Method train

model.py:2171–2245

Train the model. train_dataset, val_dataset: Training and validation Dataset objects. learning_rate: The learning rate to train with epochs: Number of training epochs. Note that previous training epochs are considered to be done alreay, so this actually determ

(self, train_dataset, val_dataset, learning_rate, epochs, layers)

            "*epoch*", "{epoch:04d}")

    def train(self, train_dataset, val_dataset, learning_rate, epochs, layers):
        """Train the model.
        train_dataset, val_dataset: Training and validation Dataset objects.
        learning_rate: The learning rate to train with
        epochs: Number of training epochs. Note that previous training epochs
                are considered to be done alreay, so this actually determines
                the epochs to train in total rather than in this particaular
                call.
        layers: Allows selecting wich layers to train. It can be:
            - A regular expression to match layer names to train
            - One of these predefined values:
              heaads: The RPN, classifier and mask heads of the network
              all: All the layers
              3+: Train Resnet stage 3 and up
              4+: Train Resnet stage 4 and up
              5+: Train Resnet stage 5 and up
        """
        assert self.mode == "training", "Create model in training mode."

        # Pre-defined layer regular expressions
        layer_regex = {
            # all layers but the backbone
            "heads": r"(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
            # From a specific Resnet stage and up
            "3+": r"(res3.*)|(bn3.*)|(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
            "4+": r"(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
            "5+": r"(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
            # All layers
            "all": ".*",
        }
        if layers in layer_regex.keys():
            layers = layer_regex[layers]

        # Data generators
        train_generator = data_generator(train_dataset, self.config, shuffle=True,
                                         batch_size=self.config.BATCH_SIZE)
        val_generator = data_generator(val_dataset, self.config, shuffle=True,
                                       batch_size=self.config.BATCH_SIZE,
                                       augment=False)

        # Callbacks
        callbacks = [
            keras.callbacks.TensorBoard(log_dir=self.log_dir,
                                        histogram_freq=0, write_graph=True, write_images=False),
            keras.callbacks.ModelCheckpoint(self.checkpoint_path,
                                            verbose=0, save_weights_only=True),
        ]

        # Train
        log("\nStarting at epoch {}. LR={}\n".format(self.epoch, learning_rate))
        log("Checkpoint Path: {}".format(self.checkpoint_path))
        self.set_trainable(layers)
        self.compile(learning_rate, self.config.LEARNING_MOMENTUM)

        # Work-around for Windows: Keras fails on Windows when using
        # multiprocessing workers. See discussion here:
        # https://github.com/matterport/Mask_RCNN/issues/13#issuecomment-353124009
        if os.name is 'nt':