diff --git a/mrcnn/model.py b/mrcnn/model.py
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+"""
+MRCNN Particle Detection
+The main Mask R-CNN model implementation.
+
+The source code of "MRCNN Particle Detection" (https://git.rwth-aachen.de/avt-fvt/private/mrcnn-particle-detection)
+is based on the source code of "Mask R-CNN" (https://github.com/matterport/Mask_RCNN).
+
+The source code of "Mask R-CNN" is licensed under the MIT License (MIT).
+Copyright (c) 2017 Matterport, Inc.
+Written by Waleed Abdulla
+
+All source code modifications to the source code of "Mask R-CNN" in "MRCNN Particle Detection"
+are licensed under the Eclipse Public License v2.0 (EPL 2.0).
+Copyright (c) 2022-2023 Fluid Process Engineering (AVT.FVT), RWTH Aachen University
+Edited by Stepan Sibirtsev, Mathias Neufang & Jakob Seiler
+
+The coyprights and license terms are given in LICENSE.
+
+Ideas and a small code snippets were adapted from these sources:
+https://github.com/mat02/Mask_RCNN
+"""
+
+import os
+import random
+import datetime
+import re
+import math
+import logging
+from collections import OrderedDict
+import multiprocessing
+import numpy as np
+import tensorflow as tf
+import keras
+import keras.backend as K
+import keras.layers as KL
+import keras.engine as KE
+import keras.models as KM
+
+from mrcnn import utils
+
+# Requires TensorFlow 1.3+ and Keras 2.0.8+.
+from distutils.version import LooseVersion
+assert LooseVersion(tf.__version__) >= LooseVersion("1.3")
+assert LooseVersion(keras.__version__) >= LooseVersion('2.0.8')
+
+
+############################################################
+# Utility Functions
+############################################################
+
+def log(text, array=None):
+ """Prints a text message. And, optionally, if a Numpy array is provided it
+ prints it's shape, min, and max values.
+ """
+ if array is not None:
+ text = text.ljust(25)
+ text += ("shape: {:20} ".format(str(array.shape)))
+ if array.size:
+ text += ("min: {:10.5f} max: {:10.5f}".format(array.min(),array.max()))
+ else:
+ text += ("min: {:10} max: {:10}".format("",""))
+ text += " {}".format(array.dtype)
+ print(text)
+
+
+class BatchNorm(KL.BatchNormalization):
+ """Extends the Keras BatchNormalization class to allow a central place
+ to make changes if needed.
+
+ Batch normalization has a negative effect on training if batches are small
+ so this layer is often frozen (via setting in Config class) and functions
+ as linear layer.
+ """
+ def call(self, inputs, training=None):
+ """
+ Note about training values:
+ None: Train BN layers. This is the normal mode
+ False: Freeze BN layers. Good when batch size is small
+ True: (don't use). Set layer in training mode even when making inferences
+ """
+ return super(self.__class__, self).call(inputs, training=training)
+
+
+def compute_backbone_shapes(config, image_shape):
+ """Computes the width and height of each stage of the backbone network.
+
+ Returns:
+ [N, (height, width)]. Where N is the number of stages
+ """
+ if callable(config.BACKBONE):
+ return config.COMPUTE_BACKBONE_SHAPE(image_shape)
+
+ # Currently supports ResNet only
+ assert config.BACKBONE in ["resnet50", "resnet101"]
+ return np.array(
+ [[int(math.ceil(image_shape[0] / stride)),
+ int(math.ceil(image_shape[1] / stride))]
+ for stride in config.BACKBONE_STRIDES])
+
+
+############################################################
+# Resnet Graph
+############################################################
+
+# Code adopted from:
+# https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
+
+def identity_block(input_tensor, kernel_size, filters, stage, block,
+ use_bias=True, train_bn=True):
+ """The identity_block is the block that has no conv layer at shortcut
+ # Arguments
+ input_tensor: input tensor
+ kernel_size: default 3, the kernel size of middle conv layer at main path
+ filters: list of integers, the nb_filters of 3 conv layer at main path
+ stage: integer, current stage label, used for generating layer names
+ block: 'a','b'..., current block label, used for generating layer names
+ use_bias: Boolean. To use or not use a bias in conv layers.
+ train_bn: Boolean. Train or freeze Batch Norm layers
+ """
+ nb_filter1, nb_filter2, nb_filter3 = filters
+ conv_name_base = 'res' + str(stage) + block + '_branch'
+ bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+ x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',
+ use_bias=use_bias)(input_tensor)
+ x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
+ name=conv_name_base + '2b', use_bias=use_bias)(x)
+ x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c',
+ use_bias=use_bias)(x)
+ x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
+
+ x = KL.Add()([x, input_tensor])
+ x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
+ return x
+
+
+def conv_block(input_tensor, kernel_size, filters, stage, block,
+ strides=(2, 2), use_bias=True, train_bn=True):
+ """conv_block is the block that has a conv layer at shortcut
+ # Arguments
+ input_tensor: input tensor
+ kernel_size: default 3, the kernel size of middle conv layer at main path
+ filters: list of integers, the nb_filters of 3 conv layer at main path
+ stage: integer, current stage label, used for generating layer names
+ block: 'a','b'..., current block label, used for generating layer names
+ use_bias: Boolean. To use or not use a bias in conv layers.
+ train_bn: Boolean. Train or freeze Batch Norm layers
+ Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
+ And the shortcut should have subsample=(2,2) as well
+ """
+ nb_filter1, nb_filter2, nb_filter3 = filters
+ conv_name_base = 'res' + str(stage) + block + '_branch'
+ bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+ x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,
+ name=conv_name_base + '2a', use_bias=use_bias)(input_tensor)
+ x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
+ name=conv_name_base + '2b', use_bias=use_bias)(x)
+ x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
+ '2c', use_bias=use_bias)(x)
+ x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
+
+ shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,
+ name=conv_name_base + '1', use_bias=use_bias)(input_tensor)
+ shortcut = BatchNorm(name=bn_name_base + '1')(shortcut, training=train_bn)
+
+ x = KL.Add()([x, shortcut])
+ x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
+ return x
+
+
+def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
+ """Build a ResNet graph.
+ architecture: Can be resnet50 or resnet101
+ stage5: Boolean. If False, stage5 of the network is not created
+ train_bn: Boolean. Train or freeze Batch Norm layers
+ """
+ assert architecture in ["resnet50", "resnet101"]
+ # Stage 1
+ x = KL.ZeroPadding2D((3, 3))(input_image)
+ x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
+ x = BatchNorm(name='bn_conv1')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+ C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
+ # Stage 2
+ x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn)
+ x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn)
+ C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn)
+ # Stage 3
+ x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn)
+ x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn)
+ x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn)
+ C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn)
+ # Stage 4
+ x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn)
+ block_count = {"resnet50": 5, "resnet101": 22}[architecture]
+ for i in range(block_count):
+ x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn)
+ C4 = x
+ # Stage 5
+ if stage5:
+ x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn)
+ x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn)
+ C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn)
+ else:
+ C5 = None
+ return [C1, C2, C3, C4, C5]
+
+
+############################################################
+# Proposal Layer
+############################################################
+
+def apply_box_deltas_graph(boxes, deltas):
+ """Applies the given deltas to the given boxes.
