Fix module dict in base finetuning

CHANGELOG.md

@@ -315,6 +315,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a DDP info message that was never shown ([#8111](https://github.com/PyTorchLightning/pytorch-lightning/pull/8111))
 
 
+- Fixed a bug where an infinite recursion would be triggered when using the `BaseFinetuning` callback on a model that contains a `ModuleDict` ([#8170](https://github.com/PyTorchLightning/pytorch-lightning/pull/8170))
+
 ## [1.3.7] - 2021-06-22
 
 - Fixed a bug where skipping an optimizer while using amp causes amp to trigger an assertion error ([#7975](https://github.com/PyTorchLightning/pytorch-lightning/pull/7975))

pytorch_lightning/callbacks/finetuning.py

@@ -20,7 +20,7 @@
 from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Union
 
 import torch
-from torch.nn import Module
+from torch.nn import Module, ModuleDict
 from torch.nn.modules.batchnorm import _BatchNorm
 from torch.optim.optimizer import Optimizer
 
@@ -114,6 +114,9 @@ def flatten_modules(modules: Union[Module, Iterable[Union[Module, Iterable]]]) -
         Returns:
             List of modules
         """
+        if isinstance(modules, ModuleDict):
+            modules = modules.values()
+
         if isinstance(modules, Iterable):
             _modules = []
             for m in modules:

tests/callbacks/test_finetuning_callback.py

@@ -331,15 +331,17 @@ class ConvBlockParam(nn.Module):
 
         def __init__(self, in_channels, out_channels):
             super().__init__()
-            self.conv = nn.Conv2d(in_channels, out_channels, 3)
-            self.act = nn.ReLU()
+            self.module_dict = nn.ModuleDict({
+                "conv": nn.Conv2d(in_channels, out_channels, 3),
+                "act": nn.ReLU(),
+            })
             # add trivial test parameter to convblock to validate parent (non-leaf) module parameter handling
             self.parent_param = nn.Parameter(torch.zeros((1), dtype=torch.float))
             self.bn = nn.BatchNorm2d(out_channels)
 
         def forward(self, x):
-            x = self.conv(x)
-            x = self.act(x)
+            x = self.module_dict["conv"](x)
+            x = self.module_dict["act"](x)
             return self.bn(x)
 
     model = nn.Sequential(
@@ -353,7 +355,7 @@ def forward(self, x):
     assert len(BaseFinetuning.flatten_modules(model)) == 10
 
     BaseFinetuning.freeze(model.encoder, train_bn=True)
-    assert not model.encoder[0].conv.weight.requires_grad  # Validate a leaf module parameter is frozen
+    assert not model.encoder[0].module_dict["conv"].weight.requires_grad  # Validate a leaf module parameter is frozen
     assert not model.encoder[0].parent_param.requires_grad  # Validate the parent module parameter is frozen
     assert model.encoder[0].bn.weight.requires_grad