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# Fine-Tune Framework based on Chainer
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+
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+[Chainer](https://docs.chainer.org/en/latest/glance.html) framework is an easy to use DL framework.
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+It is designed in a hierarchical manner and provides usefull implementations for all of the parts required to train a network:
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+
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+<img src="https://docs.chainer.org/en/latest/_images/trainer1.png">
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+
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+We developed [cvmodelz](https://git.inf-cv.uni-jena.de/ComputerVisionJena/cvmodelz) for fast and easy way of initializing commonly used models ("Model" box), and [cvdatasets](https://git.inf-cv.uni-jena.de/ComputerVisionJena/cvdatasets) provides methods to load dataset annotations and create a dataset object ("Dataset" box) that can be further passed to the iterator.
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+
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+An example training script might be looking like this:
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+
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+```python
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+#!/usr/bin/env python
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+if __name__ != '__main__': raise Exception("Do not import me!")
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+
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+import chainer as ch
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+
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+from chainer import training
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+from chainer.training import extensions
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+
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+from cvargparse import GPUParser
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+from cvargparse import Arg
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+
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+from cvdatasets import FileListAnnotations
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+from cvdatasets import Dataset as BaseDataset
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+
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+from cvmodelz.models import ModelFactory
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+from cvmodelz.classifiers import Classifier
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+
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+
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+class Dataset(BaseDataset):
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+
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+ def __init__(self, *args, prepare, **kw):
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+ super().__init__(*args, **kw)
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+ self._prepare = prepare
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+
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+ def get_example(self, i):
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+ im, _, label = super().get_example(i)
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+ im = self._prepare(im, size=(224, 224))
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+ return im, label
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+
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+
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+def main(args):
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+
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+ model = ModelFactory.new(args.model_type)
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+ clf = Classifier(model)
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+
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+ device = ch.get_device(args.gpu[0])
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+ device.use()
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+
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+ annot = FileListAnnotations(root_or_infofile=args.data_root)
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+ train, test = annot.new_train_test_datasets(
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+ dataset_cls=Dataset, prepare=model.prepare)
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+
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+ train_iter = ch.iterators.MultiprocessIterator(train, batch_size=32, n_processes=4)
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+ test_iter = ch.iterators.MultiprocessIterator(test, batch_size=32, n_processes=4,
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+ repeat=False, shuffle=False)
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+
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+ # Setup an optimizer
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+ optimizer = ch.optimizers.AdamW(alpha=1e-3).setup(clf)
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+
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+ # Create the updater, using the optimizer
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+ updater = training.StandardUpdater(train_iter, optimizer, device=device)
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+
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+ # Set up a trainer
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+ trainer = training.Trainer(updater, (50, 'epoch'), out='result')
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+ # Evaluate the model with the test dataset for each epoch
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+ trainer.extend(extensions.Evaluator(test_iter, clf,
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+ progress_bar=True,
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+ device=device))
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+
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+ trainer.extend(extensions.ProgressBar(update_interval=1))
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+ # Write a log of evaluation statistics for each epoch
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+ trainer.extend(extensions.LogReport())
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+
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+ # Print selected entries of the log to stdout
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+ trainer.extend(extensions.PrintReport(
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+ ['epoch', 'main/loss', 'validation/main/loss',
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+ 'main/accuracy', 'validation/main/accuracy']))
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+
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+
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+ # Run the training
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+ trainer.run()
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+
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+parser = GPUParser([
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+ Arg("data_root"),
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+ Arg("model_type", choices=ModelFactory.get_models(["cvmodelz"])),
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+])
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+
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+main(parser.parse_args())
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+
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+# start it with 'python train.py path/to/dataset cvmodelz.ResNet50'
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+```
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+Everything after the first two lines in the `main` function is chainer-related and has to be done every time one needs to write an experiment.
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+Hence, `cvfinetune` simplifies everything and abstracts the initializations:
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+
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+```python
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+from chainer.training.updaters import StandardUpdater
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+
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+from cvfinetune.finetuner import FinetunerFactory
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+from cvfinetune.training.trainer import Trainer
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+from cvfinetune.parser import default_factory
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+from cvfinetune.parser import FineTuneParser
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+
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+from cvmodelz.classifiers import Classifier
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+
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+from cvdatasets.dataset import AnnotationsReadMixin
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+from cvdatasets.dataset import TransformMixin
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+
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+parser = FineTuneParser(default_factory())
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+
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+class Dataset(TransformMixin, AnnotationsReadMixin):
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+ def __init__(self, *args, prepare, center_crop_on_val: bool = True, **kwargs):
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+ super().__init__(*args, **kwargs)
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+ self.prepare = prepare
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+ self.center_crop_on_val = center_crop_on_val
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+
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+ def transform(self, im_obj):
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+ im, parts, lab = im_obj.as_tuple()
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+ return self.prepare(im), lab + self.label_shift
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+
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+def main(args):
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+ factory = FinetunerFactory(mpi=False)
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+
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+ tuner = factory(args,
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+ classifier_cls=Classifier,
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+ dataset_cls=Dataset,
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+ updater_cls=StandardUpdater,
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+
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+ no_sacred=True,
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+ )
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+ tuner.run(trainer_cls=Trainer, opts=args)
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+
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+
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+main(parser.parse_args())
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+```
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