Upgrade template 1.4.0->2.0.2

.gitignore

@@ -146,7 +146,9 @@ dmypy.json
 
 # Lightning-Hydra-Template
 configs/local/default.yaml
-data/
-logs/
+/data/
+/logs/
 .env
-.autoenv
+
+# Aim logging
+.aim

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       # list of supported hooks: https://pre-commit.com/hooks.html
       - id: trailing-whitespace
@@ -19,7 +19,7 @@ repos:
 
   # python code formatting
   - repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 23.1.0
     hooks:
       - id: black
         args: [--line-length, "99"]
@@ -33,55 +33,56 @@ repos:
 
   # python upgrading syntax to newer version
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.32.1
+    rev: v3.3.1
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
 
   # python docstring formatting
   - repo: https://github.com/myint/docformatter
-    rev: v1.4
+    rev: v1.5.1
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=99, --wrap-descriptions=99]
 
   # python check (PEP8), programming errors and code complexity
   - repo: https://github.com/PyCQA/flake8
-    rev: 4.0.1
+    rev: 6.0.0
     hooks:
       - id: flake8
         # ignore E203 because black is used for formatting.
         args:
           [
-            "--ignore",
-            "E203,E501,F401,F403,F841,W504",
+            "--extend-ignore",
+            "E203,E402,E501,F401,F403,F841",
             "--exclude",
             "logs/*,data/*",
           ]
 
   # python security linter
   - repo: https://github.com/PyCQA/bandit
-    rev: "1.7.1"
+    rev: "1.7.5"
     hooks:
       - id: bandit
         args: ["-s", "B101"]
 
   # yaml formatting
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v2.7.1
+    rev: v3.0.0-alpha.6
     hooks:
       - id: prettier
         types: [yaml]
+        exclude: "environment.yaml"
 
-  # jupyter notebook cell output clearing
-  - repo: https://github.com/kynan/nbstripout
-    rev: 0.5.0
+  # shell scripts linter
+  - repo: https://github.com/shellcheck-py/shellcheck-py
+    rev: v0.9.0.2
     hooks:
-      - id: nbstripout
+      - id: shellcheck
 
   # md formatting
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.14
+    rev: 0.7.16
     hooks:
       - id: mdformat
         args: ["--number"]
@@ -94,9 +95,30 @@ repos:
 
   # word spelling linter
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.1.0
+    rev: v2.2.4
     hooks:
       - id: codespell
         args:
-          - --skip=logs/**,data/**
+          - --skip=logs/**,data/**,*.ipynb
           - --ignore-words-list=abc,def,gard
+
+  # jupyter notebook cell output clearing
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.6.1
+    hooks:
+      - id: nbstripout
+
+  # jupyter notebook linting
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.6.3
+    hooks:
+      - id: nbqa-black
+        args: ["--line-length=99"]
+      - id: nbqa-isort
+        args: ["--profile=black"]
+      - id: nbqa-flake8
+        args:
+          [
+            "--extend-ignore=E203,E402,E501,F401,F841",
+            "--exclude=logs/*,data/*",
+          ]

examples/carla_overhead_object_detection/.gitignore

@@ -0,0 +1,4 @@
+# Default folder for downloading dataset
+data
+
+logs

examples/carla_overhead_object_detection/configs/experiment/ArmoryCarlaOverObjDet_TorchvisionFasterRCNN.yaml

@@ -3,7 +3,7 @@
 defaults:
   - COCO_TorchvisionFasterRCNN
   - override /model/[email protected]: tuple_tensorizer_normalizer
-  - override /datamodule: armory_carla_over_objdet_perturbable_mask
+  - override /data: armory_carla_over_objdet_perturbable_mask
 
 task_name: "ArmoryCarlaOverObjDet_TorchvisionFasterRCNN"
 tags: ["regular_training"]

examples/robust_bench/.gitignore

@@ -0,0 +1,4 @@
+# Default folder for downloading dataset
+data
+
+logs

mart/__init__.py

@@ -1,7 +1,7 @@
 import importlib
 
 from mart import attack as attack
-from mart import datamodules as datamodules
+from mart import data as data
 from mart import models as models
 from mart import nn as nn
 from mart import optim as optim

mart/__main__.py

@@ -31,8 +31,7 @@
 
 @hydra.main(version_base="1.2", config_path=config_path, config_name="lightning.yaml")
 def main(cfg: DictConfig) -> float:
-
-    if cfg.resume is None and ("datamodule" not in cfg or "model" not in cfg):
+    if cfg.resume is None and ("data" not in cfg or "model" not in cfg):
         log.fatal("")
         log.fatal("Please specify an experiment to run, e.g.")
         log.fatal(

mart/attack/adversary.py

@@ -10,8 +10,8 @@
 from itertools import cycle
 from typing import TYPE_CHECKING, Any, Callable
 
