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__init__.py

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config.py

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+"""
+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.,
+        "rpn_bbox_loss": 1.,
+        "mrcnn_class_loss": 1.,
+        "mrcnn_bbox_loss": 1.,
+        "mrcnn_mask_loss": 1.
+    }
+
+    # 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 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")

parallel_model.py

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+"""
+Mask R-CNN
+Multi-GPU Support for Keras.
+
+Copyright (c) 2017 Matterport, Inc.
+Licensed under the MIT License (see LICENSE for details)
+Written by Waleed Abdulla
+
+Ideas and a small code snippets from these sources:
+https://github.com/fchollet/keras/issues/2436
+https://medium.com/@kuza55/transparent-multi-gpu-training-on-tensorflow-with-keras-8b0016fd9012
+https://github.com/avolkov1/keras_experiments/blob/master/keras_exp/multigpu/
+https://github.com/fchollet/keras/blob/master/keras/utils/training_utils.py
+"""
+
+import tensorflow as tf
+import keras.backend as K
+import keras.layers as KL
+import keras.models as KM
+
+
+class ParallelModel(KM.Model):
+    """Subclasses the standard Keras Model and adds multi-GPU support.
+    It works by creating a copy of the model on each GPU. Then it slices
+    the inputs and sends a slice to each copy of the model, and then
+    merges the outputs together and applies the loss on the combined
+    outputs.
+    """
+
+    def __init__(self, keras_model, gpu_count):
+        """Class constructor.
+        keras_model: The Keras model to parallelize
+        gpu_count: Number of GPUs. Must be > 1
+        """
+        self.inner_model = keras_model
+        self.gpu_count = gpu_count
+        merged_outputs = self.make_parallel()
+        super(ParallelModel, self).__init__(inputs=self.inner_model.inputs,
+                                            outputs=merged_outputs)
+
+    def __getattribute__(self, attrname):
+        """Redirect loading and saving methods to the inner model. That's where
+        the weights are stored."""
+        if 'load' in attrname or 'save' in attrname:
+            return getattr(self.inner_model, attrname)
+        return super(ParallelModel, self).__getattribute__(attrname)
+
+    def summary(self, *args, **kwargs):
+        """Override summary() to display summaries of both, the wrapper
+        and inner models."""
+        super(ParallelModel, self).summary(*args, **kwargs)
+        self.inner_model.summary(*args, **kwargs)
+
+    def make_parallel(self):
+        """Creates a new wrapper model that consists of multiple replicas of
+        the original model placed on different GPUs.
+        """
+        # Slice inputs. Slice inputs on the CPU to avoid sending a copy
+        # of the full inputs to all GPUs. Saves on bandwidth and memory.
+        input_slices = {name: tf.split(x, self.gpu_count)
+                        for name, x in zip(self.inner_model.input_names,
+                                           self.inner_model.inputs)}
+
+        output_names = self.inner_model.output_names
+        outputs_all = []
+        for i in range(len(self.inner_model.outputs)):
+            outputs_all.append([])
+
+        # Run the model call() on each GPU to place the ops there
+        for i in range(self.gpu_count):
+            with tf.device('/gpu:%d' % i):
+                with tf.name_scope('tower_%d' % i):
+                    # Run a slice of inputs through this replica
+                    zipped_inputs = zip(self.inner_model.input_names,
+                                        self.inner_model.inputs)
+                    inputs = [
+                        KL.Lambda(lambda s: input_slices[name][i],
+                                  output_shape=lambda s: (None,) + s[1:])(tensor)
+                        for name, tensor in zipped_inputs]
+                    # Create the model replica and get the outputs
+                    outputs = self.inner_model(inputs)
+                    if not isinstance(outputs, list):
+                        outputs = [outputs]
+                    # Save the outputs for merging back together later
+                    for l, o in enumerate(outputs):
+                        outputs_all[l].append(o)
+
+        # Merge outputs on CPU
+        with tf.device('/cpu:0'):
+            merged = []
+            for outputs, name in zip(outputs_all, output_names):
+                # Concatenate or average outputs?
+                # Outputs usually have a batch dimension and we concatenate
+                # across it. If they don't, then the output is likely a loss
+                # or a metric value that gets averaged across the batch.
+                # Keras expects losses and metrics to be scalars.
+                if K.int_shape(outputs[0]) == ():
+                    # Average
+                    m = KL.Lambda(lambda o: tf.add_n(o) / len(outputs), name=name)(outputs)
+                else:
+                    # Concatenate
+                    m = KL.Concatenate(axis=0, name=name)(outputs)
+                merged.append(m)
+        return merged
+
+
+if __name__ == "__main__":
+    # Testing code below. It creates a simple model to train on MNIST and
+    # tries to run it on 2 GPUs. It saves the graph so it can be viewed
+    # in TensorBoard. Run it as:
+    #
+    # python3 parallel_model.py
+
+    import os
+    import numpy as np
+    import keras.optimizers
+    from keras.datasets import mnist
+    from keras.preprocessing.image import ImageDataGenerator
+
+    GPU_COUNT = 2
+
+    # Root directory of the project
+    ROOT_DIR = os.path.abspath("../")
+
+    # Directory to save logs and trained model
+    MODEL_DIR = os.path.join(ROOT_DIR, "logs")
+
+    def build_model(x_train, num_classes):
+        # Reset default graph. Keras leaves old ops in the graph,
+        # which are ignored for execution but clutter graph
+        # visualization in TensorBoard.
+        tf.reset_default_graph()
+
+        inputs = KL.Input(shape=x_train.shape[1:], name="input_image")
+        x = KL.Conv2D(32, (3, 3), activation='relu', padding="same",
+                      name="conv1")(inputs)
+        x = KL.Conv2D(64, (3, 3), activation='relu', padding="same",
+                      name="conv2")(x)
+        x = KL.MaxPooling2D(pool_size=(2, 2), name="pool1")(x)
+        x = KL.Flatten(name="flat1")(x)
+        x = KL.Dense(128, activation='relu', name="dense1")(x)
+        x = KL.Dense(num_classes, activation='softmax', name="dense2")(x)
+
+        return KM.Model(inputs, x, "digit_classifier_model")
+
+    # Load MNIST Data
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train = np.expand_dims(x_train, -1).astype('float32') / 255
+    x_test = np.expand_dims(x_test, -1).astype('float32') / 255
+
+    print('x_train shape:', x_train.shape)
+    print('x_test shape:', x_test.shape)
+
+    # Build data generator and model
+    datagen = ImageDataGenerator()
+    model = build_model(x_train, 10)
+
+    # Add multi-GPU support.
+    model = ParallelModel(model, GPU_COUNT)
+
+    optimizer = keras.optimizers.SGD(lr=0.01, momentum=0.9, clipnorm=5.0)
+
+    model.compile(loss='sparse_categorical_crossentropy',
+                  optimizer=optimizer, metrics=['accuracy'])
+
+    model.summary()
+
+    # Train
+    model.fit_generator(
+        datagen.flow(x_train, y_train, batch_size=64),
+        steps_per_epoch=50, epochs=10, verbose=1,
+        validation_data=(x_test, y_test),
+        callbacks=[keras.callbacks.TensorBoard(log_dir=MODEL_DIR,
+                                               write_graph=True)]
+    )

utils.py

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+"""
+Mask R-CNN
+Common utility functions and classes.
