1 files changed, 77 insertions, 0 deletions
diff --git a/become_yukarin/updater.py b/become_yukarin/updater.py
new file mode 100644
index 0000000..927601f
--- /dev/null
+++ b/become_yukarin/updater.py
@@ -0,0 +1,77 @@
+import chainer
+import numpy
+from chainer import reporter
+
+from .config import LossConfig
+from .config import ModelConfig
+from .model import Aligner
+from .model import Discriminator
+from .model import Predictor
+
+
+class Updater(chainer.training.StandardUpdater):
+    def __init__(
+            self,
+            loss_config: LossConfig,
+            model_config: ModelConfig,
+            predictor: Predictor,
+            aligner: Aligner = None,
+            discriminator: Discriminator = None,
+            *args,
+            **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.loss_config = loss_config
+        self.model_config = model_config
+        self.predictor = predictor
+        self.aligner = aligner
+        self.discriminator = discriminator
+
+    def forward(self, input, target, mask):
+        xp = self.predictor.xp
+
+        input = chainer.as_variable(input)
+        target = chainer.as_variable(target)
+        mask = chainer.as_variable(mask)
+
+        if self.aligner is not None:
+            input = self.aligner(input)
+        y = self.predictor(input)
+
+        loss_l1 = chainer.functions.sum(chainer.functions.absolute_error(y, target) * mask)
+        loss_l1 = loss_l1 / chainer.functions.sum(mask)
+        reporter.report({'l1': loss_l1}, self.predictor)
+
+        if self.discriminator is not None:
+            pair_fake = chainer.functions.concat([y * mask, input])
+            pair_true = chainer.functions.concat([target * mask, input])
+            d_fake = self.discriminator(pair_fake)
+            d_true = self.discriminator(pair_true)
+            loss_dis_f = chainer.functions.mean_squared_error(d_fake, xp.zeros_like(d_fake.data, numpy.float32))
+            loss_dis_t = chainer.functions.mean_squared_error(d_true, xp.ones_like(d_true.data, numpy.float32))
+            loss_gen_f = chainer.functions.mean_squared_error(d_fake, xp.ones_like(d_fake.data, numpy.float32))
+            reporter.report({'fake': loss_dis_f}, self.discriminator)
+            reporter.report({'true': loss_dis_t}, self.discriminator)
+
+        loss = {'predictor': loss_l1 * self.loss_config.l1}
+
+        if self.aligner is not None:
+            loss['aligner'] = loss_l1 * self.loss_config.l1
+            reporter.report({'loss': loss['aligner']}, self.aligner)
+
+        if self.discriminator is not None:
+            loss['discriminator'] = \
+                loss_dis_f * self.loss_config.discriminator_fake + \
+                loss_dis_t * self.loss_config.discriminator_true
+            reporter.report({'loss': loss['discriminator']}, self.discriminator)
+            loss['predictor'] += loss_gen_f * self.loss_config.predictor_fake
+
+        reporter.report({'loss': loss['predictor']}, self.predictor)
+        return loss
+
+    def update_core(self):
+        batch = self.get_iterator('main').next()
+        loss = self.forward(**self.converter(batch, self.device))
+
+        for k, opt in self.get_all_optimizers().items():
+            opt.update(loss.get, k)