def __init__(self, config): import aimaker.models.model_factory as mf import aimaker.loss.loss_factory as lf import aimaker.optimizers.optimizer_factory as of self.config = config self.ch = ch = util.ConfigHandler(config) self.gpu_ids = ch.get_GPU_ID() self.checkpoints_dir = ch.get_check_points_dir() self.pool = util.ImagePool( int(ch.settings['controller']['voxcelFlow']['imagePoolSize'])) self.lambda_1 = float(config['controller']['voxcelFlow']['lambda1']) self.lambda_2 = float(config['controller']['voxcelFlow']['lambda2']) model_factory = mf.ModelFactory(config) loss_factory = lf.LossFactory(config) optimizer_factory = of.OptimizerFactory(config) name = config['controller']['voxcelFlow']['generatorModel'] self.generator = model_factory.create(name) self.generator_criterion = loss_factory.create(\ config['controller']['voxcelFlow']['generatorCriterion']) if len(self.gpu_ids): self.generator = self.generator.cuda(self.gpu_ids[0]) if config['data']['isTrain']: name = config['controller']['voxcelFlow']['discriminatorModel'] self.discriminator = model_factory.create(name) if len(self.gpu_ids): self.discriminator = self.discriminator.cuda(self.gpu_ids[0]) self.loadModels() if config['data']['isTrain']: self.discriminator_criterion = loss_factory.create(\ config['controller']['voxcelFlow']\ ['discriminatorCriterion']) if len(self.gpu_ids): self.generator_criterion = self.generator_criterion.cuda( self.gpu_ids[0]) self.discriminator_criterion = self.discriminator_criterion.cuda( self.gpu_ids[0]) self.generator_optimizer = optimizer_factory.create(\ config['controller']['voxcelFlow']['generatorOptimizer'])\ (self.generator.parameters(), config) self.discriminator_optimizer = optimizer_factory.create(config['controller']['voxcelFlow']['discriminatorOptimizer'])\ (self.discriminator.parameters(), config) if config['ui settings']['isShowModelInfo']: self.showModel()
def __init__(self, settings: EasyDict): import aimaker.models.model_factory as mf import aimaker.loss.loss_factory as lf import aimaker.optimizers.optimizer_factory as of self.settings = settings ch = util.ConfigHandler(settings) self.gpu_ids = ch.get_GPU_ID() self.checkpoints_dir = ch.get_check_points_dir() self.pool = util.ImagePool( int(ch.settings['annotation']['imagePoolSize'])) model_factory = mf.ModelFactory(settings) loss_factory = lf.LossFactory(settings) optimizer_factory = of.OptimizerFactory(settings) name = settings['annotation']['generatorModel'] self.downsampler = nn.AvgPool2d(8) self.upsampler = nn.Upsample(scale_factor=8) if not self.load_models(): self.generator = model_factory.create(name) if settings['dataset'].getboolean('isTrain'): name = settings['annotation']['discriminatorModel'] self.discriminator = model_factory.create(name) self.generator_criterion = loss_factory \ .create(settings['annotation']['generatorCriterion']) if len(self.gpu_ids): self.generator = self.generator.cuda(self.gpu_ids[0]) if settings['dataset'].getboolean('isTrain'): if len(self.gpu_ids): self.discriminator = self.discriminator.cuda(self.gpu_ids[0]) self.discriminator_criterion = loss_factory.create( settings['annotation']['discriminatorCriterion']) if len(self.gpu_ids): self.generator_criterion = self.generator_criterion.cuda( self.gpu_ids[0]) self.discriminator_criterion = self.discriminator_criterion.cuda( self.gpu_ids[0]) self.generator_optimizer = optimizer_factory.create( \ settings['annotation']['generatorOptimizer']) \ (self.generator.parameters(), settings) self.discriminator_optimizer = optimizer_factory.create(settings['annotation']['discriminatorOptimizer']) \ (self.discriminator.parameters(), settings) if settings['ui'].getboolean('isShowModelInfo'): self.show_model()
def __init__(self, setting): import aimaker.models.model_factory as mf import aimaker.loss.loss_factory as lf import aimaker.optimizers.optimizer_factory as of self.setting = setting ch = util.SettingHandler(setting) self.gpu_ids = ch.get_GPU_ID() self.sheckpoints_dir = ch.get_check_points_dir() self.pool = util.ImagePool( int(ch.setting['controllers']['pix2pix']['imagePoolSize'])) model_factory = mf.ModelFactory(setting) loss_factory = lf.LossFactory(setting) optimizer_factory = of.OptimizerFactory(setting) name = setting['controllers']['pix2pix']['generatorModel'] self.is_feature = setting['controllers']['pix2pix']['isFeature'] if not self.loadModels(): self.generator = model_factory.create(name) if setting['data']['base']['isTrain']: name = setting['controllers']['pix2pix']['discriminatorModel'] self.discriminator = model_factory.create(name) self.generator_criterion = loss_factory\ .create(setting['controllers']['pix2pix']['generatorCriterion']) if len(self.gpu_ids): self.generator = self.generator.to(self.gpu_ids[1]) if setting['data']['base']['isTrain']: if len(self.gpu_ids): self.discriminator = self.discriminator.to(self.gpu_ids[0]) self.discriminator_criterion = loss_factory.create( setting['controllers']['pix2pix']['discriminatorCriterion']) if len(self.gpu_ids): self.generator_criterion = self.generator_criterion.to( self.gpu_ids[1]) self.discriminator_criterion = self.discriminator_criterion.to( self.gpu_ids[1]) self.feature_criterion = loss_factory.create('TF') self.generator_optimizer = optimizer_factory.create(\ setting['controllers']['pix2pix']['generatorOptimizer'])\ (self.generator.parameters(), setting) self.discriminator_optimizer = optimizer_factory.create(setting['controllers']['pix2pix']['discriminatorOptimizer'])\ (self.discriminator.parameters(), setting) if setting['ui']['base']['isShowModelInfo']: self.showModel()