+
+Copyright (c) 2017 Matterport, Inc.
+Licensed under the MIT License (see LICENSE for details)
+Written by Waleed Abdulla
+"""
+
+import sys
+import os
+import logging
+import math
+import random
+import numpy as np
+import tensorflow as tf
+import scipy
+import skimage.color
+import skimage.io
+import skimage.transform
+import urllib.request
+import shutil
+import warnings
+from distutils.version import LooseVersion
+
+# URL from which to download the latest COCO trained weights
+COCO_MODEL_URL = "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5"
+
+
+############################################################
+#  Bounding Boxes
+############################################################
+
+def extract_bboxes(mask):
+    """Compute bounding boxes from masks.
+    mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
+
+    Returns: bbox array [num_instances, (y1, x1, y2, x2)].
+    """
+    boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
+    for i in range(mask.shape[-1]):
+        m = mask[:, :, i]
+        # Bounding box.
+        horizontal_indicies = np.where(np.any(m, axis=0))[0]
+        vertical_indicies = np.where(np.any(m, axis=1))[0]
+        if horizontal_indicies.shape[0]:
+            x1, x2 = horizontal_indicies[[0, -1]]
+            y1, y2 = vertical_indicies[[0, -1]]
+            # x2 and y2 should not be part of the box. Increment by 1.
+            x2 += 1
+            y2 += 1
+        else:
+            # No mask for this instance. Might happen due to
+            # resizing or cropping. Set bbox to zeros
+            x1, x2, y1, y2 = 0, 0, 0, 0
+        boxes[i] = np.array([y1, x1, y2, x2])
+    return boxes.astype(np.int32)
+
+
+def compute_iou(box, boxes, box_area, boxes_area):
+    """Calculates IoU of the given box with the array of the given boxes.
+    box: 1D vector [y1, x1, y2, x2]
+    boxes: [boxes_count, (y1, x1, y2, x2)]
+    box_area: float. the area of 'box'
+    boxes_area: array of length boxes_count.
+
+    Note: the areas are passed in rather than calculated here for
+    efficiency. Calculate once in the caller to avoid duplicate work.
+    """
+    # Calculate intersection areas
+    y1 = np.maximum(box[0], boxes[:, 0])
+    y2 = np.minimum(box[2], boxes[:, 2])
+    x1 = np.maximum(box[1], boxes[:, 1])
+    x2 = np.minimum(box[3], boxes[:, 3])
+    intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0)
+    union = box_area + boxes_area[:] - intersection[:]
+    iou = intersection / union
+    return iou
+
+
+def compute_overlaps(boxes1, boxes2):
+    """Computes IoU overlaps between two sets of boxes.
+    boxes1, boxes2: [N, (y1, x1, y2, x2)].
+
+    For better performance, pass the largest set first and the smaller second.
+    """
+    # Areas of anchors and GT boxes
+    area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
+    area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
+
+    # Compute overlaps to generate matrix [boxes1 count, boxes2 count]
+    # Each cell contains the IoU value.
+    overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0]))
+    for i in range(overlaps.shape[1]):
+        box2 = boxes2[i]
+        overlaps[:, i] = compute_iou(box2, boxes1, area2[i], area1)
+    return overlaps
+
+
+def compute_overlaps_masks(masks1, masks2):
+    """Computes IoU overlaps between two sets of masks.
+    masks1, masks2: [Height, Width, instances]
+    """
+    
+    # If either set of masks is empty return empty result
+    if masks1.shape[-1] == 0 or masks2.shape[-1] == 0:
+        return np.zeros((masks1.shape[-1], masks2.shape[-1]))
+    # flatten masks and compute their areas
+    masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32)
+    masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32)
+    area1 = np.sum(masks1, axis=0)
+    area2 = np.sum(masks2, axis=0)
+
+    # intersections and union
+    intersections = np.dot(masks1.T, masks2)
+    union = area1[:, None] + area2[None, :] - intersections
+    overlaps = intersections / union
+
+    return overlaps
+
+
+def non_max_suppression(boxes, scores, threshold):
+    """Performs non-maximum suppression and returns indices of kept boxes.
+    boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box.
+    scores: 1-D array of box scores.
+    threshold: Float. IoU threshold to use for filtering.
+    """
+    assert boxes.shape[0] > 0
+    if boxes.dtype.kind != "f":
+        boxes = boxes.astype(np.float32)
+
+    # Compute box areas
+    y1 = boxes[:, 0]
+    x1 = boxes[:, 1]
+    y2 = boxes[:, 2]
+    x2 = boxes[:, 3]
+    area = (y2 - y1) * (x2 - x1)
+
+    # Get indicies of boxes sorted by scores (highest first)
+    ixs = scores.argsort()[::-1]
+
+    pick = []
+    while len(ixs) > 0:
+        # Pick top box and add its index to the list
+        i = ixs[0]
+        pick.append(i)
+        # Compute IoU of the picked box with the rest
+        iou = compute_iou(boxes[i], boxes[ixs[1:]], area[i], area[ixs[1:]])
+        # Identify boxes with IoU over the threshold. This
+        # returns indices into ixs[1:], so add 1 to get
+        # indices into ixs.
+        remove_ixs = np.where(iou > threshold)[0] + 1
+        # Remove indices of the picked and overlapped boxes.
+        ixs = np.delete(ixs, remove_ixs)
+        ixs = np.delete(ixs, 0)
+    return np.array(pick, dtype=np.int32)
+
+
+def apply_box_deltas(boxes, deltas):
+    """Applies the given deltas to the given boxes.
+    boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box.
+    deltas: [N, (dy, dx, log(dh), log(dw))]
+    """
+    boxes = boxes.astype(np.float32)
+    # Convert to y, x, h, w
+    height = boxes[:, 2] - boxes[:, 0]
+    width = boxes[:, 3] - boxes[:, 1]
+    center_y = boxes[:, 0] + 0.5 * height
+    center_x = boxes[:, 1] + 0.5 * width
+    # Apply deltas
+    center_y += deltas[:, 0] * height
+    center_x += deltas[:, 1] * width
+    height *= np.exp(deltas[:, 2])
+    width *= np.exp(deltas[:, 3])
+    # Convert back to y1, x1, y2, x2
+    y1 = center_y - 0.5 * height
+    x1 = center_x - 0.5 * width
+    y2 = y1 + height
+    x2 = x1 + width
+    return np.stack([y1, x1, y2, x2], axis=1)
+
+
+def box_refinement_graph(box, gt_box):
+    """Compute refinement needed to transform box to gt_box.
+    box and gt_box are [N, (y1, x1, y2, x2)]
+    """
+    box = tf.cast(box, tf.float32)
+    gt_box = tf.cast(gt_box, tf.float32)
+
+    height = box[:, 2] - box[:, 0]
+    width = box[:, 3] - box[:, 1]
+    center_y = box[:, 0] + 0.5 * height
+    center_x = box[:, 1] + 0.5 * width
+
+    gt_height = gt_box[:, 2] - gt_box[:, 0]
+    gt_width = gt_box[:, 3] - gt_box[:, 1]
+    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
+    gt_center_x = gt_box[:, 1] + 0.5 * gt_width
+
+    dy = (gt_center_y - center_y) / height
+    dx = (gt_center_x - center_x) / width
+    dh = tf.log(gt_height / height)
+    dw = tf.log(gt_width / width)
+
+    result = tf.stack([dy, dx, dh, dw], axis=1)
+    return result
+
+
+def box_refinement(box, gt_box):
+    """Compute refinement needed to transform box to gt_box.
