init commit

README.md

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 # MaskRCNN
 Implementation of Mask RCNN in PyTorch
+
+Dependency:  
+Python 3.6.3  
+torch-0.3.0.post4  
+torchvision-0.2.0  
+pillow-5.0.0  
+pyyaml-3.12  

config.py

@@ -0,0 +1,166 @@
+"""
+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 math
+
+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. For CPU training, use 1
+    GPU_COUNT = 2
+
+    # 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 = 2  # Override in sub-classes
+
+    # Length of square anchor side in pixels
+    RPN_ANCHOR_SCALES = (2, 4, 8, 16, 32)
+
+    # 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 = 200
+    POST_NMS_ROIS_INFERENCE = 200
+
+    # If enabled, resizes instance masks to a smaller size to reduce
+    # memory load. Recommended when using high-resolution images.
+    USE_MINI_MASK = False
+    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 = 256
+    IMAGE_MAX_DIM = 256
+    # 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]
+
+    # 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)))
+        print("\n")