Renamed all pytorch_lightning to lightning.pytorch

Signed-off-by: George Araujo <[email protected]>

Author: george-gca

Dockerfile

@@ -58,14 +58,14 @@ RUN APT_INSTALL="apt-get install -y --no-install-recommends" && \
         ipdb \
         ipython \
         kornia \
+        lightning \
+        "lightning[extra]" \
         matplotlib \
         numpy \
         omegaconf \
         pillow \
         piq \
         prettytable \
-        pytorch-lightning \
-        "pytorch-lightning[extra]" \
         rich \
         tensorboard \
         torch_optimizer \

README.md

@@ -110,7 +110,7 @@ api_key=YOUR_API_KEY
 
 More configuration variables can be found [here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables).
 
-Most of the things that I found useful to log (metrics, codes, log, image results) are already being logged. Check [train.py](train.py) and [srmodel.py](models/srmodel.py) for more details. All these loggings are done by the [comet logger](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.loggers.comet.html) already available from pytorch lightning. An example of these experiments logged in Comet can be found [here](https://www.comet.ml/george-gca/super-resolution-experiments).
+Most of the things that I found useful to log (metrics, codes, log, image results) are already being logged. Check [train.py](train.py) and [srmodel.py](models/srmodel.py) for more details. All these loggings are done by the [comet logger](https://pytorch-lightning.readthedocs.io/en/stable/api/lightning.pytorch.loggers.comet.html) already available from pytorch lightning. An example of these experiments logged in Comet can be found [here](https://www.comet.ml/george-gca/super-resolution-experiments).
 
 ## Finished experiment Telegram notification
 

configs/all.yml

@@ -66,7 +66,7 @@ trainer:
   barebones: false
   benchmark: null
   callbacks:
-  - class_path: pytorch_lightning.callbacks.ModelCheckpoint
+  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
     init_args:
       dirpath: ${trainer.default_root_dir}/checkpoints
       every_n_epochs: ${trainer.check_val_every_n_epoch}
@@ -89,7 +89,7 @@ trainer:
   gradient_clip_val: null
   inference_mode: true
   logger:
-  - class_path: pytorch_lightning.loggers.CometLogger
+  - class_path: lightning.pytorch.loggers.CometLogger
     # for this to work, create the file ~/.comet.config with
     # [comet]
     # api_key = YOUR API KEY
@@ -99,7 +99,7 @@ trainer:
       offline: false
       project_name: sr-pytorch-lightning
       save_dir: ${trainer.default_root_dir}
-  - class_path: pytorch_lightning.loggers.TensorBoardLogger
+  - class_path: lightning.pytorch.loggers.TensorBoardLogger
     init_args:
       default_hp_metric: false
       log_graph: true

configs/train_default_sr.yml

@@ -35,7 +35,7 @@ model:
 trainer:
   # https://lightning.ai/docs/pytorch/stable/common/trainer.html
   callbacks:
-  - class_path: pytorch_lightning.callbacks.ModelCheckpoint
+  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
     init_args:
       every_n_epochs: ${trainer.check_val_every_n_epoch}
       filename: model
@@ -44,20 +44,20 @@ trainer:
       save_last: true
       save_top_k: 3
       verbose: false
-  # - class_path: pytorch_lightning.callbacks.RichModelSummary
+  # - class_path: lightning.pytorch.callbacks.RichModelSummary
   #   init_args:
   #     max_depth: -1
-  # - class_path: pytorch_lightning.callbacks.RichProgressBar
+  # - class_path: lightning.pytorch.callbacks.RichProgressBar
   check_val_every_n_epoch: 200
   default_root_dir: experiments/test
   logger:
-  - class_path: pytorch_lightning.loggers.CometLogger
+  - class_path: lightning.pytorch.loggers.CometLogger
     init_args:
       experiment_name: test
       offline: false
       project_name: sr-pytorch-lightning
       save_dir: ${trainer.default_root_dir} # without save_dir defined here, Trainer throws an assertion error
-  # - class_path: pytorch_lightning.loggers.TensorBoardLogger
+  # - class_path: lightning.pytorch.loggers.TensorBoardLogger
   #   init_args:
   #     default_hp_metric: false
   #     log_graph: true

main.py

@@ -2,8 +2,8 @@
 from logging.handlers import RotatingFileHandler
 from pathlib import Path
 import numpy as np
-from pytorch_lightning.cli import LightningCLI
-from pytorch_lightning.loggers import CometLogger
+from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.loggers import CometLogger
 