+    box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is
+    assumed to be outside the box.
+    """
+    box = box.astype(np.float32)
+    gt_box = gt_box.astype(np.float32)
+
+    height = box[:, 2] - box[:, 0]
+    width = box[:, 3] - box[:, 1]
+    center_y = box[:, 0] + 0.5 * height
+    center_x = box[:, 1] + 0.5 * width
+
+    gt_height = gt_box[:, 2] - gt_box[:, 0]
+    gt_width = gt_box[:, 3] - gt_box[:, 1]
+    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
+    gt_center_x = gt_box[:, 1] + 0.5 * gt_width
+
+    dy = (gt_center_y - center_y) / height
+    dx = (gt_center_x - center_x) / width
+    dh = np.log(gt_height / height)
+    dw = np.log(gt_width / width)
+
+    return np.stack([dy, dx, dh, dw], axis=1)
+
+
+############################################################
+#  Dataset
+############################################################
+
+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.
+    """
+
+    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 = {}
+
+    def add_class(self, source, class_id, class_name):
+        assert "." not in source, "Source name cannot contain a dot"
+        # Does the class exist already?
+        for info in self.class_info:
+            if info['source'] == source and info["id"] == class_id:
+                # source.class_id combination already available, skip
+                return
+        # Add the class
+        self.class_info.append({
+            "source": source,
+            "id": class_id,
+            "name": class_name,
+        })
+
+    def add_image(self, source, image_id, path, **kwargs):
+        image_info = {
+            "id": image_id,
+            "source": source,
+            "path": path,
+        }
+        image_info.update(kwargs)
+        self.image_info.append(image_info)
+
+    def image_reference(self, image_id):
+        """Return a link to the image in its source Website or details about
+        the image that help looking it up or debugging it.
+
+        Override for your dataset, but pass to this function
+        if you encounter images not in your dataset.
+        """
+        return ""
+
+    def prepare(self, class_map=None):
+        """Prepares the Dataset class for use.
+
+        TODO: class map is not supported yet. When done, it should handle mapping
+              classes from different datasets to the same class ID.
+        """
+
+        def clean_name(name):
+            """Returns a shorter version of object names for cleaner display."""
+            return ",".join(name.split(",")[:1])
+
+        # Build (or rebuild) everything else from the info dicts.
+        self.num_classes = len(self.class_info)
+        self.class_ids = np.arange(self.num_classes)
+        self.class_names = [clean_name(c["name"]) for c in self.class_info]
+        self.num_images = len(self.image_info)
+        self._image_ids = np.arange(self.num_images)
+
+        # Mapping from source class and image IDs to internal IDs
+        self.class_from_source_map = {"{}.{}".format(info['source'], info['id']): id
+                                      for info, id in zip(self.class_info, self.class_ids)}
+        self.image_from_source_map = {"{}.{}".format(info['source'], info['id']): id
+                                      for info, id in zip(self.image_info, self.image_ids)}
+
+        # Map sources to class_ids they support
+        self.sources = list(set([i['source'] for i in self.class_info]))
+        self.source_class_ids = {}
+        # Loop over datasets
+        for source in self.sources:
+            self.source_class_ids[source] = []
+            # Find classes that belong to this dataset
+            for i, info in enumerate(self.class_info):
+                # Include BG class in all datasets
+                if i == 0 or source == info['source']:
+                    self.source_class_ids[source].append(i)
+
+    def map_source_class_id(self, source_class_id):
+        """Takes a source class ID and returns the int class ID assigned to it.
+
+        For example:
+        dataset.map_source_class_id("coco.12") -> 23
+        """
+        return self.class_from_source_map[source_class_id]
+
+    def get_source_class_id(self, class_id, source):
+        """Map an internal class ID to the corresponding class ID in the source dataset."""
+        info = self.class_info[class_id]
+        assert info['source'] == source
+        return info['id']
+
+    @property
+    def image_ids(self):
+        return self._image_ids
+
+    def source_image_link(self, image_id):
+        """Returns the path or URL to the image.
+        Override this to return a URL to the image if it's available online for easy
+        debugging.
+        """
+        return self.image_info[image_id]["path"]
+
+    def load_image(self, image_id):
+        """Load the specified image and return a [H,W,3] Numpy array.
+        """
+        # Load image
+        image = skimage.io.imread(self.image_info[image_id]['path'])
+        # If grayscale. Convert to RGB for consistency.
+        if image.ndim != 3:
+            image = skimage.color.gray2rgb(image)
+        # If has an alpha channel, remove it for consistency
+        if image.shape[-1] == 4:
+            image = image[..., :3]
+        return image
+
+    def load_mask(self, image_id):
+        """Load instance masks for the given image.
+
+        Different datasets use different ways to store masks. Override this
+        method to load instance masks and return them in the form of am
+        array of binary masks of shape [height, width, instances].
+
+        Returns:
+            masks: A bool array of shape [height, width, instance count] with
+                a binary mask per instance.
+            class_ids: a 1D array of class IDs of the instance masks.
+        """
+        # Override this function to load a mask from your dataset.
+        # Otherwise, it returns an empty mask.
+        logging.warning("You are using the default load_mask(), maybe you need to define your own one.")
+        mask = np.empty([0, 0, 0])
+        class_ids = np.empty([0], np.int32)
+        return mask, class_ids
+
+
+def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):
+    """Resizes an image keeping the aspect ratio unchanged.
+
+    min_dim: if provided, resizes the image such that it's smaller
+        dimension == min_dim
+    max_dim: if provided, ensures that the image longest side doesn't
+        exceed this value.
+    min_scale: if provided, ensure that the image is scaled up by at least
+        this percent even if min_dim doesn't require it.
+    mode: Resizing mode.
+        none: No resizing. 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 min_dim or min_scale are provided, it scales the image up
+               before padding. 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 min_dim and min_scale, then picks a random crop of
+              size min_dim x min_dim. Can be used in training only.
+              max_dim is not used in this mode.
+
+    Returns:
+    image: the resized image
+    window: (y1, x1, y2, x2). If max_dim is provided, padding might
+        be inserted in the returned image. If so, this window is the
+        coordinates of the image part of the full image (excluding
+        the padding). The x2, y2 pixels are not included.
+    scale: The scale factor used to resize the image
+    padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]
+    """
+    # Keep track of image dtype and return results in the same dtype
+    image_dtype = image.dtype
+    # Default window (y1, x1, y2, x2) and default scale == 1.
+    h, w = image.shape[:2]
+    window = (0, 0, h, w)
+    scale = 1
+    padding = [(0, 0), (0, 0), (0, 0)]
+    crop = None
+
+    if mode == "none":
+        return image, window, scale, padding, crop
+
+    # Scale?
+    if min_dim:
+        # Scale up but not down
+        scale = max(1, min_dim / min(h, w))
+    if min_scale and scale < min_scale:
+        scale = min_scale
+
+    # Does it exceed max dim?
+    if max_dim and mode == "square":
+        image_max = max(h, w)
+        if round(image_max * scale) > max_dim:
+            scale = max_dim / image_max
+
+    # Resize image using bilinear interpolation
+    if scale != 1:
+        image = resize(image, (round(h * scale), round(w * scale)),
+                       preserve_range=True)
+
+    # Need padding or cropping?