+ boxes: [N, (y1, x1, y2, x2)] boxes to update
+ deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply
+ """
+ # 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 *= tf.exp(deltas[:, 2])
+ width *= tf.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
+ result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")
+ return result
+
+
+def clip_boxes_graph(boxes, window):
+ """
+ boxes: [N, (y1, x1, y2, x2)]
+ window: [4] in the form y1, x1, y2, x2
+ """
+ # Split
+ wy1, wx1, wy2, wx2 = tf.split(window, 4)
+ y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)
+ # Clip
+ y1 = tf.maximum(tf.minimum(y1, wy2), wy1)
+ x1 = tf.maximum(tf.minimum(x1, wx2), wx1)
+ y2 = tf.maximum(tf.minimum(y2, wy2), wy1)
+ x2 = tf.maximum(tf.minimum(x2, wx2), wx1)
+ clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes")
+ clipped.set_shape((clipped.shape[0], 4))
+ return clipped
+
+
+class ProposalLayer(KE.Layer):
+ """Receives anchor scores and selects a subset to pass as proposals
+ to the second stage. Filtering is done based on anchor scores and
+ non-max suppression to remove overlaps. It also applies bounding
+ box refinement deltas to anchors.
+
+ Inputs:
+ rpn_probs: [batch, num_anchors, (bg prob, fg prob)]
+ rpn_bbox: [batch, num_anchors, (dy, dx, log(dh), log(dw))]
+ anchors: [batch, num_anchors, (y1, x1, y2, x2)] anchors in normalized coordinates
+
+ Returns:
+ Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)]
+ """
+
+ def __init__(self, proposal_count, nms_threshold, config=None, **kwargs):
+ super(ProposalLayer, self).__init__(**kwargs)
+ self.config = config
+ self.proposal_count = proposal_count
+ self.nms_threshold = nms_threshold
+
+ def call(self, inputs):
+ # Box Scores. Use the foreground class confidence. [Batch, num_rois, 1]
+ scores = inputs[0][:, :, 1]
+ # Box deltas [batch, num_rois, 4]
+ deltas = inputs[1]
+ deltas = deltas * np.reshape(self.config.RPN_BBOX_STD_DEV, [1, 1, 4])
+ # Anchors
+ anchors = inputs[2]
+
+ # Improve performance by trimming to top anchors by score
+ # and doing the rest on the smaller subset.
+ pre_nms_limit = tf.minimum(self.config.PRE_NMS_LIMIT, tf.shape(anchors)[1])
+ ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True,
+ name="top_anchors").indices
+ scores = utils.batch_slice([scores, ix], lambda x, y: tf.gather(x, y),
+ self.config.IMAGES_PER_GPU)
+ deltas = utils.batch_slice([deltas, ix], lambda x, y: tf.gather(x, y),
+ self.config.IMAGES_PER_GPU)
+ pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x),
+ self.config.IMAGES_PER_GPU,
+ names=["pre_nms_anchors"])
+
+ # Apply deltas to anchors to get refined anchors.
+ # [batch, N, (y1, x1, y2, x2)]
+ boxes = utils.batch_slice([pre_nms_anchors, deltas],
+ lambda x, y: apply_box_deltas_graph(x, y),
+ self.config.IMAGES_PER_GPU,
+ names=["refined_anchors"])
+
+ # Clip to image boundaries. Since we're in normalized coordinates,
+ # clip to 0..1 range. [batch, N, (y1, x1, y2, x2)]
+ window = np.array([0, 0, 1, 1], dtype=np.float32)
+ boxes = utils.batch_slice(boxes,
+ lambda x: clip_boxes_graph(x, window),
+ self.config.IMAGES_PER_GPU,
+ names=["refined_anchors_clipped"])
+
+ # Filter out small boxes
+ # According to Xinlei Chen's paper, this reduces detection accuracy
+ # for small objects, so we're skipping it.
+
+ # Non-max suppression
+ def nms(boxes, scores):
+ indices = tf.image.non_max_suppression(
+ boxes, scores, self.proposal_count,
+ self.nms_threshold, name="rpn_non_max_suppression")
+ proposals = tf.gather(boxes, indices)
+ # Pad if needed
+ padding = tf.maximum(self.proposal_count - tf.shape(proposals)[0], 0)
+ proposals = tf.pad(proposals, [(0, padding), (0, 0)])
+ return proposals
+ proposals = utils.batch_slice([boxes, scores], nms,
+ self.config.IMAGES_PER_GPU)
+ return proposals
+
+ def compute_output_shape(self, input_shape):
+ return (None, self.proposal_count, 4)
+
+
+############################################################
+# ROIAlign Layer
+############################################################
+
+def log2_graph(x):
+ """Implementation of Log2. TF doesn't have a native implementation."""
+ return tf.log(x) / tf.log(2.0)
+
+
+class PyramidROIAlign(KE.Layer):
+ """Implements ROI Pooling on multiple levels of the feature pyramid.
+
+ Params:
+ - pool_shape: [pool_height, pool_width] of the output pooled regions. Usually [7, 7]
+
+ Inputs:
+ - boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized
+ coordinates. Possibly padded with zeros if not enough
+ boxes to fill the array.
+ - image_meta: [batch, (meta data)] Image details. See compose_image_meta()
+ - feature_maps: List of feature maps from different levels of the pyramid.
+ Each is [batch, height, width, channels]
+
+ Output:
+ Pooled regions in the shape: [batch, num_boxes, pool_height, pool_width, channels].
+ The width and height are those specific in the pool_shape in the layer
+ constructor.
+ """
+
+ def __init__(self, pool_shape, **kwargs):
+ super(PyramidROIAlign, self).__init__(**kwargs)
+ self.pool_shape = tuple(pool_shape)
+
+ def call(self, inputs):
+ # Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords
+ boxes = inputs[0]
+
+ # Image meta
+ # Holds details about the image. See compose_image_meta()
+ image_meta = inputs[1]
+
+ # Feature Maps. List of feature maps from different level of the
+ # feature pyramid. Each is [batch, height, width, channels]
+ feature_maps = inputs[2:]
+
+ # Assign each ROI to a level in the pyramid based on the ROI area.
+ y1, x1, y2, x2 = tf.split(boxes, 4, axis=2)
+ h = y2 - y1
+ w = x2 - x1
+ # Use shape of first image. Images in a batch must have the same size.
+ image_shape = parse_image_meta_graph(image_meta)['image_shape'][0]
+ # Equation 1 in the Feature Pyramid Networks paper. Account for
+ # the fact that our coordinates are normalized here.
+ # e.g. a 224x224 ROI (in pixels) maps to P4
+ image_area = tf.cast(image_shape[0] * image_shape[1], tf.float32)
+ roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area)))
+ roi_level = tf.minimum(5, tf.maximum(
+ 2, 4 + tf.cast(tf.round(roi_level), tf.int32)))
+ roi_level = tf.squeeze(roi_level, 2)
+
+ # Loop through levels and apply ROI pooling to each. P2 to P5.
