Python 3.8 branch merged
some small template fixes
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@ -44,11 +44,6 @@ def run_lightning_loop(config_obj):
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# Init
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# Init
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model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
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model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
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model.init_weights(torch.nn.init.xavier_normal_)
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model.init_weights(torch.nn.init.xavier_normal_)
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if model.name == 'CNNRouteGeneratorDiscriminated':
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# ToDo: Make this dependent on the used seed
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path = logger.outpath / 'classifier_cnn' / 'version_0'
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disc_model = SavedLightningModels.load_checkpoint(path).restore()
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model.set_discriminator(disc_model)
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# Trainer
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# Trainer
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# =============================================================================
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# =============================================================================
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@ -70,8 +65,8 @@ def run_lightning_loop(config_obj):
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trainer.fit(model)
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trainer.fit(model)
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# Save the last state & all parameters
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# Save the last state & all parameters
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trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
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trainer.save_checkpoint(config_obj.exp_path.log_dir / 'weights.ckpt')
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model.save_to_disk(logger.log_dir)
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model.save_to_disk(config_obj.exp_path)
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# Evaluate It
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# Evaluate It
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if config_obj.main.eval:
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if config_obj.main.eval:
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@ -1,6 +1,6 @@
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import warnings
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import warnings
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from utils.config import Config
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from _templates.new_project.utils.project_config import Config
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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@ -15,10 +15,9 @@ if __name__ == '__main__':
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# Model Settings
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# Model Settings
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config = Config().read_namespace(args)
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config = Config().read_namespace(args)
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# bias, activation, model, norm, max_epochs, filters
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# bias, activation, model, norm, max_epochs
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cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
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cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, data_batchsize=512)
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model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
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# bias, activation, model, norm, max_epochs
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# bias, activation, model, norm, max_epochs, sr, feature_mixed_dim, filters
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for arg_dict in [cnn_classifier]:
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for arg_dict in [cnn_classifier]:
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for seed in range(5):
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for seed in range(5):
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