+    if mode == "square":
+        # Get new height and width
+        h, w = image.shape[:2]
+        top_pad = (max_dim - h) // 2
+        bottom_pad = max_dim - h - top_pad
+        left_pad = (max_dim - w) // 2
+        right_pad = max_dim - w - left_pad
+        padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
+        image = np.pad(image, padding, mode='constant', constant_values=0)
+        window = (top_pad, left_pad, h + top_pad, w + left_pad)
+    elif mode == "pad64":
+        h, w = image.shape[:2]
+        # Both sides must be divisible by 64
+        assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64"
+        # Height
+        if h % 64 > 0:
+            max_h = h - (h % 64) + 64
+            top_pad = (max_h - h) // 2
+            bottom_pad = max_h - h - top_pad
+        else:
+            top_pad = bottom_pad = 0
+        # Width
+        if w % 64 > 0:
+            max_w = w - (w % 64) + 64
+            left_pad = (max_w - w) // 2
+            right_pad = max_w - w - left_pad
+        else:
+            left_pad = right_pad = 0
+        padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
+        image = np.pad(image, padding, mode='constant', constant_values=0)
+        window = (top_pad, left_pad, h + top_pad, w + left_pad)
+    elif mode == "crop":
+        # Pick a random crop
+        h, w = image.shape[:2]
+        y = random.randint(0, (h - min_dim))
+        x = random.randint(0, (w - min_dim))
+        crop = (y, x, min_dim, min_dim)
+        image = image[y:y + min_dim, x:x + min_dim]
+        window = (0, 0, min_dim, min_dim)
+    else:
+        raise Exception("Mode {} not supported".format(mode))
+    return image.astype(image_dtype), window, scale, padding, crop
+
+
+def resize_mask(mask, scale, padding, crop=None):
+    """Resizes a mask using the given scale and padding.
+    Typically, you get the scale and padding from resize_image() to
+    ensure both, the image and the mask, are resized consistently.
+
+    scale: mask scaling factor
+    padding: Padding to add to the mask in the form
+            [(top, bottom), (left, right), (0, 0)]
+    """
+    # Suppress warning from scipy 0.13.0, the output shape of zoom() is
+    # calculated with round() instead of int()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0)
+    if crop is not None:
+        y, x, h, w = crop
+        mask = mask[y:y + h, x:x + w]
+    else:
+        mask = np.pad(mask, padding, mode='constant', constant_values=0)
+    return mask
+
+
+def minimize_mask(bbox, mask, mini_shape):
+    """Resize masks to a smaller version to reduce memory load.
+    Mini-masks can be resized back to image scale using expand_masks()
+
+    See inspect_data.ipynb notebook for more details.
+    """
+    mini_mask = np.zeros(mini_shape + (mask.shape[-1],), dtype=bool)
+    for i in range(mask.shape[-1]):
+        # Pick slice and cast to bool in case load_mask() returned wrong dtype
+        m = mask[:, :, i].astype(bool)
+        y1, x1, y2, x2 = bbox[i][:4]
+        m = m[y1:y2, x1:x2]
+        if m.size == 0:
+            raise Exception("Invalid bounding box with area of zero")
+        # Resize with bilinear interpolation
+        m = resize(m, mini_shape)
+        mini_mask[:, :, i] = np.around(m).astype(np.bool)
+    return mini_mask
+
+
+def expand_mask(bbox, mini_mask, image_shape):
+    """Resizes mini masks back to image size. Reverses the change
+    of minimize_mask().
+
+    See inspect_data.ipynb notebook for more details.
+    """
+    mask = np.zeros(image_shape[:2] + (mini_mask.shape[-1],), dtype=bool)
+    for i in range(mask.shape[-1]):
+        m = mini_mask[:, :, i]
+        y1, x1, y2, x2 = bbox[i][:4]
+        h = y2 - y1
+        w = x2 - x1
+        # Resize with bilinear interpolation
+        m = resize(m, (h, w))
+        mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool)
+    return mask
+
+
+# TODO: Build and use this function to reduce code duplication
+def mold_mask(mask, config):
+    pass
+
+
+def unmold_mask(mask, bbox, image_shape):
+    """Converts a mask generated by the neural network to a format similar
+    to its original shape.
+    mask: [height, width] of type float. A small, typically 28x28 mask.
+    bbox: [y1, x1, y2, x2]. The box to fit the mask in.
+
+    Returns a binary mask with the same size as the original image.
+    """
+    threshold = 0.5
+    y1, x1, y2, x2 = bbox
+    mask = resize(mask, (y2 - y1, x2 - x1))
+    mask = np.where(mask >= threshold, 1, 0).astype(np.bool)
+
+    # Put the mask in the right location.
+    full_mask = np.zeros(image_shape[:2], dtype=np.bool)
+    full_mask[y1:y2, x1:x2] = mask
+    return full_mask
+
+
+############################################################
+#  Anchors
+############################################################
+
+def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride):
+    """
+    scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
+    ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
+    shape: [height, width] spatial shape of the feature map over which
+            to generate anchors.
+    feature_stride: Stride of the feature map relative to the image in pixels.
+    anchor_stride: Stride of anchors on the feature map. For example, if the
+        value is 2 then generate anchors for every other feature map pixel.
+    """
+    # Get all combinations of scales and ratios
+    scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
+    scales = scales.flatten()
+    ratios = ratios.flatten()
+
+    # Enumerate heights and widths from scales and ratios
+    heights = scales / np.sqrt(ratios)
+    widths = scales * np.sqrt(ratios)
+
+    # Enumerate shifts in feature space
+    shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
+    shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
+    shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
+
+    # Enumerate combinations of shifts, widths, and heights
+    box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
+    box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
+
+    # Reshape to get a list of (y, x) and a list of (h, w)
+    box_centers = np.stack(
+        [box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
+    box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])
+
+    # Convert to corner coordinates (y1, x1, y2, x2)
+    boxes = np.concatenate([box_centers - 0.5 * box_sizes,
+                            box_centers + 0.5 * box_sizes], axis=1)
+    return boxes
+
+
+def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides,
+                             anchor_stride):
+    """Generate anchors at different levels of a feature pyramid. Each scale
+    is associated with a level of the pyramid, but each ratio is used in
+    all levels of the pyramid.
+
+    Returns:
+    anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted
+        with the same order of the given scales. So, anchors of scale[0] come
+        first, then anchors of scale[1], and so on.
+    """
+    # Anchors
+    # [anchor_count, (y1, x1, y2, x2)]
+    anchors = []
+    for i in range(len(scales)):
+        anchors.append(generate_anchors(scales[i], ratios, feature_shapes[i],
+                                        feature_strides[i], anchor_stride))
+    return np.concatenate(anchors, axis=0)
+
+
+############################################################
+#  Miscellaneous
+############################################################
+
+def trim_zeros(x):
+    """It's common to have tensors larger than the available data and
+    pad with zeros. This function removes rows that are all zeros.
+
+    x: [rows, columns].
+    """
+    assert len(x.shape) == 2
+    return x[~np.all(x == 0, axis=1)]
+
+
+def compute_matches(gt_boxes, gt_class_ids, gt_masks,
+                    pred_boxes, pred_class_ids, pred_scores, pred_masks,
+                    iou_threshold=0.5, score_threshold=0.0):
+    """Finds matches between prediction and ground truth instances.