+ pooled = []
+ box_to_level = []
+ for i, level in enumerate(range(2, 6)):
+ ix = tf.where(tf.equal(roi_level, level))
+ level_boxes = tf.gather_nd(boxes, ix)
+
+ # Box indices for crop_and_resize.
+ box_indices = tf.cast(ix[:, 0], tf.int32)
+
+ # Keep track of which box is mapped to which level
+ box_to_level.append(ix)
+
+ # Stop gradient propogation to ROI proposals
+ level_boxes = tf.stop_gradient(level_boxes)
+ box_indices = tf.stop_gradient(box_indices)
+
+ # Crop and Resize
+ # From Mask R-CNN paper: "We sample four regular locations, so
+ # that we can evaluate either max or average pooling. In fact,
+ # interpolating only a single value at each bin center (without
+ # pooling) is nearly as effective."
+ #
+ # Here we use the simplified approach of a single value per bin,
+ # which is how it's done in tf.crop_and_resize()
+ # Result: [batch * num_boxes, pool_height, pool_width, channels]
+ pooled.append(tf.image.crop_and_resize(
+ feature_maps[i], level_boxes, box_indices, self.pool_shape,
+ method="bilinear"))
+
+ # Pack pooled features into one tensor
+ pooled = tf.concat(pooled, axis=0)
+
+ # Pack box_to_level mapping into one array and add another
+ # column representing the order of pooled boxes
+ box_to_level = tf.concat(box_to_level, axis=0)
+ box_range = tf.expand_dims(tf.range(tf.shape(box_to_level)[0]), 1)
+ box_to_level = tf.concat([tf.cast(box_to_level, tf.int32), box_range],
+ axis=1)
+
+ # Rearrange pooled features to match the order of the original boxes
+ # Sort box_to_level by batch then box index
+ # TF doesn't have a way to sort by two columns, so merge them and sort.
+ sorting_tensor = box_to_level[:, 0] * 100000 + box_to_level[:, 1]
+ ix = tf.nn.top_k(sorting_tensor, k=tf.shape(
+ box_to_level)[0]).indices[::-1]
+ ix = tf.gather(box_to_level[:, 2], ix)
+ pooled = tf.gather(pooled, ix)
+
+ # Re-add the batch dimension
+ shape = tf.concat([tf.shape(boxes)[:2], tf.shape(pooled)[1:]], axis=0)
+ pooled = tf.reshape(pooled, shape)
+ return pooled
+
+ def compute_output_shape(self, input_shape):
+ return input_shape[0][:2] + self.pool_shape + (input_shape[2][-1], )
+
+
+############################################################
+# Detection Target Layer
+############################################################
+
+def overlaps_graph(boxes1, boxes2):
+ """Computes IoU overlaps between two sets of boxes.
+ boxes1, boxes2: [N, (y1, x1, y2, x2)].
+ """
+ # 1. Tile boxes2 and repeat boxes1. This allows us to compare
+ # every boxes1 against every boxes2 without loops.
+ # TF doesn't have an equivalent to np.repeat() so simulate it
+ # using tf.tile() and tf.reshape.
+ b1 = tf.reshape(tf.tile(tf.expand_dims(boxes1, 1),
+ [1, 1, tf.shape(boxes2)[0]]), [-1, 4])
+ b2 = tf.tile(boxes2, [tf.shape(boxes1)[0], 1])
+ # 2. Compute intersections
+ b1_y1, b1_x1, b1_y2, b1_x2 = tf.split(b1, 4, axis=1)
+ b2_y1, b2_x1, b2_y2, b2_x2 = tf.split(b2, 4, axis=1)
+ y1 = tf.maximum(b1_y1, b2_y1)
+ x1 = tf.maximum(b1_x1, b2_x1)
+ y2 = tf.minimum(b1_y2, b2_y2)
+ x2 = tf.minimum(b1_x2, b2_x2)
+ intersection = tf.maximum(x2 - x1, 0) * tf.maximum(y2 - y1, 0)
+ # 3. Compute unions
+ b1_area = (b1_y2 - b1_y1) * (b1_x2 - b1_x1)
+ b2_area = (b2_y2 - b2_y1) * (b2_x2 - b2_x1)
+ union = b1_area + b2_area - intersection
+ # 4. Compute IoU and reshape to [boxes1, boxes2]
+ iou = intersection / union
+ overlaps = tf.reshape(iou, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]])
+ return overlaps
+
+
+def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config):
+ """Generates detection targets for one image. Subsamples proposals and
+ generates target class IDs, bounding box deltas, and masks for each.
+
+ Inputs:
+ proposals: [POST_NMS_ROIS_TRAINING, (y1, x1, y2, x2)] in normalized coordinates. Might
+ be zero padded if there are not enough proposals.
+ gt_class_ids: [MAX_GT_INSTANCES] int class IDs
+ gt_boxes: [MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized coordinates.
+ gt_masks: [height, width, MAX_GT_INSTANCES] of boolean type.
+
+ Returns: Target ROIs and corresponding class IDs, bounding box shifts,
+ and masks.
+ rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized coordinates
+ class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. Zero padded.
+ deltas: [TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw))]
+ masks: [TRAIN_ROIS_PER_IMAGE, height, width]. Masks cropped to bbox
+ boundaries and resized to neural network output size.
+
+ Note: Returned arrays might be zero padded if not enough target ROIs.
+ """
+ # Assertions
+ asserts = [
+ tf.Assert(tf.greater(tf.shape(proposals)[0], 0), [proposals],
+ name="roi_assertion"),
+ ]
+ with tf.control_dependencies(asserts):
+ proposals = tf.identity(proposals)
+
+ # Remove zero padding
+ proposals, _ = trim_zeros_graph(proposals, name="trim_proposals")
+ gt_boxes, non_zeros = trim_zeros_graph(gt_boxes, name="trim_gt_boxes")
+ gt_class_ids = tf.boolean_mask(gt_class_ids, non_zeros,
+ name="trim_gt_class_ids")
+ gt_masks = tf.gather(gt_masks, tf.where(non_zeros)[:, 0], axis=2,
+ name="trim_gt_masks")
+
+ # Handle COCO crowds
+ # A crowd box in COCO is a bounding box around several instances. Exclude
+ # them from training. A crowd box is given a negative class ID.
+ crowd_ix = tf.where(gt_class_ids < 0)[:, 0]
+ non_crowd_ix = tf.where(gt_class_ids > 0)[:, 0]
+ crowd_boxes = tf.gather(gt_boxes, crowd_ix)
+ gt_class_ids = tf.gather(gt_class_ids, non_crowd_ix)
+ gt_boxes = tf.gather(gt_boxes, non_crowd_ix)
+ gt_masks = tf.gather(gt_masks, non_crowd_ix, axis=2)
+
+ # Compute overlaps matrix [proposals, gt_boxes]
+ overlaps = overlaps_graph(proposals, gt_boxes)
+
+ # Compute overlaps with crowd boxes [proposals, crowd_boxes]
+ crowd_overlaps = overlaps_graph(proposals, crowd_boxes)
+ crowd_iou_max = tf.reduce_max(crowd_overlaps, axis=1)
+ no_crowd_bool = (crowd_iou_max < 0.001)
+
+ # Determine positive and negative ROIs
+ roi_iou_max = tf.reduce_max(overlaps, axis=1)
+ # 1. Positive ROIs are those with >= 0.5 IoU with a GT box
+ positive_roi_bool = (roi_iou_max >= 0.5)
+ positive_indices = tf.where(positive_roi_bool)[:, 0]
+ # 2. Negative ROIs are those with < 0.5 with every GT box. Skip crowds.
