import chainer

from chainer import Variable
from lib.loss_functions.mae_loss import mae_loss
from lib.loss_functions.mse_loss import mse_loss
from lib.loss_functions.ssim_loss import ssim_loss

loss_functions = {'mse': mse_loss, 'mae': mae_loss, 'ssim': ssim_loss}


class Updater(chainer.training.StandardUpdater):
    def __init__(self, *args, **kwargs):
        self.cae = kwargs.pop('model')
        self.loss_function = kwargs.pop('loss_function')
        super(Updater, self).__init__(*args, **kwargs)

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp

        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]

        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]

        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype("f")
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype("f")

        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])

        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)

8675d3747f41ec8178a224b247de766d213055d7
<mask token>


class Updater(chainer.training.StandardUpdater):
    <mask token>

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp
        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]
        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]
        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype('f')
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype('f')
        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])
        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)

<mask token>


class Updater(chainer.training.StandardUpdater):

    def __init__(self, *args, **kwargs):
        self.cae = kwargs.pop('model')
        self.loss_function = kwargs.pop('loss_function')
        super(Updater, self).__init__(*args, **kwargs)

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp
        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]
        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]
        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype('f')
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype('f')
        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])
        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)

<mask token>
loss_functions = {'mse': mse_loss, 'mae': mae_loss, 'ssim': ssim_loss}


class Updater(chainer.training.StandardUpdater):

    def __init__(self, *args, **kwargs):
        self.cae = kwargs.pop('model')
        self.loss_function = kwargs.pop('loss_function')
        super(Updater, self).__init__(*args, **kwargs)

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp
        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]
        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]
        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype('f')
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype('f')
        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])
        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)

import chainer
from chainer import Variable
from lib.loss_functions.mae_loss import mae_loss
from lib.loss_functions.mse_loss import mse_loss
from lib.loss_functions.ssim_loss import ssim_loss
loss_functions = {'mse': mse_loss, 'mae': mae_loss, 'ssim': ssim_loss}


class Updater(chainer.training.StandardUpdater):

    def __init__(self, *args, **kwargs):
        self.cae = kwargs.pop('model')
        self.loss_function = kwargs.pop('loss_function')
        super(Updater, self).__init__(*args, **kwargs)

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp
        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]
        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]
        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype('f')
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype('f')
        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])
        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)

import chainer

from chainer import Variable
from lib.loss_functions.mae_loss import mae_loss
from lib.loss_functions.mse_loss import mse_loss
from lib.loss_functions.ssim_loss import ssim_loss

loss_functions = {'mse': mse_loss, 'mae': mae_loss, 'ssim': ssim_loss}


class Updater(chainer.training.StandardUpdater):
    def __init__(self, *args, **kwargs):
        self.cae = kwargs.pop('model')
        self.loss_function = kwargs.pop('loss_function')
        super(Updater, self).__init__(*args, **kwargs)

    def loss(self, cae, x, t):
        loss = loss_functions[self.loss_function](x, t)
        chainer.report({'loss': loss}, cae)
        return loss

    def update_core(self):
        optimizer = self.get_optimizer('cae')
        cae = self.cae
        xp = cae.xp

        batch = self.get_iterator('main').next()
        batchsize = len(batch)
        in_ch = batch[0][0].shape[0]
        out_ch = batch[0][1].shape[0]

        w_in = batch[0][0].shape[1]
        w_out = batch[0][1].shape[1]

        x_in = xp.zeros((batchsize, in_ch, w_in, w_in)).astype("f")
        t = xp.zeros((batchsize, out_ch, w_out, w_out)).astype("f")

        for i in range(batchsize):
            x_in[i, :] = xp.asarray(batch[i][0])
            t[i, :] = xp.asarray(batch[i][1])

        x_in = Variable(x_in)
        x_out = cae(x_in)
        optimizer.update(self.loss, cae, x_out, t)
