cvfinetune/finetune/classifier.py

import chainer
import chainer.functions as F
import chainer.links as L

from chainer_addons.models.base import BaseClassifier
import logging

class SeparateModelClassifier(BaseClassifier):
	"""Classifier, that holds two separate models"""
	def __init__(self, *args, **kwargs):
		super(SeparateModelClassifier, self).__init__(*args, **kwargs)

		with self.init_scope():
			self.init_separate_model()

	def init_separate_model(self):

		assert hasattr(self, "model"), \
			"This classifiert has no \"model\" attribute!"

		if hasattr(self, "separate_model"):
			logging.warn("Global Model already initialized! Skipping further execution!")
			return

		self.separate_model = self.model.copy(mode="copy")

	def loader(self, model_loader):

		def inner(n_classes, feat_size):
			# use the given feature size here
			model_loader(n_classes=n_classes, feat_size=feat_size)

			# use the given feature size first ...
			self.separate_model.reinitialize_clf(
				n_classes=n_classes,
				feat_size=feat_size)

			# then copy model params ...
			self.separate_model.copyparams(self.model)

			# now use the default feature size to re-init the classifier
			self.separate_model.reinitialize_clf(
				n_classes=n_classes,
				feat_size=self.feat_size)

		return inner