Deep-Learning-with-PyTorch

424 CHAPTER 14 End-to-end nodule analysis, and where to go next
Tail Backbone HEAD
Luna Model Architecture
ChaNnels: 64
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--finetune-Depth=1
--finetune-Depth=2
ChaNnels: 16
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ChaNnels: 1
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Filter
SmaLler
Output
Input
Image
Figure 14.8
The model architecture from chapter 11, with the depth-1 and depth-2 weights highlighted
not be ideal for our problem, but we expect them to be a reasonable starting point.
This is easy: we add some loading code into our model setup.
Listing 14.9
training.py:124, .initModel
Filters out top-level modules
that have parameters (as
opposed to the final activation)
d = torch.load(self.cli_args.finetune, map_location='cpu')
model_blocks = [
n for n, subm in model.named_children() Takes the last finetune_depth blocks.
if len(list(subm.parameters())) > 0
The default (if fine-tuning) is 1.
]
finetune_blocks = model_blocks[-self.cli_args.finetune_depth:]
model.load_state_dict(
{
k: v for k,v in d['model_state'].items()
if k.split('.')[0] not in model_blocks[-1]
},
Filters out the last block (the final
linear part) and does not load it.
Starting from a fully initialized model
would have us begin with (almost) all
nodules labeled as malignant,
because that output means “nodule”
in the classifier we start from.