"""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. For CPU training, use 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

# The strides of each layer of the FPN Pyramid. These values

# are based on a Resnet101 backbone.

BACKBONE_STRIDES = [4, 8, 16, 32, 64]

# 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 reduce 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 non-maximum supression (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 resing

# Images are resized such that the smallest side is >= IMAGE_MIN_DIM and

# the longest side is <= IMAGE_MAX_DIM. In case both conditions can't

# be satisfied together the IMAGE_MAX_DIM is enforced.

IMAGE_MIN_DIM = 800

IMAGE_MAX_DIM = 1024

# If True, pad images with zeros such that they're (max_dim by max_dim)

IMAGE_PADDING = True # currently, the False option is not supported

# 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

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 optimzer

# implementation.

LEARNING_RATE = 0.001

LEARNING_MOMENTUM = 0.9

# Weight decay regularization

WEIGHT_DECAY = 0.0001

# 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

def __init__(self):

"""Set values of computed attributes."""

# Effective batch size

self.BATCH_SIZE = self.IMAGES_PER_GPU * self.GPU_COUNT

# Input image size

self.IMAGE_SHAPE = np.array(

[self.IMAGE_MAX_DIM, self.IMAGE_MAX_DIM, 3])

# Compute backbone size from input image size

self.BACKBONE_SHAPES = np.array(

[[int(math.ceil(self.IMAGE_SHAPE[0] / stride)),

int(math.ceil(self.IMAGE_SHAPE[1] / stride))]

for stride in self.BACKBONE_STRIDES])

def display(self):

"""Display Configuration values."""

print("\nConfigurations:")

for a in dir(self):

if not a.startswith("__") and not callable(getattr(self, a)):

print("{:30} {}".format(a, getattr(self, a)))