超解像

1 files changed, 0 insertions, 106 deletions

diff --git a/become_yukarin/updater.py b/become_yukarin/updater.py
deleted file mode 100644
index f6444d0..0000000
--- a/become_yukarin/updater.py
+++ /dev/null
@@ -1,106 +0,0 @@
-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])
-
-            # DRAGAN
-            if chainer.config.train:  # grad is not available on test
-                std = xp.std(pair_true.data, axis=0, keepdims=True)
-                rand = xp.random.uniform(0, 1, pair_true.shape).astype(xp.float32)
-                perturb = chainer.Variable(pair_true.data + 0.5 * rand * std)
-                grad, = chainer.grad([self.discriminator(perturb)], [perturb], enable_double_backprop=True)
-                grad = chainer.functions.sqrt(chainer.functions.batch_l2_norm_squared(grad))
-                loss_grad = chainer.functions.mean_squared_error(grad, xp.ones_like(grad.data, numpy.float32))
-                reporter.report({'grad': loss_grad}, self.discriminator)
-
-                if xp.any(xp.isnan(loss_grad.data)):
-                    import code
-                    code.interact(local=locals())
-
-            # GAN
-            d_fake = self.discriminator(pair_fake)
-            d_true = self.discriminator(pair_true)
-            loss_dis_f = chainer.functions.average(chainer.functions.softplus(d_fake))
-            loss_dis_t = chainer.functions.average(chainer.functions.softplus(-d_true))
-            loss_gen_f = chainer.functions.average(chainer.functions.softplus(-d_fake))
-            reporter.report({'fake': loss_dis_f}, self.discriminator)
-            reporter.report({'true': loss_dis_t}, self.discriminator)
-
-            tp = (d_true.data > 0.5).sum()
-            fp = (d_fake.data > 0.5).sum()
-            fn = (d_true.data <= 0.5).sum()
-            tn = (d_fake.data <= 0.5).sum()
-            accuracy = (tp + tn) / (tp + fp + fn + tn)
-            precision = tp / (tp + fp)
-            recall = tp / (tp + fn)
-            reporter.report({'accuracy': accuracy}, self.discriminator)
-            reporter.report({'precision': precision}, self.discriminator)
-            reporter.report({'recall': recall}, 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
-            if chainer.config.train:  # grad is not available on test
-                loss['discriminator'] += loss_grad * self.loss_config.discriminator_grad
-            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)