 import models
 from srdata import SRData

models/srmodel.py

@@ -8,14 +8,14 @@
 
 import kornia.augmentation as K
 import piq
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 import torch
 import torch.nn as nn
 import torch.optim as optim
 import torchvision
 import torch_optimizer as toptim
 from losses import EdgeLoss, FLIP, FLIPLoss, PencilSketchLoss
-from pytorch_lightning.loggers import CometLogger, TensorBoardLogger
+from lightning.pytorch.loggers import CometLogger, TensorBoardLogger
 from robust_loss_pytorch import AdaptiveImageLossFunction
 from torch import is_tensor
 

predict.py

@@ -6,8 +6,8 @@
 
 import numpy as np
 import torch
-from pytorch_lightning import Trainer, seed_everything
-from pytorch_lightning.loggers import CometLogger, TensorBoardLogger
+from lightning.pytorch import Trainer, seed_everything
+from lightning.pytorch.loggers import CometLogger, TensorBoardLogger
 
 import models
 from srdata import SRData

srdata.py

@@ -7,7 +7,7 @@
 import numpy as np
 from PIL import Image
 from PIL.Image import Image as Img
-from pytorch_lightning import LightningDataModule
+from lightning.pytorch import LightningDataModule
 from torch import Tensor
 from torch.utils.data import ConcatDataset, DataLoader, Dataset
 from torchvision.transforms import functional as TF

train.py

@@ -6,10 +6,10 @@
 
 import numpy as np
 import torch
-from pytorch_lightning import Trainer, seed_everything
-from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.callbacks import TQDMProgressBar
-from pytorch_lightning.loggers import CometLogger, TensorBoardLogger
+from lightning.pytorch import Trainer, seed_everything
+from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks import TQDMProgressBar
+from lightning.pytorch.loggers import CometLogger, TensorBoardLogger
 
 import models
 from srdata import SRData
@@ -23,14 +23,14 @@ class ItemsProgressBar(TQDMProgressBar):
     - **sanity check progress:** the progress during the sanity check run
     - **main progress:** shows training + validation progress combined. It also accounts for
       multiple validation runs during training when
-      :paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval` is used.
+      :paramref:`~lightning.pytorch.trainer.trainer.Trainer.val_check_interval` is used.
     - **validation progress:** only visible during validation;
       shows total progress over all validation datasets.
     - **test progress:** only active when testing; shows total progress over all test datasets.
     For infinite datasets, the progress bar never ends.
     If you want to customize the default ``tqdm`` progress bars used by Lightning, you can override
     specific methods of the callback class and pass your custom implementation to the
-    :class:`~pytorch_lightning.trainer.trainer.Trainer`:
+    :class:`~lightning.pytorch.trainer.trainer.Trainer`:
     Example::
         class LitProgressBar(ProgressBar):
             def init_validation_tqdm(self):
@@ -43,16 +43,16 @@ def init_validation_tqdm(self):
         refresh_rate:
             Determines at which rate (in number of batches) the progress bars get updated.
             Set it to ``0`` to disable the display. By default, the
-            :class:`~pytorch_lightning.trainer.trainer.Trainer` uses this implementation of the progress
+            :class:`~lightning.pytorch.trainer.trainer.Trainer` uses this implementation of the progress
             bar and sets the refresh rate to the value provided to the
-            :paramref:`~pytorch_lightning.trainer.trainer.Trainer.progress_bar_refresh_rate` argument in the
-            :class:`~pytorch_lightning.trainer.trainer.Trainer`.
+            :paramref:`~lightning.pytorch.trainer.trainer.Trainer.progress_bar_refresh_rate` argument in the
+            :class:`~lightning.pytorch.trainer.trainer.Trainer`.
         process_position:
             Set this to a value greater than ``0`` to offset the progress bars by this many lines.
             This is useful when you have progress bars defined elsewhere and want to show all of them
             together. This corresponds to
-            :paramref:`~pytorch_lightning.trainer.trainer.Trainer.process_position` in the
-            :class:`~pytorch_lightning.trainer.trainer.Trainer`.
+            :paramref:`~lightning.pytorch.trainer.trainer.Trainer.process_position` in the
+            :class:`~lightning.pytorch.trainer.trainer.Trainer`.
     """
 
     def __init__(self, refresh_rate: int = 1, process_position: int = 0, batch_size: int = 16):