Simplify backbone_image_classifier example (#7246)

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

pl_examples/basic_examples/backbone_image_classifier.py

@@ -17,6 +17,7 @@ MNIST backbone image classifier example.
 To run:
 python backbone_image_classifier.py --trainer.max_epochs=50
 """
+from typing import Optional
 
 import torch
 from torch.nn import functional as F
@@ -66,11 +67,13 @@ class LitClassifier(pl.LightningModule):
 
     def __init__(
         self,
-        backbone,
+        backbone: Optional[Backbone] = None,
         learning_rate: float = 0.0001,
     ):
         super().__init__()
-        self.save_hyperparameters()
+        self.save_hyperparameters(ignore=['backbone'])
+        if backbone is None:
+            backbone = Backbone()
         self.backbone = backbone
 
     def forward(self, x):
@@ -124,18 +127,8 @@ class MyDataModule(pl.LightningDataModule):
         return DataLoader(self.mnist_test, batch_size=self.batch_size)
 
 
-class MyLightningCLI(LightningCLI):
-
-    def add_arguments_to_parser(self, parser):
-        parser.add_class_arguments(Backbone, 'model.backbone')
-
-    def instantiate_model(self):
-        self.config_init['model']['backbone'] = Backbone(**self.config['model']['backbone'])
-        super().instantiate_model()
-
-
 def cli_main():
-    cli = MyLightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
+    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
     result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
     print(result)
 

requirements/extra.txt

@@ -7,4 +7,4 @@ torchtext>=0.5
 # onnx>=1.7.0
 onnxruntime>=1.3.0
 hydra-core>=1.0
-jsonargparse[signatures]>=3.10.1
+jsonargparse[signatures]>=3.11.0