+
+    Returns:
+        gt_match: 1-D array. For each GT box it has the index of the matched
+                  predicted box.
+        pred_match: 1-D array. For each predicted box, it has the index of
+                    the matched ground truth box.
+        overlaps: [pred_boxes, gt_boxes] IoU overlaps.
+    """
+    # Trim zero padding
+    # TODO: cleaner to do zero unpadding upstream
+    gt_boxes = trim_zeros(gt_boxes)
+    gt_masks = gt_masks[..., :gt_boxes.shape[0]]
+    pred_boxes = trim_zeros(pred_boxes)
+    pred_scores = pred_scores[:pred_boxes.shape[0]]
+    # Sort predictions by score from high to low
+    indices = np.argsort(pred_scores)[::-1]
+    pred_boxes = pred_boxes[indices]
+    pred_class_ids = pred_class_ids[indices]
+    pred_scores = pred_scores[indices]
+    pred_masks = pred_masks[..., indices]
+
+    # Compute IoU overlaps [pred_masks, gt_masks]
+    overlaps = compute_overlaps_masks(pred_masks, gt_masks)
+
+    # Loop through predictions and find matching ground truth boxes
+    match_count = 0
+    pred_match = -1 * np.ones([pred_boxes.shape[0]])
+    gt_match = -1 * np.ones([gt_boxes.shape[0]])
+    for i in range(len(pred_boxes)):
+        # Find best matching ground truth box
+        # 1. Sort matches by score
+        sorted_ixs = np.argsort(overlaps[i])[::-1]
+        # 2. Remove low scores
+        low_score_idx = np.where(overlaps[i, sorted_ixs] < score_threshold)[0]
+        if low_score_idx.size > 0:
+            sorted_ixs = sorted_ixs[:low_score_idx[0]]
+        # 3. Find the match
+        for j in sorted_ixs:
+            # If ground truth box is already matched, go to next one
+            if gt_match[j] > -1:
+                continue
+            # If we reach IoU smaller than the threshold, end the loop
+            iou = overlaps[i, j]
+            if iou < iou_threshold:
+                break
+            # Do we have a match?
+            if pred_class_ids[i] == gt_class_ids[j]:
+                match_count += 1
+                gt_match[j] = i
+                pred_match[i] = j
+                break
+
+    return gt_match, pred_match, overlaps
+
+
+def compute_ap(gt_boxes, gt_class_ids, gt_masks,
+               pred_boxes, pred_class_ids, pred_scores, pred_masks,
+               iou_threshold=0.5):
+    """Compute Average Precision at a set IoU threshold (default 0.5).
+
+    Returns:
+    mAP: Mean Average Precision
+    precisions: List of precisions at different class score thresholds.
+    recalls: List of recall values at different class score thresholds.
+    overlaps: [pred_boxes, gt_boxes] IoU overlaps.
+    """
+    # Get matches and overlaps
+    gt_match, pred_match, overlaps = compute_matches(
+        gt_boxes, gt_class_ids, gt_masks,
+        pred_boxes, pred_class_ids, pred_scores, pred_masks,
+        iou_threshold)
+
+    # Compute precision and recall at each prediction box step
+    precisions = np.cumsum(pred_match > -1) / (np.arange(len(pred_match)) + 1)
+    recalls = np.cumsum(pred_match > -1).astype(np.float32) / len(gt_match)
+
+    # Pad with start and end values to simplify the math
+    precisions = np.concatenate([[0], precisions, [0]])
+    recalls = np.concatenate([[0], recalls, [1]])
+
+    # Ensure precision values decrease but don't increase. This way, the
+    # precision value at each recall threshold is the maximum it can be
+    # for all following recall thresholds, as specified by the VOC paper.
+    for i in range(len(precisions) - 2, -1, -1):
+        precisions[i] = np.maximum(precisions[i], precisions[i + 1])
+
+    # Compute mean AP over recall range
+    indices = np.where(recalls[:-1] != recalls[1:])[0] + 1
+    mAP = np.sum((recalls[indices] - recalls[indices - 1]) *
+                 precisions[indices])
+
+    return mAP, precisions, recalls, overlaps
+
+
+def compute_ap_range(gt_box, gt_class_id, gt_mask,
+                     pred_box, pred_class_id, pred_score, pred_mask,
+                     iou_thresholds=None, verbose=1):
+    """Compute AP over a range or IoU thresholds. Default range is 0.5-0.95."""
+    # Default is 0.5 to 0.95 with increments of 0.05
+    iou_thresholds = iou_thresholds or np.arange(0.5, 1.0, 0.05)
+    
+    # Compute AP over range of IoU thresholds
+    AP = []
+    for iou_threshold in iou_thresholds:
+        ap, precisions, recalls, overlaps =\
+            compute_ap(gt_box, gt_class_id, gt_mask,
+                        pred_box, pred_class_id, pred_score, pred_mask,
+                        iou_threshold=iou_threshold)
+        if verbose:
+            print("AP @{:.2f}:\t {:.3f}".format(iou_threshold, ap))
+        AP.append(ap)
+    AP = np.array(AP).mean()
+    if verbose:
+        print("AP @{:.2f}-{:.2f}:\t {:.3f}".format(
+            iou_thresholds[0], iou_thresholds[-1], AP))
+    return AP
+
+
+def compute_recall(pred_boxes, gt_boxes, iou):
+    """Compute the recall at the given IoU threshold. It's an indication
+    of how many GT boxes were found by the given prediction boxes.
+
+    pred_boxes: [N, (y1, x1, y2, x2)] in image coordinates
+    gt_boxes: [N, (y1, x1, y2, x2)] in image coordinates
+    """
+    # Measure overlaps
+    overlaps = compute_overlaps(pred_boxes, gt_boxes)
+    iou_max = np.max(overlaps, axis=1)
+    iou_argmax = np.argmax(overlaps, axis=1)
+    positive_ids = np.where(iou_max >= iou)[0]
+    matched_gt_boxes = iou_argmax[positive_ids]
+
+    recall = len(set(matched_gt_boxes)) / gt_boxes.shape[0]
+    return recall, positive_ids
+
+
+# ## Batch Slicing
+# Some custom layers support a batch size of 1 only, and require a lot of work
+# to support batches greater than 1. This function slices an input tensor
+# across the batch dimension and feeds batches of size 1. Effectively,
+# an easy way to support batches > 1 quickly with little code modification.
+# In the long run, it's more efficient to modify the code to support large
+# batches and getting rid of this function. Consider this a temporary solution
+def batch_slice(inputs, graph_fn, batch_size, names=None):
+    """Splits inputs into slices and feeds each slice to a copy of the given
+    computation graph and then combines the results. It allows you to run a
+    graph on a batch of inputs even if the graph is written to support one
+    instance only.
+
+    inputs: list of tensors. All must have the same first dimension length
+    graph_fn: A function that returns a TF tensor that's part of a graph.
+    batch_size: number of slices to divide the data into.
+    names: If provided, assigns names to the resulting tensors.