+ negative_indices = tf.where(tf.logical_and(roi_iou_max < 0.5, no_crowd_bool))[:, 0]
+
+ # Subsample ROIs. Aim for 33% positive
+ # Positive ROIs
+ positive_count = int(config.TRAIN_ROIS_PER_IMAGE *
+ config.ROI_POSITIVE_RATIO)
+ positive_indices = tf.random_shuffle(positive_indices)[:positive_count]
+ positive_count = tf.shape(positive_indices)[0]
+ # Negative ROIs. Add enough to maintain positive:negative ratio.
+ r = 1.0 / config.ROI_POSITIVE_RATIO
+ negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count
+ negative_indices = tf.random_shuffle(negative_indices)[:negative_count]
+ # Gather selected ROIs
+ positive_rois = tf.gather(proposals, positive_indices)
+ negative_rois = tf.gather(proposals, negative_indices)
+
+ # Assign positive ROIs to GT boxes.
+ positive_overlaps = tf.gather(overlaps, positive_indices)
+ roi_gt_box_assignment = tf.cond(
+ tf.greater(tf.shape(positive_overlaps)[1], 0),
+ true_fn = lambda: tf.argmax(positive_overlaps, axis=1),
+ false_fn = lambda: tf.cast(tf.constant([]),tf.int64)
+ )
+ roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment)
+ roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment)
+
+ # Compute bbox refinement for positive ROIs
+ deltas = utils.box_refinement_graph(positive_rois, roi_gt_boxes)
+ deltas /= config.BBOX_STD_DEV
+
+ # Assign positive ROIs to GT masks
+ # Permute masks to [N, height, width, 1]
+ transposed_masks = tf.expand_dims(tf.transpose(gt_masks, [2, 0, 1]), -1)
+ # Pick the right mask for each ROI
+ roi_masks = tf.gather(transposed_masks, roi_gt_box_assignment)
+
+ # Compute mask targets
+ boxes = positive_rois
+ if config.USE_MINI_MASK:
+ # Transform ROI coordinates from normalized image space
+ # to normalized mini-mask space.
+ y1, x1, y2, x2 = tf.split(positive_rois, 4, axis=1)
+ gt_y1, gt_x1, gt_y2, gt_x2 = tf.split(roi_gt_boxes, 4, axis=1)
+ gt_h = gt_y2 - gt_y1
+ gt_w = gt_x2 - gt_x1
+ y1 = (y1 - gt_y1) / gt_h
+ x1 = (x1 - gt_x1) / gt_w
+ y2 = (y2 - gt_y1) / gt_h
+ x2 = (x2 - gt_x1) / gt_w
+ boxes = tf.concat([y1, x1, y2, x2], 1)
+ box_ids = tf.range(0, tf.shape(roi_masks)[0])
+ masks = tf.image.crop_and_resize(tf.cast(roi_masks, tf.float32), boxes,
+ box_ids,
+ config.MASK_SHAPE)
+ # Remove the extra dimension from masks.
+ masks = tf.squeeze(masks, axis=3)
+
+ # Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with
+ # binary cross entropy loss.
+ masks = tf.round(masks)
+
+ # Append negative ROIs and pad bbox deltas and masks that
+ # are not used for negative ROIs with zeros.
+ rois = tf.concat([positive_rois, negative_rois], axis=0)
+ N = tf.shape(negative_rois)[0]
+ P = tf.maximum(config.TRAIN_ROIS_PER_IMAGE - tf.shape(rois)[0], 0)
+ rois = tf.pad(rois, [(0, P), (0, 0)])
+ roi_gt_boxes = tf.pad(roi_gt_boxes, [(0, N + P), (0, 0)])
+ roi_gt_class_ids = tf.pad(roi_gt_class_ids, [(0, N + P)])
+ deltas = tf.pad(deltas, [(0, N + P), (0, 0)])
+ masks = tf.pad(masks, [[0, N + P], (0, 0), (0, 0)])
+
+ return rois, roi_gt_class_ids, deltas, masks
+
+
+class DetectionTargetLayer(KE.Layer):
+ """Subsamples proposals and generates target box refinement, class_ids,
+ and masks for each.
+
+ Inputs:
+ proposals: [batch, N, (y1, x1, y2, x2)] in normalized coordinates. Might
+ be zero padded if there are not enough proposals.
+ gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs.
+ gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized
+ coordinates.
+ gt_masks: [batch, height, width, MAX_GT_INSTANCES] of boolean type
+
+ Returns: Target ROIs and corresponding class IDs, bounding box shifts,
+ and masks.
+ rois: [batch, TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized
+ coordinates
+ target_class_ids: [batch, TRAIN_ROIS_PER_IMAGE]. Integer class IDs.
+ target_deltas: [batch, TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw)]
+ target_mask: [batch, TRAIN_ROIS_PER_IMAGE, height, width]
+ Masks cropped to bbox boundaries and resized to neural
+ network output size.
+
+ Note: Returned arrays might be zero padded if not enough target ROIs.
+ """
+
+ def __init__(self, config, **kwargs):
+ super(DetectionTargetLayer, self).__init__(**kwargs)
+ self.config = config
+
+ def call(self, inputs):
+ proposals = inputs[0]
+ gt_class_ids = inputs[1]
+ gt_boxes = inputs[2]
+ gt_masks = inputs[3]
+
+ # Slice the batch and run a graph for each slice
+ # TODO: Rename target_bbox to target_deltas for clarity
+ names = ["rois", "target_class_ids", "target_bbox", "target_mask"]
+ outputs = utils.batch_slice(
+ [proposals, gt_class_ids, gt_boxes, gt_masks],
+ lambda w, x, y, z: detection_targets_graph(
+ w, x, y, z, self.config),
+ self.config.IMAGES_PER_GPU, names=names)
+ return outputs
+
+ def compute_output_shape(self, input_shape):
+ return [
+ (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # rois
+ (None, self.config.TRAIN_ROIS_PER_IMAGE), # class_ids
+ (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # deltas
+ (None, self.config.TRAIN_ROIS_PER_IMAGE, self.config.MASK_SHAPE[0],
+ self.config.MASK_SHAPE[1]) # masks
+ ]
+
+ def compute_mask(self, inputs, mask=None):
+ return [None, None, None, None]
+
+
+############################################################
+# Detection Layer
+############################################################
+
+def refine_detections_graph(rois, probs, deltas, window, config):
+ """Refine classified proposals and filter overlaps and return final
+ detections.
+
+ Inputs:
+ rois: [N, (y1, x1, y2, x2)] in normalized coordinates
+ probs: [N, num_classes]. Class probabilities.
