コード例 #1
0
    def make(self, cf, valid_gen):
        cb = []

        # Jaccard callback
        if cf.dataset.class_mode == 'segmentation':
            print('   Jaccard metric')
            cb += [Jacc_new(cf.dataset.n_classes)]

        # Save image results
        if cf.save_results_enabled:
            print('   Save image result')
            cb += [
                Save_results(n_classes=cf.dataset.n_classes,
                             void_label=cf.dataset.void_class,
                             save_path=cf.savepath,
                             generator=valid_gen,
                             epoch_length=int(
                                 math.ceil(cf.save_results_nsamples /
                                           float(cf.save_results_batch_size))),
                             color_map=cf.dataset.color_map,
                             classes=cf.dataset.classes,
                             tag='valid')
            ]

        # Early stopping
        if cf.earlyStopping_enabled:
            print('   Early stopping')
            cb += [
                EarlyStopping(monitor=cf.earlyStopping_monitor,
                              mode=cf.earlyStopping_mode,
                              patience=cf.earlyStopping_patience,
                              verbose=cf.earlyStopping_verbose)
            ]

        # Define model saving callbacks
        if cf.checkpoint_enabled:
            print('   Model Checkpoint')
            cb += [
                ModelCheckpoint(
                    filepath=os.path.join(cf.savepath, "weights.hdf5"),
                    verbose=cf.checkpoint_verbose,
                    monitor=cf.checkpoint_monitor,
                    mode=cf.checkpoint_mode,
                    save_best_only=cf.checkpoint_save_best_only,
                    save_weights_only=cf.checkpoint_save_weights_only)
            ]

        # Plot the loss after every epoch.
        if cf.plotHist_enabled:
            print('   Plot per epoch')
            cb += [
                History_plot(cf.dataset.n_classes, cf.savepath,
                             cf.train_metrics, cf.valid_metrics,
                             cf.best_metric, cf.best_type, cf.plotHist_verbose)
            ]

        # Decay learning rate at specific epochs
        if cf.lrDecayScheduler_enabled:
            print('   Learning rate decay scheduler (Deprecated)')
            cb += [
                LRDecayScheduler(cf.lrDecayScheduler_epochs,
                                 cf.lrDecayScheduler_rate)
            ]

        # Save the log
        cb += [
            CSVLogger(os.path.join(cf.savepath, 'logFile.csv'),
                      separator=',',
                      append=False)
        ]

        # Learning rate scheduler
        if cf.LRScheduler_enabled:
            print('   Learning rate cheduler by batch')
            scheduler = Scheduler(cf.LRScheduler_type, cf.learning_rate,
                                  cf.LRScheduler_M, cf.LRScheduler_decay,
                                  cf.LRScheduler_S, cf.LRScheduler_power)

            if cf.LRScheduler_batch_epoch == 'batch':
                cb += [
                    LearningRateSchedulerBatch(scheduler.scheduler_function)
                ]
            elif cf.LRScheduler_batch_epoch == 'epoch':
                cb += [LearningRateScheduler(scheduler.scheduler_function)]
            else:
                raise ValueError('Unknown scheduler mode: ' +
                                 cf.LRScheduler_batch_epoch)

        # TensorBoard callback
        if cf.TensorBoard_enabled:
            print('   Tensorboard')
            if cf.TensorBoard_logs_folder is None:
                log_dir = os.path.join(cf.usr_path, 'TensorBoardLogs')
            if not os.path.exists(log_dir):
                os.makedirs(log_dir)
            cb += [
                TensorBoard(log_dir=log_dir,
                            histogram_freq=cf.TensorBoard_histogram_freq,
                            write_graph=cf.TensorBoard_write_graph,
                            write_images=cf.TensorBoard_write_images)
            ]

        # Output the list of callbacks
        return cb
コード例 #2
0
    def make(self, cf, valid_gen):
        cb = []

        # Jaccard callback
        if cf.dataset.class_mode == 'segmentation':
            print('   Jaccard metric')
            cb += [Jacc_new(cf.dataset.n_classes)]

        # Save image results
        if cf.save_results_enabled:
            print('   Save image result')
            cb += [
                Save_results(n_classes=cf.dataset.n_classes,
                             void_label=cf.dataset.void_class,
                             save_path=cf.savepath,
                             generator=valid_gen,
                             epoch_length=int(
                                 math.ceil(cf.save_results_nsamples /
                                           float(cf.save_results_batch_size))),
                             color_map=cf.dataset.color_map,
                             classes=cf.dataset.classes,
                             tag='valid')
            ]

        # Early stopping
        if cf.earlyStopping_enabled:
            print('   Early stopping')
            cb += [
                EarlyStopping(monitor=cf.earlyStopping_monitor,
                              mode=cf.earlyStopping_mode,
                              patience=cf.earlyStopping_patience,
                              verbose=cf.earlyStopping_verbose)
            ]

        # Define model saving callbacks
        if cf.checkpoint_enabled:
            print('   Model Checkpoint')
            cb += [
                ModelCheckpoint(
                    filepath=os.path.join(cf.savepath, "weights.hdf5"),
                    verbose=cf.checkpoint_verbose,
                    monitor=cf.checkpoint_monitor,
                    mode=cf.checkpoint_mode,
                    save_best_only=cf.checkpoint_save_best_only,
                    save_weights_only=cf.checkpoint_save_weights_only)
            ]

        # Plot the loss after every epoch.
        if cf.plotHist_enabled:
            print('   Plot per epoch')
            cb += [
                History_plot(cf.dataset.n_classes, cf.savepath,
                             cf.train_metrics, cf.valid_metrics,
                             cf.best_metric, cf.best_type, cf.plotHist_verbose)
            ]

        # Save the log
        cb += [
            CSVLogger(os.path.join(cf.savepath, 'logFile.csv'),
                      separator=',',
                      append=False)
        ]

        # Output the list of callbacks
        return cb