config.py

"""
Mask R-CNN
Base Configurations class.

Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""

import numpy as np


# Base Configuration Class
# Don't use this class directly. Instead, sub-class it and override
# the configurations you need to change.


class Config(object):
    """Base configuration class. For custom configurations, create a
    sub-class that inherits from this one and override properties
    that need to be changed.
    """

    # Name the configurations. For example, 'COCO', 'Experiment 3', ...etc.
    # Useful if your code needs to do things differently depending on which
    # experiment is running.
    NAME = None  # Override in sub-classes

    # NUMBER OF GPUs to use. When using only a CPU, this needs to be set to 1.
    GPU_COUNT = 1

    # Number of images to train with on each GPU. A 12GB GPU can typically
    # handle 2 images of 1024x1024px.
    # Adjust based on your GPU memory and image sizes. Use the highest
    # number that your GPU can handle for best performance.
    IMAGES_PER_GPU = 2

    # Number of training steps per epoch
    # This doesn't need to match the size of the training set. Tensorboard
    # updates are saved at the end of each epoch, so setting this to a
    # smaller number means getting more frequent TensorBoard updates.
    # Validation stats are also calculated at each epoch end and they
    # might take a while, so don't set this too small to avoid spending
    # a lot of time on validation stats.
    STEPS_PER_EPOCH = 1000

    # Number of validation steps to run at the end of every training epoch.
    # A bigger number improves accuracy of validation stats, but slows
    # down the training.
    VALIDATION_STEPS = 50

    # Backbone network architecture
    # Supported values are: resnet50, resnet101.
    # You can also provide a callable that should have the signature
    # of model.resnet_graph. If you do so, you need to supply a callable
    # to COMPUTE_BACKBONE_SHAPE as well
    BACKBONE = "resnet101"

    # Only useful if you supply a callable to BACKBONE. Should compute
    # the shape of each layer of the FPN Pyramid.
    # See model.compute_backbone_shapes
    COMPUTE_BACKBONE_SHAPE = None

    # The strides of each layer of the FPN Pyramid. These values
    # are based on a Resnet101 backbone.
    BACKBONE_STRIDES = [4, 8, 16, 32, 64]

    # Size of the fully-connected layers in the classification graph
    FPN_CLASSIF_FC_LAYERS_SIZE = 1024

    # Size of the top-down layers used to build the feature pyramid
    TOP_DOWN_PYRAMID_SIZE = 256

    # Number of classification classes (including background)
    NUM_CLASSES = 1  # Override in sub-classes

    # Length of square anchor side in pixels
    RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512)

    # Ratios of anchors at each cell (width/height)
    # A value of 1 represents a square anchor, and 0.5 is a wide anchor
    RPN_ANCHOR_RATIOS = [0.5, 1, 2]

    # Anchor stride
    # If 1 then anchors are created for each cell in the backbone feature map.
    # If 2, then anchors are created for every other cell, and so on.
    RPN_ANCHOR_STRIDE = 1

    # Non-max suppression threshold to filter RPN proposals.
    # You can increase this during training to generate more propsals.
    RPN_NMS_THRESHOLD = 0.7

    # How many anchors per image to use for RPN training
    RPN_TRAIN_ANCHORS_PER_IMAGE = 256

    # ROIs kept after tf.nn.top_k and before non-maximum suppression
    PRE_NMS_LIMIT = 6000

    # ROIs kept after non-maximum suppression (training and inference)
    POST_NMS_ROIS_TRAINING = 2000
    POST_NMS_ROIS_INFERENCE = 1000

    # If enabled, resizes instance masks to a smaller size to reduce
    # memory load. Recommended when using high-resolution images.
    USE_MINI_MASK = True
    MINI_MASK_SHAPE = (56, 56)  # (height, width) of the mini-mask

    # Input image resizing
    # Generally, use the "square" resizing mode for training and predicting
    # and it should work well in most cases. In this mode, images are scaled
    # up such that the small side is = IMAGE_MIN_DIM, but ensuring that the
    # scaling doesn't make the long side > IMAGE_MAX_DIM. Then the image is
    # padded with zeros to make it a square so multiple images can be put
    # in one batch.
    # Available resizing modes:
    # none:   No resizing or padding. Return the image unchanged.
    # square: Resize and pad with zeros to get a square image
    #         of size [max_dim, max_dim].
    # pad64:  Pads width and height with zeros to make them multiples of 64.
    #         If IMAGE_MIN_DIM or IMAGE_MIN_SCALE are not None, then it scales
    #         up before padding. IMAGE_MAX_DIM is ignored in this mode.
    #         The multiple of 64 is needed to ensure smooth scaling of feature
    #         maps up and down the 6 levels of the FPN pyramid (2**6=64).
    # crop:   Picks random crops from the image. First, scales the image based
    #         on IMAGE_MIN_DIM and IMAGE_MIN_SCALE, then picks a random crop of
    #         size IMAGE_MIN_DIM x IMAGE_MIN_DIM. Can be used in training only.
    #         IMAGE_MAX_DIM is not used in this mode.
    IMAGE_RESIZE_MODE = "square"
    IMAGE_MIN_DIM = 800
    IMAGE_MAX_DIM = 1024
    # Minimum scaling ratio. Checked after MIN_IMAGE_DIM and can force further
    # up scaling. For example, if set to 2 then images are scaled up to double
    # the width and height, or more, even if MIN_IMAGE_DIM doesn't require it.
    # However, in 'square' mode, it can be overruled by IMAGE_MAX_DIM.
    IMAGE_MIN_SCALE = 0
    # Number of color channels per image. RGB = 3, grayscale = 1, RGB-D = 4
    # Changing this requires other changes in the code. See the WIKI for more
    # details: https://github.com/matterport/Mask_RCNN/wiki
    IMAGE_CHANNEL_COUNT = 3