+ deltas: [N, num_classes, (dy, dx, log(dh), log(dw))]. Class-specific
+ bounding box deltas.
+ window: (y1, x1, y2, x2) in normalized coordinates. The part of the image
+ that contains the image excluding the padding.
+
+ Returns detections shaped: [num_detections, (y1, x1, y2, x2, class_id, score)] where
+ coordinates are normalized.
+ """
+ # Class IDs per ROI
+ class_ids = tf.argmax(probs, axis=1, output_type=tf.int32)
+ # Class probability of the top class of each ROI
+ indices = tf.stack([tf.range(probs.shape[0]), class_ids], axis=1)
+ class_scores = tf.gather_nd(probs, indices)
+ # Class-specific bounding box deltas
+ deltas_specific = tf.gather_nd(deltas, indices)
+ # Apply bounding box deltas
+ # Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinates
+ refined_rois = apply_box_deltas_graph(
+ rois, deltas_specific * config.BBOX_STD_DEV)
+ # Clip boxes to image window
+ refined_rois = clip_boxes_graph(refined_rois, window)
+
+ # TODO: Filter out boxes with zero area
+
+ # Filter out background boxes
+ keep = tf.where(class_ids > 0)[:, 0]
+ # Filter out low confidence boxes
+ if config.DETECTION_MIN_CONFIDENCE:
+ conf_keep = tf.where(class_scores >= config.DETECTION_MIN_CONFIDENCE)[:, 0]
+ keep = tf.sets.set_intersection(tf.expand_dims(keep, 0),
+ tf.expand_dims(conf_keep, 0))
+ keep = tf.sparse_tensor_to_dense(keep)[0]
+
+ # Apply per-class NMS
+ # 1. Prepare variables
+ pre_nms_class_ids = tf.gather(class_ids, keep)
+ pre_nms_scores = tf.gather(class_scores, keep)
+ pre_nms_rois = tf.gather(refined_rois, keep)
+ unique_pre_nms_class_ids = tf.unique(pre_nms_class_ids)[0]
+
+ def nms_keep_map(class_id):
+ """Apply Non-Maximum Suppression on ROIs of the given class."""
+ # Indices of ROIs of the given class
+ ixs = tf.where(tf.equal(pre_nms_class_ids, class_id))[:, 0]
+ # Apply NMS
+ class_keep = tf.image.non_max_suppression(
+ tf.gather(pre_nms_rois, ixs),
+ tf.gather(pre_nms_scores, ixs),
+ max_output_size=config.DETECTION_MAX_INSTANCES,
+ iou_threshold=config.DETECTION_NMS_THRESHOLD)
+ # Map indices
+ class_keep = tf.gather(keep, tf.gather(ixs, class_keep))
+ # Pad with -1 so returned tensors have the same shape
+ gap = config.DETECTION_MAX_INSTANCES - tf.shape(class_keep)[0]
+ class_keep = tf.pad(class_keep, [(0, gap)],
+ mode='CONSTANT', constant_values=-1)
+ # Set shape so map_fn() can infer result shape
+ class_keep.set_shape([config.DETECTION_MAX_INSTANCES])
+ return class_keep
+
+ # 2. Map over class IDs
+ nms_keep = tf.map_fn(nms_keep_map, unique_pre_nms_class_ids,
+ dtype=tf.int64)
+ # 3. Merge results into one list, and remove -1 padding
+ nms_keep = tf.reshape(nms_keep, [-1])
+ nms_keep = tf.gather(nms_keep, tf.where(nms_keep > -1)[:, 0])
+ # 4. Compute intersection between keep and nms_keep
+ keep = tf.sets.set_intersection(tf.expand_dims(keep, 0),
+ tf.expand_dims(nms_keep, 0))
+ keep = tf.sparse_tensor_to_dense(keep)[0]
+ # Keep top detections
+ roi_count = config.DETECTION_MAX_INSTANCES
+ class_scores_keep = tf.gather(class_scores, keep)
+ num_keep = tf.minimum(tf.shape(class_scores_keep)[0], roi_count)
+ top_ids = tf.nn.top_k(class_scores_keep, k=num_keep, sorted=True)[1]
+ keep = tf.gather(keep, top_ids)
+
+ # Arrange output as [N, (y1, x1, y2, x2, class_id, score)]
+ # Coordinates are normalized.
+ detections = tf.concat([
+ tf.gather(refined_rois, keep),
+ tf.to_float(tf.gather(class_ids, keep))[..., tf.newaxis],
+ tf.gather(class_scores, keep)[..., tf.newaxis]
+ ], axis=1)
+
+ # Pad with zeros if detections < DETECTION_MAX_INSTANCES
+ gap = config.DETECTION_MAX_INSTANCES - tf.shape(detections)[0]
+ detections = tf.pad(detections, [(0, gap), (0, 0)], "CONSTANT")
+ return detections
+
+
+class DetectionLayer(KE.Layer):
+ """Takes classified proposal boxes and their bounding box deltas and
+ returns the final detection boxes.
+
+ Returns:
+ [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] where
+ coordinates are normalized.
+ """
+
+ def __init__(self, config=None, **kwargs):
+ super(DetectionLayer, self).__init__(**kwargs)
+ self.config = config
+
+ def call(self, inputs):
+ rois = inputs[0]
+ mrcnn_class = inputs[1]
+ mrcnn_bbox = inputs[2]
+ image_meta = inputs[3]
+
+ # Get windows of images in normalized coordinates. Windows are the area
+ # in the image that excludes the padding.
+ # Use the shape of the first image in the batch to normalize the window
+ # because we know that all images get resized to the same size.
+ m = parse_image_meta_graph(image_meta)
+ image_shape = m['image_shape'][0]
+ window = norm_boxes_graph(m['window'], image_shape[:2])
+
+ # Run detection refinement graph on each item in the batch
+ detections_batch = utils.batch_slice(
+ [rois, mrcnn_class, mrcnn_bbox, window],
+ lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
+ self.config.IMAGES_PER_GPU)
+
+ # Reshape output
+ # [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] in
+ # normalized coordinates
+ return tf.reshape(
+ detections_batch,
+ [self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6])
+
+ def compute_output_shape(self, input_shape):
+ return (None, self.config.DETECTION_MAX_INSTANCES, 6)
+
+
+############################################################
+# Region Proposal Network (RPN)
+############################################################
+
+def rpn_graph(feature_map, anchors_per_location, anchor_stride):
+ """Builds the computation graph of Region Proposal Network.
+
+ feature_map: backbone features [batch, height, width, depth]
+ anchors_per_location: number of anchors per pixel in the feature map
+ anchor_stride: Controls the density of anchors. Typically 1 (anchors for
+ every pixel in the feature map), or 2 (every other pixel).
+
+ Returns:
+ rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax)
+ rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities.
+ rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be
+ applied to anchors.
+ """
+ # TODO: check if stride of 2 causes alignment issues if the feature map
+ # is not even.
+ # Shared convolutional base of the RPN
+ shared = KL.Conv2D(512, (3, 3), padding='same', activation='relu',
+ strides=anchor_stride,
+ name='rpn_conv_shared')(feature_map)
+
+ # Anchor Score. [batch, height, width, anchors per location * 2].