+    """
+    if not isinstance(inputs, list):
+        inputs = [inputs]
+
+    outputs = []
+    for i in range(batch_size):
+        inputs_slice = [x[i] for x in inputs]
+        output_slice = graph_fn(*inputs_slice)
+        if not isinstance(output_slice, (tuple, list)):
+            output_slice = [output_slice]
+        outputs.append(output_slice)
+    # Change outputs from a list of slices where each is
+    # a list of outputs to a list of outputs and each has
+    # a list of slices
+    outputs = list(zip(*outputs))
+
+    if names is None:
+        names = [None] * len(outputs)
+
+    result = [tf.stack(o, axis=0, name=n)
+              for o, n in zip(outputs, names)]
+    if len(result) == 1:
+        result = result[0]
+
+    return result
+
+
+def download_trained_weights(coco_model_path, verbose=1):
+    """Download COCO trained weights from Releases.
+
+    coco_model_path: local path of COCO trained weights
+    """
+    if verbose > 0:
+        print("Downloading pretrained model to " + coco_model_path + " ...")
+    with urllib.request.urlopen(COCO_MODEL_URL) as resp, open(coco_model_path, 'wb') as out:
+        shutil.copyfileobj(resp, out)
+    if verbose > 0:
+        print("... done downloading pretrained model!")
+
+
+def norm_boxes(boxes, shape):
+    """Converts boxes from pixel coordinates to normalized coordinates.
+    boxes: [N, (y1, x1, y2, x2)] in pixel coordinates
+    shape: [..., (height, width)] in pixels
+
+    Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
+    coordinates it's inside the box.
+
+    Returns:
+        [N, (y1, x1, y2, x2)] in normalized coordinates
+    """
+    h, w = shape
+    scale = np.array([h - 1, w - 1, h - 1, w - 1])
+    shift = np.array([0, 0, 1, 1])
+    return np.divide((boxes - shift), scale).astype(np.float32)
+
+
+def denorm_boxes(boxes, shape):
+    """Converts boxes from normalized coordinates to pixel coordinates.
+    boxes: [N, (y1, x1, y2, x2)] in normalized coordinates
+    shape: [..., (height, width)] in pixels
+
+    Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
+    coordinates it's inside the box.
+
+    Returns:
+        [N, (y1, x1, y2, x2)] in pixel coordinates
+    """
+    h, w = shape
+    scale = np.array([h - 1, w - 1, h - 1, w - 1])
+    shift = np.array([0, 0, 1, 1])
+    return np.around(np.multiply(boxes, scale) + shift).astype(np.int32)
+
+
+def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
+           preserve_range=False, anti_aliasing=False, anti_aliasing_sigma=None):
+    """A wrapper for Scikit-Image resize().
+
+    Scikit-Image generates warnings on every call to resize() if it doesn't
+    receive the right parameters. The right parameters depend on the version
+    of skimage. This solves the problem by using different parameters per
+    version. And it provides a central place to control resizing defaults.
+    """
+    if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
+        # New in 0.14: anti_aliasing. Default it to False for backward
+        # compatibility with skimage 0.13.
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range, anti_aliasing=anti_aliasing,
+            anti_aliasing_sigma=anti_aliasing_sigma)
+    else:
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range)

visualize.py

@@ -0,0 +1,500 @@
+"""
+Mask R-CNN
+Display and Visualization Functions.
+
+Copyright (c) 2017 Matterport, Inc.
+Licensed under the MIT License (see LICENSE for details)
+Written by Waleed Abdulla
+"""
+
+import os
+import sys
+import random
+import itertools
+import colorsys
+
+import numpy as np
+from skimage.measure import find_contours
+import matplotlib.pyplot as plt
+from matplotlib import patches,  lines
+from matplotlib.patches import Polygon
+import IPython.display
+
+# Root directory of the project
+ROOT_DIR = os.path.abspath("../")
+
+# Import Mask RCNN
+sys.path.append(ROOT_DIR)  # To find local version of the library
+from mrcnn import utils
+
+
+############################################################
+#  Visualization
+############################################################
+
+def display_images(images, titles=None, cols=4, cmap=None, norm=None,
+                   interpolation=None):
+    """Display the given set of images, optionally with titles.
+    images: list or array of image tensors in HWC format.
+    titles: optional. A list of titles to display with each image.
+    cols: number of images per row
+    cmap: Optional. Color map to use. For example, "Blues".
+    norm: Optional. A Normalize instance to map values to colors.
+    interpolation: Optional. Image interpolation to use for display.
+    """
+    titles = titles if titles is not None else [""] * len(images)
+    rows = len(images) // cols + 1
+    plt.figure(figsize=(14, 14 * rows // cols))
+    i = 1
+    for image, title in zip(images, titles):
+        plt.subplot(rows, cols, i)
+        plt.title(title, fontsize=9)
+        plt.axis('off')
+        plt.imshow(image.astype(np.uint8), cmap=cmap,
+                   norm=norm, interpolation=interpolation)
+        i += 1
+    plt.show()
+
+
+def random_colors(N, bright=True):
+    """
+    Generate random colors.
+    To get visually distinct colors, generate them in HSV space then
+    convert to RGB.
+    """
+    brightness = 1.0 if bright else 0.7
+    hsv = [(i / N, 1, brightness) for i in range(N)]
+    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
+    random.shuffle(colors)
+    return colors
+
+
+def apply_mask(image, mask, color, alpha=0.5):
+    """Apply the given mask to the image.
+    """
+    for c in range(3):
+        image[:, :, c] = np.where(mask == 1,
+                                  image[:, :, c] *
+                                  (1 - alpha) + alpha * color[c] * 255,
+                                  image[:, :, c])
+    return image
+
+
+def display_instances(image, boxes, masks, class_ids, class_names,
+                      scores=None, title="",
+                      figsize=(16, 16), ax=None,
+                      show_mask=True, show_bbox=True,
+                      colors=None, captions=None):
+    """
+    boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
+    masks: [height, width, num_instances]
+    class_ids: [num_instances]
+    class_names: list of class names of the dataset
+    scores: (optional) confidence scores for each box
+    title: (optional) Figure title
+    show_mask, show_bbox: To show masks and bounding boxes or not
+    figsize: (optional) the size of the image
+    colors: (optional) An array or colors to use with each object
+    captions: (optional) A list of strings to use as captions for each object
+    """
+    # Number of instances
+    N = boxes.shape[0]
+    if not N:
+        print("\n*** No instances to display *** \n")
+    else:
+        assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
+
+    # If no axis is passed, create one and automatically call show()
+    auto_show = False
+    if not ax:
+        _, ax = plt.subplots(1, figsize=figsize)
+        auto_show = True
+
+    # Generate random colors
+    colors = colors or random_colors(N)
+
+    # Show area outside image boundaries.
+    height, width = image.shape[:2]
+    ax.set_ylim(height + 10, -10)
+    ax.set_xlim(-10, width + 10)
+    ax.axis('off')
+    ax.set_title(title)
+
+    masked_image = image.astype(np.uint32).copy()
+    for i in range(N):
+        color = colors[i]
+
+        # Bounding box
+        if not np.any(boxes[i]):
+            # Skip this instance. Has no bbox. Likely lost in image cropping.
+            continue
+        y1, x1, y2, x2 = boxes[i]
+        if show_bbox:
+            p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
+                                alpha=0.7, linestyle="dashed",
+                                edgecolor=color, facecolor='none')
+            ax.add_patch(p)
+
+        # Label
+        if not captions:
+            class_id = class_ids[i]
+            score = scores[i] if scores is not None else None
+            label = class_names[class_id]
+            caption = "{} {:.3f}".format(label, score) if score else label
+        else:
+            caption = captions[i]
+        ax.text(x1, y1 + 8, caption,
+                color='w', size=11, backgroundcolor="none")
+
+        # Mask
+        mask = masks[:, :, i]
+        if show_mask:
+            masked_image = apply_mask(masked_image, mask, color)
+
+        # Mask Polygon
+        # Pad to ensure proper polygons for masks that touch image edges.
