Mask RCNN

1.数据集的准备

    # Train or evaluate
    if args.command == "train":
        # Training dataset. Use the training set and 35K from the
        # validation set, as as in the Mask RCNN paper.
        dataset_train = CocoDataset()
        dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download)
        dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)
        dataset_train.prepare()

        # Validation dataset
        dataset_val = CocoDataset()
        dataset_val.load_coco(args.dataset, "minival", year=args.year, auto_download=args.download)
        dataset_val.prepare()

        # *** This training schedule is an example. Update to your needs ***

        # Training - Stage 1
        print("Training network heads")
        model.train_model(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE,
                    epochs=40,
                    layers='heads')

        # Training - Stage 2
        # Finetune layers from ResNet stage 4 and up
        print("Fine tune Resnet stage 4 and up")
        model.train_model(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE,
                    epochs=120,
                    layers='4+')

        # Training - Stage 3
        # Fine tune all layers
        print("Fine tune all layers")
        model.train_model(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE / 10,
                    epochs=160,
                    layers='all')

a.初始化一个类CocoDataset的的对象。

class CocoDataset(utils.Dataset):

此类本身继承自

class Dataset(object):
    """The base class for dataset classes.
    To use it, create a new class that adds functions specific to the dataset
    you want to use. For example:

    class CatsAndDogsDataset(Dataset):
        def load_cats_and_dogs(self):
            ...
        def load_mask(self, image_id):
            ...
        def image_reference(self, image_id):
            ...

    See COCODataset and ShapesDataset as examples.
    """

那么CocoDataset在初始化时来自Dataset的初始化函数：

    def __init__(self, class_map=None):
        self._image_ids = []
        self.image_info = []
        # Background is always the first class
        self.class_info = [{"source": "", "id": 0, "name": "BG"}]
        self.source_class_ids = {}

2.load_coco（）方法

        dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download)
        dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)

    def load_coco(self, dataset_dir, subset, year=DEFAULT_DATASET_YEAR, class_ids=None,
                  class_map=None, return_coco=False, auto_download=False):
        """Load a subset of the COCO dataset.
        dataset_dir: The root directory of the COCO dataset.
        subset: What to load (train, val, minival, valminusminival)
        year: What dataset year to load (2014, 2017) as a string, not an integer
        class_ids: If provided, only loads images that have the given classes.
        class_map: TODO: Not implemented yet. Supports maping classes from
            different datasets to the same class ID.
        return_coco: If True, returns the COCO object.
        auto_download: Automatically download and unzip MS-COCO images and annotations
        """

        if auto_download is True:
            self.auto_download(dataset_dir, subset, year)

        coco = COCO("{}/annotations/instances_{}{}.json".format(dataset_dir, subset, year))
        if subset == "minival" or subset == "valminusminival":
            subset = "val"
        image_dir = "{}/{}{}".format(dataset_dir, subset, year)

        # Load all classes or a subset?
        if not class_ids:
            # All classes
            class_ids = sorted(coco.getCatIds())

        # All images or a subset?
        if class_ids:
            image_ids = []
            for id in class_ids:
                image_ids.extend(list(coco.getImgIds(catIds=[id])))
            # Remove duplicates
            image_ids = list(set(image_id