+ x = KL.Conv2D(2 * anchors_per_location, (1, 1), padding='valid',
+ activation='linear', name='rpn_class_raw')(shared)
+
+ # Reshape to [batch, anchors, 2]
+ rpn_class_logits = KL.Lambda(
+ lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 2]))(x)
+
+ # Softmax on last dimension of BG/FG.
+ rpn_probs = KL.Activation(
+ "softmax", name="rpn_class_xxx")(rpn_class_logits)
+
+ # Bounding box refinement. [batch, H, W, anchors per location * depth]
+ # where depth is [x, y, log(w), log(h)]
+ x = KL.Conv2D(anchors_per_location * 4, (1, 1), padding="valid",
+ activation='linear', name='rpn_bbox_pred')(shared)
+
+ # Reshape to [batch, anchors, 4]
+ rpn_bbox = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 4]))(x)
+
+ return [rpn_class_logits, rpn_probs, rpn_bbox]
+
+
+def build_rpn_model(anchor_stride, anchors_per_location, depth):
+ """Builds a Keras model of the Region Proposal Network.
+ It wraps the RPN graph so it can be used multiple times with shared
+ weights.
+
+ anchors_per_location: number of anchors per pixel in the feature map
+ anchor_stride: Controls the density of anchors. Typically 1 (anchors for
+ every pixel in the feature map), or 2 (every other pixel).
+ depth: Depth of the backbone feature map.
+
+ Returns a Keras Model object. The model outputs, when called, are:
+ rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax)
+ rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities.
+ rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be
+ applied to anchors.
+ """
+ input_feature_map = KL.Input(shape=[None, None, depth],
+ name="input_rpn_feature_map")
+ outputs = rpn_graph(input_feature_map, anchors_per_location, anchor_stride)
+ return KM.Model([input_feature_map], outputs, name="rpn_model")
+
+
+############################################################
+# Feature Pyramid Network Heads
+############################################################
+
+def fpn_classifier_graph(rois, feature_maps, image_meta,
+ pool_size, num_classes, train_bn=True,
+ fc_layers_size=1024):
+ """Builds the computation graph of the feature pyramid network classifier
+ and regressor heads.
+
+ rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
+ coordinates.
+ feature_maps: List of feature maps from different layers of the pyramid,
+ [P2, P3, P4, P5]. Each has a different resolution.
+ image_meta: [batch, (meta data)] Image details. See compose_image_meta()
+ pool_size: The width of the square feature map generated from ROI Pooling.
+ num_classes: number of classes, which determines the depth of the results
+ train_bn: Boolean. Train or freeze Batch Norm layers
+ fc_layers_size: Size of the 2 FC layers
+
+ Returns:
+ logits: [batch, num_rois, NUM_CLASSES] classifier logits (before softmax)
+ probs: [batch, num_rois, NUM_CLASSES] classifier probabilities
+ bbox_deltas: [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] Deltas to apply to
+ proposal boxes
+ """
+ # ROI Pooling
+ # Shape: [batch, num_rois, POOL_SIZE, POOL_SIZE, channels]
+ x = PyramidROIAlign([pool_size, pool_size],
+ name="roi_align_classifier")([rois, image_meta] + feature_maps)
+ # Two 1024 FC layers (implemented with Conv2D for consistency)
+ x = KL.TimeDistributed(KL.Conv2D(fc_layers_size, (pool_size, pool_size), padding="valid"),
+ name="mrcnn_class_conv1")(x)
+ x = KL.TimeDistributed(BatchNorm(), name='mrcnn_class_bn1')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+ x = KL.TimeDistributed(KL.Conv2D(fc_layers_size, (1, 1)),
+ name="mrcnn_class_conv2")(x)
+ x = KL.TimeDistributed(BatchNorm(), name='mrcnn_class_bn2')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ shared = KL.Lambda(lambda x: K.squeeze(K.squeeze(x, 3), 2),
+ name="pool_squeeze")(x)
+
+ # Classifier head
+ mrcnn_class_logits = KL.TimeDistributed(KL.Dense(num_classes),
+ name='mrcnn_class_logits')(shared)
+ mrcnn_probs = KL.TimeDistributed(KL.Activation("softmax"),
+ name="mrcnn_class")(mrcnn_class_logits)
+
+ # BBox head
+ # [batch, num_rois, NUM_CLASSES * (dy, dx, log(dh), log(dw))]
+ x = KL.TimeDistributed(KL.Dense(num_classes * 4, activation='linear'),
+ name='mrcnn_bbox_fc')(shared)
+ # Reshape to [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))]
+ s = K.int_shape(x)
+ mrcnn_bbox = KL.Reshape((s[1], num_classes, 4), name="mrcnn_bbox")(x)
+
+ return mrcnn_class_logits, mrcnn_probs, mrcnn_bbox
+
+
+def build_fpn_mask_graph(rois, feature_maps, image_meta,
+ pool_size, num_classes, train_bn=True):
+ """Builds the computation graph of the mask head of Feature Pyramid Network.
+
+ rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
+ coordinates.
+ feature_maps: List of feature maps from different layers of the pyramid,
+ [P2, P3, P4, P5]. Each has a different resolution.
+ image_meta: [batch, (meta data)] Image details. See compose_image_meta()
+ pool_size: The width of the square feature map generated from ROI Pooling.
+ num_classes: number of classes, which determines the depth of the results
+ train_bn: Boolean. Train or freeze Batch Norm layers
+
+ Returns: Masks [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, NUM_CLASSES]
+ """
+ # ROI Pooling
+ # Shape: [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, channels]
+ x = PyramidROIAlign([pool_size, pool_size],
+ name="roi_align_mask")([rois, image_meta] + feature_maps)
+
+ # Conv layers
+ x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
+ name="mrcnn_mask_conv1")(x)
+ x = KL.TimeDistributed(BatchNorm(),
+ name='mrcnn_mask_bn1')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
+ name="mrcnn_mask_conv2")(x)
+ x = KL.TimeDistributed(BatchNorm(),
+ name='mrcnn_mask_bn2')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
+ name="mrcnn_mask_conv3")(x)
+ x = KL.TimeDistributed(BatchNorm(),
+ name='mrcnn_mask_bn3')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
+ name="mrcnn_mask_conv4")(x)
+ x = KL.TimeDistributed(BatchNorm(),
+ name='mrcnn_mask_bn4')(x, training=train_bn)
+ x = KL.Activation('relu')(x)
+
+ x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2), strides=2, activation="relu"),
+ name="mrcnn_mask_deconv")(x)
+ x = KL.TimeDistributed(KL.Conv2D(num_classes, (1, 1), strides=1, activation="sigmoid"),
+ name="mrcnn_mask")(x)
+ return x
+
+
+############################################################
+# Loss Functions
+############################################################
+
+def smooth_l1_loss(y_true, y_pred):
+ """Implements Smooth-L1 loss.
+ y_true and y_pred are typically: [N, 4], but could be any shape.
