sharded_tensor",
+    # https://github.com/Lightning-AI/lightning/issues/13989
+    "ignore:SelectableGroups dict interface is deprecated. Use select.:DeprecationWarning:pytorch_lightning.trainer.connectors.callback_connector",
     # https://github.com/rasterio/rasterio/issues/1742
     # https://github.com/rasterio/rasterio/pull/1753
     "ignore:Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated:DeprecationWarning:rasterio.crs",

requirements/required.old

@@ -11,7 +11,6 @@ numpy==1.21.6;python_version=='3.7'
 omegaconf==2.2.2
 packaging==21.3
 pillow==9.2.0
-protobuf==3.20.1
 pyproj==3.3.1;python_version>='3.8'
 pyproj==3.2.0;python_version=='3.7'
 pytorch-lightning==1.6.4

requirements/required.txt

@@ -10,9 +10,8 @@ numpy==1.23.1;python_version>='3.8'
 omegaconf==2.2.2
 packaging==21.3
 pillow==9.2.0
-protobuf==3.20.1
 pyproj==3.3.1;python_version>='3.8'
-pytorch-lightning==1.6.5
+pytorch-lightning==1.7.0
 rasterio==1.3.0;python_version>='3.8'
 rtree==1.0.0
 scikit-learn==1.1.1;python_version>='3.8'

torchgeo/datamodules/bigearthnet.py

@@ -90,7 +90,7 @@ class BigEarthNetDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.bands = bands
         self.num_classes = num_classes

torchgeo/datamodules/chesapeake.py

@@ -64,7 +64,7 @@ class ChesapeakeCVPRDataModule(LightningDataModule):
         Raises:
             ValueError: if ``use_prior_labels`` is used with ``class_set==7``
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         for state in train_splits + val_splits + test_splits:
             assert state in ChesapeakeCVPR.splits
         assert class_set in [5, 7]

torchgeo/datamodules/cowc.py

@@ -36,7 +36,7 @@ class COWCCountingDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.seed = seed
         self.batch_size = batch_size

torchgeo/datamodules/cyclone.py

@@ -44,7 +44,7 @@ class CycloneDataModule(pl.LightningDataModule):
             api_key: The RadiantEarth MLHub API key to use if the dataset needs to be
                 downloaded
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.seed = seed
         self.batch_size = batch_size

torchgeo/datamodules/deepglobelandcover.py

@@ -36,7 +36,7 @@ class DeepGlobeLandCoverDataModule(pl.LightningDataModule):
             num_workers: The number of workers to use in all created DataLoaders
             val_split_pct: What percentage of the dataset to use as a validation set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/etci2021.py

@@ -48,7 +48,7 @@ class ETCI2021DataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.seed = seed
         self.batch_size = batch_size

torchgeo/datamodules/eurosat.py

@@ -68,7 +68,7 @@ class EuroSATDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/fair1m.py

@@ -56,7 +56,7 @@ class FAIR1MDataModule(pl.LightningDataModule):
             val_split_pct: What percentage of the dataset to use as a validation set
             test_split_pct: What percentage of the dataset to use as a test set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/inria.py

@@ -65,7 +65,7 @@ class InriaAerialImageLabelingDataModule(pl.LightningDataModule):
             num_patches_per_tile: Number of random patches per sample
             predict_on: Directory/Dataset of images to run inference on
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/landcoverai.py

@@ -33,7 +33,7 @@ class LandCoverAIDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/loveda.py

@@ -39,7 +39,7 @@ class LoveDADataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.scene = scene
         self.batch_size = batch_size

torchgeo/datamodules/naip.py

@@ -46,7 +46,7 @@ class NAIPChesapeakeDataModule(pl.LightningDataModule):
             num_workers: The number of workers to use in all created DataLoaders
             patch_size: size of patches to sample
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.naip_root_dir = naip_root_dir
         self.chesapeake_root_dir = chesapeake_root_dir
         self.batch_size = batch_size

torchgeo/datamodules/nasa_marine_debris.py

@@ -57,7 +57,7 @@ class NASAMarineDebrisDataModule(pl.LightningDataModule):
             val_split_pct: What percentage of the dataset to use as a validation set
             test_split_pct: What percentage of the dataset to use as a test set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/oscd.py

@@ -87,7 +87,7 @@ class OSCDDataModule(pl.LightningDataModule):
             num_patches_per_tile: number of random patches per sample
             pad_size: size to pad images to during val/test steps
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.bands = bands
         self.train_batch_size = train_batch_size

torchgeo/datamodules/potsdam.py

@@ -37,7 +37,7 @@ class Potsdam2DDataModule(pl.LightningDataModule):
             num_workers: The number of workers to use in all created DataLoaders
             val_split_pct: What percentage of the dataset to use as a validation set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/resisc45.py

@@ -38,7 +38,7 @@ class RESISC45DataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/sen12ms.py

