Python tensorpack.logger.set_logger_dir() Examples

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Example #1
Source File: train.py    From hover_net with MIT License 4 votes vote down vote up 
def run_once(self, opt, sess_init=None, save_dir=None):
        ####
        train_datagen = self.get_datagen(opt['train_batch_size'], mode='train')
        valid_datagen = self.get_datagen(opt['infer_batch_size'], mode='valid')

        ###### must be called before ModelSaver
        if save_dir is None:
            logger.set_logger_dir(self.save_dir)
        else:
            logger.set_logger_dir(save_dir)

        ######            
        model_flags = opt['model_flags']
        model = self.get_model()(**model_flags)
        ######
        callbacks=[
                ModelSaver(max_to_keep=opt['nr_epochs']),
        ]

        for param_name, param_info in opt['manual_parameters'].items():
            model.add_manual_variable(param_name, param_info[0])
            callbacks.append(ScheduledHyperParamSetter(param_name, param_info[1]))
        # multi-GPU inference (with mandatory queue prefetch)
        infs = [StatCollector()]
        callbacks.append(DataParallelInferenceRunner(
                                valid_datagen, infs, list(range(nr_gpus))))
        callbacks.append(MaxSaver('valid_dice'))
        
        ######
        steps_per_epoch = train_datagen.size() // nr_gpus

        config = TrainConfig(
                    model           = model,
                    callbacks       = callbacks      ,
                    dataflow        = train_datagen  ,
                    steps_per_epoch = steps_per_epoch,
                    max_epoch       = opt['nr_epochs'],
                )
        config.session_init = sess_init

        launch_train_with_config(config, SyncMultiGPUTrainerParameterServer(nr_gpus))
        tf.reset_default_graph() # remove the entire graph in case of multiple runs
        return
    ####