Deep-Learning-with-PyTorch

Updating the training script for segmentation
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When we add similar code to our classification training loop in the next chapter, we’ll
use the F1 score.
Back in the main training loop, we’ll keep track of the best_score we’ve seen so
far in this training run. When we save our model, we’ll include a flag that indicates
whether this is the best score we’ve seen so far. Recall from section 13.6.4 that we’re
only calling the doValidation function for the first and then every fifth epochs. That
means we’re only going to check for a best score on those epochs. That shouldn’t be a
problem, but it’s something to keep in mind if you need to debug something happening
on epoch 7. We do this checking just before we save the images.
Listing 13.33
training.py:210, SegmentationTrainingApp.main
def main(self):
The epoch-loop
best_score = 0.0
we already saw
for epoch_ndx in range(1, self.cli_args.epochs + 1):
# if validation is wanted
# ... line 233
valMetrics_t = self.doValidation(epoch_ndx, val_dl)
score = self.logMetrics(epoch_ndx, 'val', valMetrics_t)
best_score = max(score, best_score)
Computes the
score. As we saw
earlier, we take
the recall.
self.saveModel('seg', epoch_ndx, score == best_score)
Now we only need to write saveModel. The third parameter
is whether we want to save it as best model, too.
Let’s take a look at how we persist our model to disk.
13.6.6 Saving our model
PyTorch makes it pretty easy to save our model to disk. Under the hood, torch.save
uses the standard Python pickle library, which means we could pass our model
instance in directly, and it would save properly. That’s not considered the ideal way to
persist our model, however, since we lose some flexibility.
Instead, we will save only the parameters of our model. Doing this allows us to load
those parameters into any model that expects parameters of the same shape, even if
the class doesn’t match the model those parameters were saved under. The saveparameters-only
approach allows us to reuse and remix our models in more ways than
saving the entire model.
We can get at our model’s parameters using the model.state_dict() function.
Listing 13.34
training.py:480, .saveModel
def saveModel(self, type_str, epoch_ndx, isBest=False):
# ... line 496
model = self.segmentation_model
if isinstance(model, torch.nn.DataParallel):
model = model.module
Gets rid of the DataParallel
wrapper, if it exists