@@ -72,7 +72,7 @@ class SEN12MSDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         assert band_set in SEN12MS.BAND_SETS.keys()
 
         self.root_dir = root_dir

torchgeo/datamodules/so2sat.py

@@ -75,7 +75,7 @@ class So2SatDataModule(pl.LightningDataModule):
             unsupervised_mode: Makes the train dataloader return imagery from the train,
                 val, and test sets
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/ucmerced.py

@@ -34,7 +34,7 @@ class UCMercedDataModule(pl.LightningDataModule):
             batch_size: The batch size to use in all created DataLoaders
             num_workers: The number of workers to use in all created DataLoaders
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/vaihingen.py

@@ -37,7 +37,7 @@ class Vaihingen2DDataModule(pl.LightningDataModule):
             num_workers: The number of workers to use in all created DataLoaders
             val_split_pct: What percentage of the dataset to use as a validation set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/datamodules/xview.py

@@ -37,7 +37,7 @@ class XView2DataModule(pl.LightningDataModule):
             num_workers: The number of workers to use in all created DataLoaders
             val_split_pct: What percentage of the dataset to use as a validation set
         """
-        super().__init__()  # type: ignore[no-untyped-call]
+        super().__init__()
         self.root_dir = root_dir
         self.batch_size = batch_size
         self.num_workers = num_workers

torchgeo/trainers/byol.py

@@ -6,6 +6,7 @@
 import random
 from typing import Any, Callable, Dict, Optional, Tuple, cast
 
+import pytorch_lightning as pl
 import torch
 import torch.nn.functional as F
 import torchvision
@@ -13,7 +14,6 @@ from kornia import augmentation as K
 from kornia import filters
 from kornia.geometry import transform as KorniaTransform
 from packaging.version import parse
-from pytorch_lightning.core.lightning import LightningModule
 from torch import Tensor, optim
 from torch.autograd import Variable
 from torch.nn.modules import BatchNorm1d, Conv2d, Linear, Module, ReLU, Sequential
@@ -304,7 +304,7 @@ class BYOL(Module):
             pt.data = self.beta * pt.data + (1 - self.beta) * p.data
 
 
-class BYOLTask(LightningModule):
+class BYOLTask(pl.LightningModule):
     """Class for pre-training any PyTorch model using BYOL."""
 
     def config_task(self) -> None:

torchgeo/trainers/classification.py

@@ -189,7 +189,7 @@ class ClassificationTask(pl.LightningModule):
 
         if batch_idx < 10:
             try:
-                datamodule = self.trainer.datamodule  # type: ignore[union-attr]
+                datamodule = self.trainer.datamodule  # type: ignore[attr-defined]
                 batch["prediction"] = y_hat_hard
                 for key in ["image", "label", "prediction"]:
                     batch[key] = batch[key].cpu()
@@ -358,7 +358,7 @@ class MultiLabelClassificationTask(ClassificationTask):
 
         if batch_idx < 10:
             try:
-                datamodule = self.trainer.datamodule  # type: ignore[union-attr]
+                datamodule = self.trainer.datamodule  # type: ignore[attr-defined]
                 batch["prediction"] = y_hat_hard
                 for key in ["image", "label", "prediction"]:
                     batch[key] = batch[key].cpu()

torchgeo/trainers/regression.py

@@ -127,7 +127,7 @@ class RegressionTask(pl.LightningModule):
 
         if batch_idx < 10:
             try:
-                datamodule = self.trainer.datamodule  # type: ignore[union-attr]
+                datamodule = self.trainer.datamodule  # type: ignore[attr-defined]
                 batch["prediction"] = y_hat
                 for key in ["image", "label", "prediction"]:
                     batch[key] = batch[key].cpu()

torchgeo/trainers/segmentation.py

@@ -6,10 +6,10 @@
 import warnings
 from typing import Any, Dict, cast
 
+import pytorch_lightning as pl
 import segmentation_models_pytorch as smp
 import torch
 import torch.nn as nn
-from pytorch_lightning.core.lightning import LightningModule
 from torch import Tensor
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 from torch.utils.data import DataLoader
@@ -23,7 +23,7 @@ from ..models import FCN
 DataLoader.__module__ = "torch.utils.data"
 
 
-class SemanticSegmentationTask(LightningModule):
+class SemanticSegmentationTask(pl.LightningModule):
     """LightningModule for semantic segmentation of images."""
 
     def config_task(self) -> None:
@@ -184,7 +184,7 @@ class SemanticSegmentationTask(LightningModule):
 
         if batch_idx < 10:
             try:
-                datamodule = self.trainer.datamodule  # type: ignore[union-attr]
+                datamodule = self.trainer.datamodule  # type: ignore[attr-defined]
                 batch["prediction"] = y_hat_hard
                 for key in ["image", "mask", "prediction"]:
                     batch[key] = batch[key].cpu()