+ """
+ diff = K.abs(y_true - y_pred)
+ less_than_one = K.cast(K.less(diff, 1.0), "float32")
+ loss = (less_than_one * 0.5 * diff**2) + (1 - less_than_one) * (diff - 0.5)
+ return loss
+
+
+def rpn_class_loss_graph(rpn_match, rpn_class_logits):
+ """RPN anchor classifier loss.
+
+ rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
+ -1=negative, 0=neutral anchor.
+ rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for BG/FG.
+ """
+ # Squeeze last dim to simplify
+ rpn_match = tf.squeeze(rpn_match, -1)
+ # Get anchor classes. Convert the -1/+1 match to 0/1 values.
+ anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
+ # Positive and Negative anchors contribute to the loss,
+ # but neutral anchors (match value = 0) don't.
+ indices = tf.where(K.not_equal(rpn_match, 0))
+ # Pick rows that contribute to the loss and filter out the rest.
+ rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
+ anchor_class = tf.gather_nd(anchor_class, indices)
+ # Cross entropy loss
+ loss = K.sparse_categorical_crossentropy(target=anchor_class,
+ output=rpn_class_logits,
+ from_logits=True)
+ loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
+ return loss
+
+
+def rpn_bbox_loss_graph(config, target_bbox, rpn_match, rpn_bbox):
+ """Return the RPN bounding box loss graph.
+
+ config: the model config object.
+ target_bbox: [batch, max positive anchors, (dy, dx, log(dh), log(dw))].
+ Uses 0 padding to fill in unsed bbox deltas.
+ rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
+ -1=negative, 0=neutral anchor.
+ rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]
+ """
+ # Positive anchors contribute to the loss, but negative and
+ # neutral anchors (match value of 0 or -1) don't.
+ rpn_match = K.squeeze(rpn_match, -1)
+ indices = tf.where(K.equal(rpn_match, 1))
+
+ # Pick bbox deltas that contribute to the loss
+ rpn_bbox = tf.gather_nd(rpn_bbox, indices)
+
+ # Trim target bounding box deltas to the same length as rpn_bbox.
+ batch_counts = K.sum(K.cast(K.equal(rpn_match, 1), tf.int32), axis=1)
+ target_bbox = batch_pack_graph(target_bbox, batch_counts,
+ config.IMAGES_PER_GPU)
+
+ loss = smooth_l1_loss(target_bbox, rpn_bbox)
+
+ loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
+ return loss
+
+
+def mrcnn_class_loss_graph(target_class_ids, pred_class_logits,
+ active_class_ids):
+ """Loss for the classifier head of Mask RCNN.
+
+ target_class_ids: [batch, num_rois]. Integer class IDs. Uses zero
+ padding to fill in the array.
+ pred_class_logits: [batch, num_rois, num_classes]
+ active_class_ids: [batch, num_classes]. Has a value of 1 for
+ classes that are in the dataset of the image, and 0
+ for classes that are not in the dataset.
+ """
+ # During model building, Keras calls this function with
+ # target_class_ids of type float32. Unclear why. Cast it
+ # to int to get around it.
+ target_class_ids = tf.cast(target_class_ids, 'int64')
+
+ # Find predictions of classes that are not in the dataset.
+ pred_class_ids = tf.argmax(pred_class_logits, axis=2)
+ # TODO: Update this line to work with batch > 1. Right now it assumes all
+ # images in a batch have the same active_class_ids
+ pred_active = tf.gather(active_class_ids[0], pred_class_ids)
+
+ # Loss
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
+ labels=target_class_ids, logits=pred_class_logits)
+
+ # Erase losses of predictions of classes that are not in the active
+ # classes of the image.
+ loss = loss * pred_active
+
+ # Computer loss mean. Use only predictions that contribute
+ # to the loss to get a correct mean.
+ loss = tf.reduce_sum(loss) / tf.reduce_sum(pred_active)
+ return loss
+
+
+def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox):
+ """Loss for Mask R-CNN bounding box refinement.
+
+ target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))]
+ target_class_ids: [batch, num_rois]. Integer class IDs.
+ pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))]
+ """
+ # Reshape to merge batch and roi dimensions for simplicity.
+ target_class_ids = K.reshape(target_class_ids, (-1,))
+ target_bbox = K.reshape(target_bbox, (-1, 4))
+ pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4))
+
+ # Only positive ROIs contribute to the loss. And only
+ # the right class_id of each ROI. Get their indices.
+ positive_roi_ix = tf.where(target_class_ids > 0)[:, 0]
+ positive_roi_class_ids = tf.cast(
+ tf.gather(target_class_ids, positive_roi_ix), tf.int64)
+ indices = tf.stack([positive_roi_ix, positive_roi_class_ids], axis=1)
+
+ # Gather the deltas (predicted and true) that contribute to loss
+ target_bbox = tf.gather(target_bbox, positive_roi_ix)
+ pred_bbox = tf.gather_nd(pred_bbox, indices)
+
+ # Smooth-L1 Loss
+ loss = K.switch(tf.size(target_bbox) > 0,
+ smooth_l1_loss(y_true=target_bbox, y_pred=pred_bbox),
+ tf.constant(0.0))
+ loss = K.mean(loss)
+ return loss
+
+
+def mrcnn_mask_loss_graph(target_masks, target_class_ids, pred_masks):
+ """Mask binary cross-entropy loss for the masks head.
+
+ target_masks: [batch, num_rois, height, width].
+ A float32 tensor of values 0 or 1. Uses zero padding to fill array.
+ target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded.
+ pred_masks: [batch, proposals, height, width, num_classes] float32 tensor
+ with values from 0 to 1.
+ """
+ # Reshape for simplicity. Merge first two dimensions into one.
+ target_class_ids = K.reshape(target_class_ids, (-1,))
+ mask_shape = tf.shape(target_masks)
+ target_masks = K.reshape(target_masks, (-1, mask_shape[2], mask_shape[3]))
+ pred_shape = tf.shape(pred_masks)
+ pred_masks = K.reshape(pred_masks,
+ (-1, pred_shape[2], pred_shape[3], pred_shape[4]))
+ # Permute predicted masks to [N, num_classes, height, width]
+ pred_masks = tf.transpose(pred_masks, [0, 3, 1, 2])
+
+ # Only positive ROIs contribute to the loss. And only
+ # the class specific mask of each ROI.
+ positive_ix = tf.where(target_class_ids > 0)[:, 0]
+ positive_class_ids = tf.cast(
+ tf.gather(target_class_ids, positive_ix), tf.int64)
+ indices = tf.stack([positive_ix, positive_class_ids], axis=1)
+
+ # Gather the masks (predicted and true) that contribute to loss
+ y_true = tf.gather(target_masks, positive_ix)
+ y_pred = tf.gather_nd(pred_masks, indices)
+
+ # Compute binary cross entropy. If no positive ROIs, then return 0.
+ # shape: [batch, roi, num_classes]
+ loss = K.switch(tf.size(y_true) > 0,
+ K.binary_crossentropy(target=y_true, output=y_pred),
+ tf.constant(0.0))
+ loss = K.mean(loss)
+ return loss
+
+
+############################################################
+# Data Generator
+############################################################
+
+def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
+ use_mini_mask=False):
+ """Load and return ground truth data for an image (image, mask, bounding boxes).