-import pytorch_lightning as pl
 import torch
+from lightning import pytorch as pl
 
 from mart.utils import silent
 
@@ -140,12 +140,10 @@ def training_step(self, batch, batch_idx):
         return gain
 
     def configure_gradient_clipping(
-        self, optimizer, optimizer_idx, gradient_clip_val=None, gradient_clip_algorithm=None
+        self, optimizer, gradient_clip_val=None, gradient_clip_algorithm=None
     ):
         # Configuring gradient clipping in pl.Trainer is still useful, so use it.
-        super().configure_gradient_clipping(
-            optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm
-        )
+        super().configure_gradient_clipping(optimizer, gradient_clip_val, gradient_clip_algorithm)
 
         if self.gradient_modifier:
             for group in optimizer.param_groups:
@@ -195,7 +193,7 @@ def attacker(self):
 
         elif self.device.type == "cpu":
             accelerator = "cpu"
-            devices = None
+            devices = 1
 
         else:
             raise NotImplementedError

mart/attack/perturber.py

@@ -9,7 +9,7 @@
 from typing import TYPE_CHECKING, Iterable
 
 import torch
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from lightning.pytorch.utilities.exceptions import MisconfigurationException
 
 from .projector import Projector
 

mart/callbacks/eval_mode.py

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 #
 
-from pytorch_lightning.callbacks import Callback
+from lightning.pytorch.callbacks import Callback
 
 __all__ = ["AttackInEvalMode"]
 

mart/callbacks/no_grad_mode.py

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 #
 
-from pytorch_lightning.callbacks import Callback
+from lightning.pytorch.callbacks import Callback
 
 __all__ = ["ModelParamsNoGrad"]
 

mart/callbacks/progress_bar.py

@@ -6,9 +6,9 @@
 
 from typing import Any
 
-import pytorch_lightning as pl
-from pytorch_lightning.callbacks import TQDMProgressBar
-from pytorch_lightning.utilities.rank_zero import rank_zero_only
+from lightning import pytorch as pl
+from lightning.pytorch.callbacks import TQDMProgressBar
+from lightning.pytorch.utilities.rank_zero import rank_zero_only
 
 __all__ = ["ProgressBar"]
 
@@ -41,8 +41,6 @@ def init_train_tqdm(self):
     def on_train_epoch_start(self, trainer: pl.Trainer, *_: Any) -> None:
         super().on_train_epoch_start(trainer)
 
-        # So that it does not display negative rate.
-        self.main_progress_bar.initial = 0
         # So that it does not display Epoch n.
         rank_id = rank_zero_only.rank
         self.main_progress_bar.set_description(f"Attack@rank{rank_id}")

mart/callbacks/visualizer.py

@@ -6,7 +6,7 @@
 
 import os
 
-from pytorch_lightning.callbacks import Callback
+from lightning.pytorch.callbacks import Callback
 from torchvision.transforms import ToPILImage
 
 __all__ = ["PerturbedImageVisualizer"]

mart/configs/callbacks/early_stopping.yaml

@@ -1,9 +1,7 @@
-# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
 
-# Monitor a metric and stop training when it stops improving.
-# Look at the above link for more detailed information.
 early_stopping:
-  _target_: pytorch_lightning.callbacks.EarlyStopping
+  _target_: lightning.pytorch.callbacks.EarlyStopping
   monitor: ??? # quantity to be monitored, must be specified !!!
   min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
   patience: 3 # number of checks with no improvement after which training will be stopped

mart/configs/callbacks/lr_monitor.yaml

@@ -1,3 +1,3 @@
 lr_monitor:
-  _target_: pytorch_lightning.callbacks.LearningRateMonitor
+  _target_: lightning.pytorch.callbacks.LearningRateMonitor
   logging_interval: "step"

mart/configs/callbacks/model_checkpoint.yaml

@@ -1,9 +1,7 @@
-# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
 
-# Save the model periodically by monitoring a quantity.
-# Look at the above link for more detailed information.
 model_checkpoint:
-  _target_: pytorch_lightning.callbacks.ModelCheckpoint
+  _target_: lightning.pytorch.callbacks.ModelCheckpoint
   dirpath: "${paths.output_dir}/checkpoints/" # directory to save the model file
   filename: "epoch_{epoch:03d}" # checkpoint filename
   monitor: ??? # name of the logged metric which determines when model is improving

mart/configs/callbacks/model_summary.yaml

@@ -1,7 +1,5 @@
-# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichModelSummary.html
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
 
