Fix support for torch Module type hints in LightningCLI (#7807)

* Fixed support for torch Module type hints in LightningCLI

* - Fix issue with serializing values when type hint is Any.
- Run unit test only on newer torchvision versions in which the base class is Module.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Minor change

* Update CHANGELOG.md

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

CHANGELOG.md

@@ -185,6 +185,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a bug where `precision=64` with `accelerator='ddp_spawn'` would throw a pickle error ([#6924](https://github.com/PyTorchLightning/pytorch-lightning/pull/6924))
 
 
+- Fixed support for `torch.nn.Module` type hints in `LightningCLI` ([#7807](https://github.com/PyTorchLightning/pytorch-lightning/pull/7807))
+
+
 ## [1.3.2] - 2021-05-18
 
 ### Changed

requirements/extra.txt

@@ -7,4 +7,4 @@ torchtext>=0.5
 # onnx>=1.7.0
 onnxruntime>=1.3.0
 hydra-core>=1.0
-jsonargparse[signatures]>=3.12.0
+jsonargparse[signatures]>=3.13.1

tests/utilities/test_cli.py

@@ -20,18 +20,26 @@ import sys
 from argparse import Namespace
 from contextlib import redirect_stdout
 from io import StringIO
+from typing import List, Optional
 from unittest import mock
 
 import pytest
+import torch
 import yaml
+from packaging import version
 
 from pytorch_lightning import LightningDataModule, LightningModule, Trainer
 from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
 from pytorch_lightning.plugins.environments import SLURMEnvironment
 from pytorch_lightning.utilities import _TPU_AVAILABLE
 from pytorch_lightning.utilities.cli import LightningArgumentParser, LightningCLI, SaveConfigCallback
+from pytorch_lightning.utilities.imports import _TORCHVISION_AVAILABLE
 from tests.helpers import BoringDataModule, BoringModel
 
+torchvision_version = version.parse('0')
+if _TORCHVISION_AVAILABLE:
+    torchvision_version = version.parse(__import__('torchvision').__version__)
+
 
 @mock.patch('argparse.ArgumentParser.parse_args')
 def test_default_args(mock_argparse, tmpdir):
@@ -443,3 +451,49 @@ def test_lightning_cli_submodules(tmpdir):
     assert cli.model.submodule2 == cli.config_init['model']['submodule2']
     assert isinstance(cli.config_init['model']['submodule1'], BoringModel)
     assert isinstance(cli.config_init['model']['submodule2'], BoringModel)
+
+
+@pytest.mark.skipif(torchvision_version < version.parse('0.8.0'), reason='torchvision>=0.8.0 is required')
+def test_lightning_cli_torch_modules(tmpdir):
+
+    class TestModule(BoringModel):
+
+        def __init__(
+            self,
+            activation: torch.nn.Module = None,
+            transform: Optional[List[torch.nn.Module]] = None,
+        ):
+            super().__init__()
+            self.activation = activation
+            self.transform = transform
+
+    config = """model:
+        activation:
+          class_path: torch.nn.LeakyReLU
+          init_args:
+            negative_slope: 0.2
+        transform:
+          - class_path: torchvision.transforms.Resize
+            init_args:
+              size: 64
+          - class_path: torchvision.transforms.CenterCrop
+            init_args:
+              size: 64
+    """
+    config_path = tmpdir / 'config.yaml'
+    with open(config_path, 'w') as f:
+        f.write(config)
+
+    cli_args = [
+        f'--trainer.default_root_dir={tmpdir}',
+        '--trainer.max_epochs=1',
+        f'--config={str(config_path)}',
+    ]
+
+    with mock.patch('sys.argv', ['any.py'] + cli_args):
+        cli = LightningCLI(TestModule)
+
+    assert isinstance(cli.model.activation, torch.nn.LeakyReLU)
+    assert cli.model.activation.negative_slope == 0.2
+    assert len(cli.model.transform) == 2
+    assert all(isinstance(v, torch.nn.Module) for v in cli.model.transform)