+        padded_mask = np.zeros(
+            (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
+        padded_mask[1:-1, 1:-1] = mask
+        contours = find_contours(padded_mask, 0.5)
+        for verts in contours:
+            # Subtract the padding and flip (y, x) to (x, y)
+            verts = np.fliplr(verts) - 1
+            p = Polygon(verts, facecolor="none", edgecolor=color)
+            ax.add_patch(p)
+    ax.imshow(masked_image.astype(np.uint8))
+    if auto_show:
+        plt.show()
+
+
+def display_differences(image,
+                        gt_box, gt_class_id, gt_mask,
+                        pred_box, pred_class_id, pred_score, pred_mask,
+                        class_names, title="", ax=None,
+                        show_mask=True, show_box=True,
+                        iou_threshold=0.5, score_threshold=0.5):
+    """Display ground truth and prediction instances on the same image."""
+    # Match predictions to ground truth
+    gt_match, pred_match, overlaps = utils.compute_matches(
+        gt_box, gt_class_id, gt_mask,
+        pred_box, pred_class_id, pred_score, pred_mask,
+        iou_threshold=iou_threshold, score_threshold=score_threshold)
+    # Ground truth = green. Predictions = red
+    colors = [(0, 1, 0, .8)] * len(gt_match)\
+           + [(1, 0, 0, 1)] * len(pred_match)
+    # Concatenate GT and predictions
+    class_ids = np.concatenate([gt_class_id, pred_class_id])
+    scores = np.concatenate([np.zeros([len(gt_match)]), pred_score])
+    boxes = np.concatenate([gt_box, pred_box])
+    masks = np.concatenate([gt_mask, pred_mask], axis=-1)
+    # Captions per instance show score/IoU
+    captions = ["" for m in gt_match] + ["{:.2f} / {:.2f}".format(
+        pred_score[i],
+        (overlaps[i, int(pred_match[i])]
+            if pred_match[i] > -1 else overlaps[i].max()))
+            for i in range(len(pred_match))]
+    # Set title if not provided
+    title = title or "Ground Truth and Detections\n GT=green, pred=red, captions: score/IoU"
+    # Display
+    display_instances(
+        image,
+        boxes, masks, class_ids,
+        class_names, scores, ax=ax,
+        show_bbox=show_box, show_mask=show_mask,
+        colors=colors, captions=captions,
+        title=title)
+
+
+def draw_rois(image, rois, refined_rois, mask, class_ids, class_names, limit=10):
+    """
+    anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates.
+    proposals: [n, 4] the same anchors but refined to fit objects better.
+    """
+    masked_image = image.copy()
+
+    # Pick random anchors in case there are too many.
+    ids = np.arange(rois.shape[0], dtype=np.int32)
+    ids = np.random.choice(
+        ids, limit, replace=False) if ids.shape[0] > limit else ids
+
+    fig, ax = plt.subplots(1, figsize=(12, 12))
+    if rois.shape[0] > limit:
+        plt.title("Showing {} random ROIs out of {}".format(
+            len(ids), rois.shape[0]))
+    else:
+        plt.title("{} ROIs".format(len(ids)))
+
+    # Show area outside image boundaries.
+    ax.set_ylim(image.shape[0] + 20, -20)
+    ax.set_xlim(-50, image.shape[1] + 20)
+    ax.axis('off')
+
+    for i, id in enumerate(ids):
+        color = np.random.rand(3)
+        class_id = class_ids[id]
+        # ROI
+        y1, x1, y2, x2 = rois[id]
+        p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
+                              edgecolor=color if class_id else "gray",
+                              facecolor='none', linestyle="dashed")
+        ax.add_patch(p)
+        # Refined ROI
+        if class_id:
+            ry1, rx1, ry2, rx2 = refined_rois[id]
+            p = patches.Rectangle((rx1, ry1), rx2 - rx1, ry2 - ry1, linewidth=2,
+                                  edgecolor=color, facecolor='none')
+            ax.add_patch(p)
+            # Connect the top-left corners of the anchor and proposal for easy visualization
+            ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
+
+            # Label
+            label = class_names[class_id]
+            ax.text(rx1, ry1 + 8, "{}".format(label),
+                    color='w', size=11, backgroundcolor="none")
+
+            # Mask
+            m = utils.unmold_mask(mask[id], rois[id]
+                                  [:4].astype(np.int32), image.shape)
+            masked_image = apply_mask(masked_image, m, color)
+
+    ax.imshow(masked_image)
+
+    # Print stats
+    print("Positive ROIs: ", class_ids[class_ids > 0].shape[0])
+    print("Negative ROIs: ", class_ids[class_ids == 0].shape[0])
+    print("Positive Ratio: {:.2f}".format(
+        class_ids[class_ids > 0].shape[0] / class_ids.shape[0]))
+
+
+# TODO: Replace with matplotlib equivalent?
+def draw_box(image, box, color):
+    """Draw 3-pixel width bounding boxes on the given image array.
+    color: list of 3 int values for RGB.
+    """
+    y1, x1, y2, x2 = box
+    image[y1:y1 + 2, x1:x2] = color
+    image[y2:y2 + 2, x1:x2] = color
+    image[y1:y2, x1:x1 + 2] = color
+    image[y1:y2, x2:x2 + 2] = color
+    return image
+
+
+def display_top_masks(image, mask, class_ids, class_names, limit=4):
+    """Display the given image and the top few class masks."""
+    to_display = []
+    titles = []
+    to_display.append(image)
+    titles.append("H x W={}x{}".format(image.shape[0], image.shape[1]))
+    # Pick top prominent classes in this image
+    unique_class_ids = np.unique(class_ids)
+    mask_area = [np.sum(mask[:, :, np.where(class_ids == i)[0]])
+                 for i in unique_class_ids]
+    top_ids = [v[0] for v in sorted(zip(unique_class_ids, mask_area),
+                                    key=lambda r: r[1], reverse=True) if v[1] > 0]
+    # Generate images and titles
+    for i in range(limit):
+        class_id = top_ids[i] if i < len(top_ids) else -1
+        # Pull masks of instances belonging to the same class.
+        m = mask[:, :, np.where(class_ids == class_id)[0]]
+        m = np.sum(m * np.arange(1, m.shape[-1] + 1), -1)
+        to_display.append(m)
+        titles.append(class_names[class_id] if class_id != -1 else "-")
+    display_images(to_display, titles=titles, cols=limit + 1, cmap="Blues_r")
+
+
+def plot_precision_recall(AP, precisions, recalls):
+    """Draw the precision-recall curve.
+
+    AP: Average precision at IoU >= 0.5
+    precisions: list of precision values
+    recalls: list of recall values
+    """
+    # Plot the Precision-Recall curve
+    _, ax = plt.subplots(1)
+    ax.set_title("Precision-Recall Curve. AP@50 = {:.3f}".format(AP))
+    ax.set_ylim(0, 1.1)
+    ax.set_xlim(0, 1.1)
+    _ = ax.plot(recalls, precisions)
+
+
+def plot_overlaps(gt_class_ids, pred_class_ids, pred_scores,
+                  overlaps, class_names, threshold=0.5):
+    """Draw a grid showing how ground truth objects are classified.