-# Generates a summary of all layers in a LightningModule with rich text formatting.
-# Look at the above link for more detailed information.
 model_summary:
-  _target_: pytorch_lightning.callbacks.RichModelSummary
+  _target_: lightning.pytorch.callbacks.RichModelSummary
   max_depth: 1 # the maximum depth of layer nesting that the summary will include

mart/configs/callbacks/rich_progress_bar.yaml

@@ -1,6 +1,4 @@
-# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichProgressBar.html
+# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
 
-# Create a progress bar with rich text formatting.
-# Look at the above link for more detailed information.
 rich_progress_bar:
-  _target_: pytorch_lightning.callbacks.RichProgressBar
+  _target_: lightning.pytorch.callbacks.RichProgressBar

mart/configs/datamodule/cifar10.yaml → mart/configs/data/cifar10.yaml

@@ -55,3 +55,6 @@ test_dataset: ${.val_dataset}
 
 num_workers: 4
 collate_fn: null
+
+# The accuracy metric may require this value.
+num_classes: 10

mart/configs/datamodule/coco.yaml → mart/configs/data/coco.yaml

@@ -2,7 +2,7 @@ defaults:
   - default.yaml
 
 train_dataset:
-  _target_: mart.datamodules.coco.CocoDetection
+  _target_: mart.data.coco.CocoDetection
   root: ${paths.data_dir}/coco/train2017
   annFile: ${paths.data_dir}/coco/annotations/instances_train2017.json
   transforms:
@@ -24,7 +24,7 @@ train_dataset:
         quant_max: 255
 
 val_dataset:
-  _target_: mart.datamodules.coco.CocoDetection
+  _target_: mart.data.coco.CocoDetection
   root: ${paths.data_dir}/coco/val2017
   annFile: ${paths.data_dir}/coco/annotations/instances_val2017.json
   transforms:
@@ -44,7 +44,7 @@ val_dataset:
         quant_max: 255
 
 test_dataset:
-  _target_: mart.datamodules.coco.CocoDetection
+  _target_: mart.data.coco.CocoDetection
   root: ${paths.data_dir}/coco/val2017
   annFile: ${paths.data_dir}/coco/annotations/instances_val2017.json
   transforms:
@@ -66,4 +66,4 @@ test_dataset:
 num_workers: 2
 collate_fn:
   _target_: hydra.utils.get_method
-  path: mart.datamodules.coco.collate_fn
+  path: mart.data.coco.collate_fn

mart/configs/datamodule/default.yaml → mart/configs/data/default.yaml

@@ -1,4 +1,4 @@
-_target_: mart.datamodules.LitDataModule
+_target_: mart.data.LitDataModule
 # _convert_: all
 
 train_dataset: ???

mart/configs/datamodule/imagenet.yaml → mart/configs/data/imagenet.yaml

@@ -46,3 +46,6 @@ val_dataset:
         quant_max: 255
 
 test_dataset: ${.val_dataset}
+
+# The accuracy metric may require this value.
+num_classes: 1000

mart/configs/debug/default.yaml

@@ -30,6 +30,6 @@ trainer:
   devices: 1 # debuggers don't like multiprocessing
   detect_anomaly: true # raise exception if NaN or +/-inf is detected in any tensor
 
-datamodule:
+data:
   num_workers: 0 # debuggers don't like multiprocessing
   pin_memory: False # disable gpu memory pin

mart/configs/debug/profiler.yaml

@@ -8,8 +8,8 @@ defaults:
 trainer:
   max_epochs: 1
   profiler:
-    _target_: pytorch_lightning.profiler.SimpleProfiler
-    # _target_: pytorch_lightning.profiler.AdvancedProfiler
-    # _target_: pytorch_lightning.profiler.PyTorchProfiler
+    _target_: lightning.pytorch.profiler.SimpleProfiler
+    # _target_: lightning.pytorch.profiler.AdvancedProfiler
+    # _target_: lightning.pytorch.profiler.PyTorchProfiler
     dirpath: ${paths.output_dir}
     filename: profiler_log