update broken gan/datamodules tutorial links (#164)

* update both datamodules and basic-gan tutorials to reference stable doc version
* fix show progress

Co-authored-by: Jirka Borovec <[email protected]>

Author: speediedan

lightning_examples/augmentation_kornia/.meta.yml

@@ -11,12 +11,12 @@ description: |
   to perform efficient data augmentation to train a simpple model using the GPU in batch
   mode without additional effort.
 requirements:
-  - pytorch-lightning
   - kornia
   - torchmetrics
   - torchvision
   - matplotlib
   - pandas
+  - seaborn
 accelerator:
   - CPU
   - GPU

lightning_examples/augmentation_kornia/augmentation.py

@@ -4,6 +4,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import seaborn as sn
 import torch
 import torch.nn as nn
 import torchmetrics
@@ -18,6 +19,8 @@
 from torch.utils.data import DataLoader
 from torchvision.datasets import CIFAR10
 
+sn.set()
+
 # %% [markdown]
 # ## Define Data Augmentations module
 #
@@ -100,11 +103,8 @@ def __init__(self):
         super().__init__()
         # not the best model: expereiment yourself
         self.model = torchvision.models.resnet18(pretrained=True)
-
         self.preprocess = Preprocess()  # per sample transforms
-
         self.transform = DataAugmentation()  # per batch augmentation_kornia
-
         self.train_accuracy = torchmetrics.Accuracy()
         self.val_accuracy = torchmetrics.Accuracy()
 
@@ -201,18 +201,12 @@ def val_dataloader(self):
 
 # %%
 metrics = pd.read_csv(f"{trainer.logger.log_dir}/metrics.csv")
-print(metrics.head())
-
-aggreg_metrics = []
-agg_col = "epoch"
-for i, dfg in metrics.groupby(agg_col):
-    agg = dict(dfg.mean())
-    agg[agg_col] = i
-    aggreg_metrics.append(agg)
-
-df_metrics = pd.DataFrame(aggreg_metrics)
-df_metrics[["train_loss", "valid_loss"]].plot(grid=True, legend=True)
-df_metrics[["valid_acc", "train_acc"]].plot(grid=True, legend=True)
+del metrics["step"]
+metrics.set_index("epoch", inplace=True)
+print(metrics.dropna(axis=1, how="all").head())
+g = sn.relplot(data=metrics, kind="line")
+plt.gcf().set_size_inches(12, 4)
+plt.grid()
 
 # %% [markdown]
 # ## Tensorboard

lightning_examples/basic-gan/gan.py

@@ -20,7 +20,7 @@
 # ### MNIST DataModule
 #
 # Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial
-# on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html).
+# on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html).
 
 
 # %%

lightning_examples/datamodules/.meta.yml

@@ -7,7 +7,7 @@ build: 3
 description: This notebook will walk you through how to start using Datamodules. With
   the release of `pytorch-lightning` version 0.9.0, we have included a new class called
   `LightningDataModule` to help you decouple data related hooks from your `LightningModule`.
-  The most up to date documentation on datamodules can be found
+  The most up-to-date documentation on datamodules can be found
   [here](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html).
 requirements:
   - torchvision