replaced test_deep_nested_model with test_complex_nested_model as it was a superset, updated changelog

Author: Daniel Dale

CHANGELOG.md

@@ -208,6 +208,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 ### Fixed
+- Fixed `BaseFinetuning` callback to properly handle parent modules w/ parameters ([#7931](https://github.com/PyTorchLightning/pytorch-lightning/pull/7931))
 
 - Fixed `_check_training_step_output` to be called after `train_step_end` to support more flexible accomodations ([#7868](https://github.com/PyTorchLightning/pytorch-lightning/pull/7868))
 

tests/callbacks/test_finetuning_callback.py

@@ -307,44 +307,7 @@ def configure_optimizers(self):
     trainer.fit(model)
 
 
-def test_deep_nested_model():
-
-    class ConvBlock(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.bn = nn.BatchNorm2d(out_channels)
-
-        def forward(self, x):
-            x = self.conv(x)
-            x = self.act(x)
-            return self.bn(x)
-
-    model = nn.Sequential(
-        OrderedDict([
-            ("encoder", nn.Sequential(ConvBlock(3, 64), ConvBlock(64, 128))),
-            ("decoder", ConvBlock(128, 10)),
-        ])
-    )
-
-    # There's 9 leaf layers in that model
-    assert len(BaseFinetuning.flatten_modules(model)) == 9
-
-    BaseFinetuning.freeze(model.encoder, train_bn=True)
-    assert not model.encoder[0].conv.weight.requires_grad
-    assert model.encoder[0].bn.weight.requires_grad
-
-    BaseFinetuning.make_trainable(model)
-    encoder_params = list(BaseFinetuning.filter_params(model.encoder, train_bn=True))
-    # The 8 parameters of the encoder are:
-    # conv0.weight, conv0.bias, bn0.weight, bn0.bias
-    # conv1.weight, conv1.bias, bn1.weight, bn1.bias
-    assert len(encoder_params) == 8
-
-
-def test_parent_module_w_param_model():
+def test_complex_nested_model():
     """Test flattening, freezing, and thawing of models which contain parent (non-leaf) modules with parameters
     directly themselves rather than exclusively their submodules containing parameters.
     """
@@ -368,7 +331,7 @@ def __init__(self, in_channels, out_channels):
             super().__init__()
             self.conv = nn.Conv2d(in_channels, out_channels, 3)
             self.act = nn.ReLU()
-            # add trivial test parameter to conv block to validate parent (non-leaf) module parameter handling
+            # 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)