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

Author: Daniel Dale

CHANGELOG.md

@@ -35,9 +35,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `clip_grad_by_value` support for TPUs ([#7025](https://github.com/PyTorchLightning/pytorch-lightning/pull/7025))
 
 
-- Added support for passing any class to `is_overridden` ([#7918](https://github.com/PyTorchLightning/pytorch-lightning/pull/7918))
-
-
 - Added `sub_dir` parameter to `TensorBoardLogger` ([#6195](https://github.com/PyTorchLightning/pytorch-lightning/pull/6195))
 
 
@@ -71,9 +68,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added trainer stage hooks for Training Plugins and Accelerators ([#7864](https://github.com/PyTorchLightning/pytorch-lightning/pull/7864))
 
 
-- Added IPU Accelerator ([#7867](https://github.com/PyTorchLightning/pytorch-lightning/pull/7867))
-
-
 - Added a warning if `Trainer(log_every_n_steps)` is a value too high for the training dataloader ([#7734](https://github.com/PyTorchLightning/pytorch-lightning/pull/7734))
 
 
@@ -178,12 +172,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `self.log(sync_dist_op)` in favor of `self.log(reduce_fx)`. ([#7891](https://github.com/PyTorchLightning/pytorch-lightning/pull/7891))
 
 
-- Deprecated `is_overridden(model=...)` in favor of `is_overridden(instance=...)` ([#7918](https://github.com/PyTorchLightning/pytorch-lightning/pull/7918))
-
-
-- Deprecated default value of `monitor` argument in EarlyStopping callback to enforce `monitor` as a required argument ([#7907](https://github.com/PyTorchLightning/pytorch-lightning/pull/7907))
-
-
 ### Removed
 
 - Removed `ProfilerConnector` ([#7654](https://github.com/PyTorchLightning/pytorch-lightning/pull/7654))
@@ -208,6 +196,7 @@ 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)