+    gt_class_ids: [N] int. Ground truth class IDs
+    pred_class_id: [N] int. Predicted class IDs
+    pred_scores: [N] float. The probability scores of predicted classes
+    overlaps: [pred_boxes, gt_boxes] IoU overlaps of predictions and GT boxes.
+    class_names: list of all class names in the dataset
+    threshold: Float. The prediction probability required to predict a class
+    """
+    gt_class_ids = gt_class_ids[gt_class_ids != 0]
+    pred_class_ids = pred_class_ids[pred_class_ids != 0]
+
+    plt.figure(figsize=(12, 10))
+    plt.imshow(overlaps, interpolation='nearest', cmap=plt.cm.Blues)
+    plt.yticks(np.arange(len(pred_class_ids)),
+               ["{} ({:.2f})".format(class_names[int(id)], pred_scores[i])
+                for i, id in enumerate(pred_class_ids)])
+    plt.xticks(np.arange(len(gt_class_ids)),
+               [class_names[int(id)] for id in gt_class_ids], rotation=90)
+
+    thresh = overlaps.max() / 2.
+    for i, j in itertools.product(range(overlaps.shape[0]),
+                                  range(overlaps.shape[1])):
+        text = ""
+        if overlaps[i, j] > threshold:
+            text = "match" if gt_class_ids[j] == pred_class_ids[i] else "wrong"
+        color = ("white" if overlaps[i, j] > thresh
+                 else "black" if overlaps[i, j] > 0
+                 else "grey")
+        plt.text(j, i, "{:.3f}\n{}".format(overlaps[i, j], text),
+                 horizontalalignment="center", verticalalignment="center",
+                 fontsize=9, color=color)
+
+    plt.tight_layout()
+    plt.xlabel("Ground Truth")
+    plt.ylabel("Predictions")
+
+
+def draw_boxes(image, boxes=None, refined_boxes=None,
+               masks=None, captions=None, visibilities=None,
+               title="", ax=None):
+    """Draw bounding boxes and segmentation masks with different
+    customizations.
+
+    boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates.
+    refined_boxes: Like boxes, but draw with solid lines to show
+        that they're the result of refining 'boxes'.
+    masks: [N, height, width]
+    captions: List of N titles to display on each box
+    visibilities: (optional) List of values of 0, 1, or 2. Determine how
+        prominent each bounding box should be.
+    title: An optional title to show over the image
+    ax: (optional) Matplotlib axis to draw on.
+    """
+    # Number of boxes
+    assert boxes is not None or refined_boxes is not None
+    N = boxes.shape[0] if boxes is not None else refined_boxes.shape[0]
+
+    # Matplotlib Axis
+    if not ax:
+        _, ax = plt.subplots(1, figsize=(12, 12))
+
+    # Generate random colors
+    colors = random_colors(N)
+
+    # Show area outside image boundaries.
+    margin = image.shape[0] // 10
+    ax.set_ylim(image.shape[0] + margin, -margin)
+    ax.set_xlim(-margin, image.shape[1] + margin)
+    ax.axis('off')
+
+    ax.set_title(title)
+
+    masked_image = image.astype(np.uint32).copy()
+    for i in range(N):
+        # Box visibility
+        visibility = visibilities[i] if visibilities is not None else 1
+        if visibility == 0:
+            color = "gray"
+            style = "dotted"
+            alpha = 0.5
+        elif visibility == 1:
+            color = colors[i]
+            style = "dotted"
+            alpha = 1
+        elif visibility == 2:
+            color = colors[i]
+            style = "solid"
+            alpha = 1
+
+        # Boxes
+        if boxes is not None:
+            if not np.any(boxes[i]):
+                # Skip this instance. Has no bbox. Likely lost in cropping.
+                continue
+            y1, x1, y2, x2 = boxes[i]
+            p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
+                                  alpha=alpha, linestyle=style,
+                                  edgecolor=color, facecolor='none')
+            ax.add_patch(p)
+
+        # Refined boxes
+        if refined_boxes is not None and visibility > 0:
+            ry1, rx1, ry2, rx2 = refined_boxes[i].astype(np.int32)
+            p = patches.Rectangle((rx1, ry1), rx2 - rx1, ry2 - ry1, linewidth=2,
+                                  edgecolor=color, facecolor='none')
+            ax.add_patch(p)
+            # Connect the top-left corners of the anchor and proposal
+            if boxes is not None:
+                ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
+
+        # Captions
+        if captions is not None:
+            caption = captions[i]
+            # If there are refined boxes, display captions on them
+            if refined_boxes is not None:
+                y1, x1, y2, x2 = ry1, rx1, ry2, rx2
+            ax.text(x1, y1, caption, size=11, verticalalignment='top',
+                    color='w', backgroundcolor="none",
+                    bbox={'facecolor': color, 'alpha': 0.5,
+                          'pad': 2, 'edgecolor': 'none'})
+
+        # Masks
+        if masks is not None:
+            mask = masks[:, :, i]
+            masked_image = apply_mask(masked_image, mask, color)
+            # Mask Polygon
+            # Pad to ensure proper polygons for masks that touch image edges.
+            padded_mask = np.zeros(
+                (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
+            padded_mask[1:-1, 1:-1] = mask
+            contours = find_contours(padded_mask, 0.5)
+            for verts in contours:
+                # Subtract the padding and flip (y, x) to (x, y)
+                verts = np.fliplr(verts) - 1
+                p = Polygon(verts, facecolor="none", edgecolor=color)
+                ax.add_patch(p)
+    ax.imshow(masked_image.astype(np.uint8))
+
+
+def display_table(table):
+    """Display values in a table format.
+    table: an iterable of rows, and each row is an iterable of values.
+    """
+    html = ""
+    for row in table:
+        row_html = ""
+        for col in row:
+            row_html += "<td>{:40}</td>".format(str(col))
+        html += "<tr>" + row_html + "</tr>"
+    html = "<table>" + html + "</table>"
+    IPython.display.display(IPython.display.HTML(html))
+
+
+def display_weight_stats(model):
+    """Scans all the weights in the model and returns a list of tuples
+    that contain stats about each weight.
+    """
+    layers = model.get_trainable_layers()
+    table = [["WEIGHT NAME", "SHAPE", "MIN", "MAX", "STD"]]
+    for l in layers:
+        weight_values = l.get_weights()  # list of Numpy arrays
+        weight_tensors = l.weights  # list of TF tensors
+        for i, w in enumerate(weight_values):
+            weight_name = weight_tensors[i].name
+            # Detect problematic layers. Exclude biases of conv layers.
+            alert = ""
+            if w.min() == w.max() and not (l.__class__.__name__ == "Conv2D" and i == 1):
+                alert += "<span style='color:red'>*** dead?</span>"
+            if np.abs(w.min()) > 1000 or np.abs(w.max()) > 1000:
+                alert += "<span style='color:red'>*** Overflow?</span>"
+            # Add row
+            table.append([
+                weight_name + alert,
+                str(w.shape),
+                "{:+9.4f}".format(w.min()),
+                "{:+10.4f}".format(w.max()),
+                "{:+9.4f}".format(w.std()),
+            ])
+    display_table(table)