    # Image mean (RGB)
    MEAN_PIXEL = np.array([123.7, 116.8, 103.9])

    # Number of ROIs per image to feed to classifier/mask heads
    # The Mask RCNN paper uses 512 but often the RPN doesn't generate
    # enough positive proposals to fill this and keep a positive:negative
    # ratio of 1:3. You can increase the number of proposals by adjusting
    # the RPN NMS threshold.
    TRAIN_ROIS_PER_IMAGE = 200

    # Percent of positive ROIs used to train classifier/mask heads
    ROI_POSITIVE_RATIO = 0.33

    # Pooled ROIs
    POOL_SIZE = 7
    MASK_POOL_SIZE = 14

    # Shape of output mask
    # To change this you also need to change the neural network mask branch
    MASK_SHAPE = [28, 28]

    # Maximum number of ground truth instances to use in one image
    MAX_GT_INSTANCES = 100

    # Bounding box refinement standard deviation for RPN and final detections.
    RPN_BBOX_STD_DEV = np.array([0.1, 0.1, 0.2, 0.2])
    BBOX_STD_DEV = np.array([0.1, 0.1, 0.2, 0.2])

    # Max number of final detections
    DETECTION_MAX_INSTANCES = 100

    # Minimum probability value to accept a detected instance
    # ROIs below this threshold are skipped
    DETECTION_MIN_CONFIDENCE = 0.7

    # Non-maximum suppression threshold for detection
    DETECTION_NMS_THRESHOLD = 0.3

    # Learning rate and momentum
    # The Mask RCNN paper uses lr=0.02, but on TensorFlow it causes
    # weights to explode. Likely due to differences in optimizer
    # implementation.
    LEARNING_RATE = 0.001
    LEARNING_MOMENTUM = 0.9

    # Weight decay regularization
    WEIGHT_DECAY = 0.0001

    # Loss weights for more precise optimization.
    # Can be used for R-CNN training setup.
    LOSS_WEIGHTS = {
        "rpn_class_loss": 1.0,
        "rpn_bbox_loss": 1.0,
        "mrcnn_class_loss": 1.0,
        "mrcnn_bbox_loss": 1.0,
        "mrcnn_mask_loss": 1.0,
    }

    # Use RPN ROIs or externally generated ROIs for training
    # Keep this True for most situations. Set to False if you want to train
    # the head branches on ROI generated by code rather than the ROIs from
    # the RPN. For example, to debug the classifier head without having to
    # train the RPN.
    USE_RPN_ROIS = True

    # Train or freeze batch normalization layers
    #     None: Train BN layers. This is the normal mode
    #     False: Freeze BN layers. Good when using a small batch size
    #     True: (don't use). Set layer in training mode even when predicting
    TRAIN_BN = False  # Defaulting to False since batch size is often small

    # Gradient norm clipping
    GRADIENT_CLIP_NORM = 5.0

    def __init__(self):
        """Set values of computed attributes."""
        # Effective batch size
        self.BATCH_SIZE = self.IMAGES_PER_GPU * self.GPU_COUNT

        # Input image size
        if self.IMAGE_RESIZE_MODE == "crop":
            self.IMAGE_SHAPE = np.array(
                [self.IMAGE_MIN_DIM, self.IMAGE_MIN_DIM, self.IMAGE_CHANNEL_COUNT]
            )
        else:
            self.IMAGE_SHAPE = np.array(
                [self.IMAGE_MAX_DIM, self.IMAGE_MAX_DIM, self.IMAGE_CHANNEL_COUNT]
            )

        # Image meta data length
        # See compose_image_meta() for details
        self.IMAGE_META_SIZE = 1 + 3 + 3 + 4 + 1 + self.NUM_CLASSES

    def to_dict(self):
        return {
            a: getattr(self, a)
            for a in sorted(dir(self))
            if not a.startswith("__") and not callable(getattr(self, a))
        }

    def display(self):
        """Display Configuration values."""
        print("\nConfigurations:")
        for key, val in self.to_dict().items():
            print(f"{key:30} {val}")
        # for a in dir(self):
        #     if not a.startswith("__") and not callable(getattr(self, a)):
        #         print("{:30} {}".format(a, getattr(self, a)))
        print("\n")