+
+ augment: (deprecated. Use augmentation instead). If true, apply random
+ image augmentation. Currently, only horizontal flipping is offered.
+ augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
+ For example, passing imgaug.augmenters.Fliplr(0.5) flips images
+ right/left 50% of the time.
+ use_mini_mask: If False, returns full-size masks that are the same height
+ and width as the original image. These can be big, for example
+ 1024x1024x100 (for 100 instances). Mini masks are smaller, typically,
+ 224x224 and are generated by extracting the bounding box of the
+ object and resizing it to MINI_MASK_SHAPE.
+
+ Returns:
+ image: [height, width, 3]
+ shape: the original shape of the image before resizing and cropping.
+ class_ids: [instance_count] Integer class IDs
+ bbox: [instance_count, (y1, x1, y2, x2)]
+ mask: [height, width, instance_count]. The height and width are those
+ of the image unless use_mini_mask is True, in which case they are
+ defined in MINI_MASK_SHAPE.
+ """
+ # Load image and mask
+ image = dataset.load_image(image_id)
+ mask, class_ids = dataset.load_mask(image_id)
+ original_shape = image.shape
+ image, window, scale, padding, crop = utils.resize_image(
+ image,
+ min_dim=config.IMAGE_MIN_DIM,
+ min_scale=config.IMAGE_MIN_SCALE,
+ max_dim=config.IMAGE_MAX_DIM,
+ mode=config.IMAGE_RESIZE_MODE)
+ mask = utils.resize_mask(mask, scale, padding, crop)
+
+ # Random horizontal flips.
+ # TODO: will be removed in a future update in favor of augmentation
+ if augment:
+ logging.warning("'augment' is deprecated. Use 'augmentation' instead.")
+ if random.randint(0, 1):
+ image = np.fliplr(image)
+ mask = np.fliplr(mask)
+
+ # Augmentation
+ # This requires the imgaug lib (https://github.com/aleju/imgaug)
+ if augmentation:
+ import imgaug
+
+ # Augmenters that are safe to apply to masks
+ # Some, such as Affine, have settings that make them unsafe, so always
+ # test your augmentation on masks
+ MASK_AUGMENTERS = ["Sequential", "SomeOf", "OneOf", "Sometimes",
+ "Fliplr", "Flipud", "CropAndPad",
+ "Affine", "PiecewiseAffine"]
+
+ def hook(images, augmenter, parents, default):
+ """Determines which augmenters to apply to masks."""
+ return augmenter.__class__.__name__ in MASK_AUGMENTERS
+
+ # Store shapes before augmentation to compare
+ image_shape = image.shape
+ mask_shape = mask.shape
+ # Make augmenters deterministic to apply similarly to images and masks
+ det = augmentation.to_deterministic()
+ image = det.augment_image(image)
+ # Change mask to np.uint8 because imgaug doesn't support np.bool
+ mask = det.augment_image(mask.astype(np.uint8),
+ hooks=imgaug.HooksImages(activator=hook))
+ # Verify that shapes didn't change
+ assert image.shape == image_shape, "Augmentation shouldn't change image size"
+ assert mask.shape == mask_shape, "Augmentation shouldn't change mask size"
+ # Change mask back to bool
+ mask = mask.astype(np.bool)
+
+ # Note that some boxes might be all zeros if the corresponding mask got cropped out.
+ # and here is to filter them out
+ _idx = np.sum(mask, axis=(0, 1)) > 0
+ mask = mask[:, :, _idx]
+ class_ids = class_ids[_idx]
+ # Bounding boxes. Note that some boxes might be all zeros
+ # if the corresponding mask got cropped out.
+ # bbox: [num_instances, (y1, x1, y2, x2)]
+ bbox = utils.extract_bboxes(mask)
+
+ # Active classes
+ # Different datasets have different classes, so track the
+ # classes supported in the dataset of this image.
+ active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32)
+ source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]["source"]]
+ active_class_ids[source_class_ids] = 1
+
+ # Resize masks to smaller size to reduce memory usage
+ if use_mini_mask:
+ mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
+
+ # Image meta data
+ image_meta = compose_image_meta(image_id, original_shape, image.shape,
+ window, scale, active_class_ids)
+
+ return image, image_meta, class_ids, bbox, mask
+
+
+def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config):
+ """Generate targets for training Stage 2 classifier and mask heads.
+ This is not used in normal training. It's useful for debugging or to train
+ the Mask RCNN heads without using the RPN head.
+
+ Inputs:
+ rpn_rois: [N, (y1, x1, y2, x2)] proposal boxes.
+ gt_class_ids: [instance count] Integer class IDs
+ gt_boxes: [instance count, (y1, x1, y2, x2)]
+ gt_masks: [height, width, instance count] Ground truth masks. Can be full
+ size or mini-masks.
+
+ Returns:
+ rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)]
+ class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs.
+ bboxes: [TRAIN_ROIS_PER_IMAGE, NUM_CLASSES, (y, x, log(h), log(w))]. Class-specific
+ bbox refinements.
+ masks: [TRAIN_ROIS_PER_IMAGE, height, width, NUM_CLASSES). Class specific masks cropped
+ to bbox boundaries and resized to neural network output size.
+ """
+ assert rpn_rois.shape[0] > 0
+ assert gt_class_ids.dtype == np.int32, "Expected int but got {}".format(
+ gt_class_ids.dtype)
+ assert gt_boxes.dtype == np.int32, "Expected int but got {}".format(
+ gt_boxes.dtype)
+ assert gt_masks.dtype == np.bool_, "Expected bool but got {}".format(
+ gt_masks.dtype)
+
+ # It's common to add GT Boxes to ROIs but we don't do that here because
+ # according to XinLei Chen's paper, it doesn't help.
+
+ # Trim empty padding in gt_boxes and gt_masks parts
+ instance_ids = np.where(gt_class_ids > 0)[0]
+ assert instance_ids.shape[0] > 0, "Image must contain instances."
+ gt_class_ids = gt_class_ids[instance_ids]
+ gt_boxes = gt_boxes[instance_ids]
+ gt_masks = gt_masks[:, :, instance_ids]
+
+ # Compute areas of ROIs and ground truth boxes.
+ rpn_roi_area = (rpn_rois[:, 2] - rpn_rois[:, 0]) * \
+ (rpn_rois[:, 3] - rpn_rois[:, 1])
+ gt_box_area = (gt_boxes[:, 2] - gt_boxes[:, 0]) * \
+ (gt_boxes[:, 3] - gt_boxes[:, 1])
+
+ # Compute overlaps [rpn_rois, gt_boxes]
+ overlaps = np.zeros((rpn_rois.shape[0], gt_boxes.shape[0]))
+ for i in range(overlaps.shape[1]):
+ gt = gt_boxes[i]
+ overlaps[:, i] = utils.compute_iou(
+ gt, rpn_rois, gt_box_area[i], rpn_roi_area)
+
+ # Assign ROIs to GT boxes
+ rpn_roi_iou_argmax = np.argmax(overlaps, axis=1)
+ rpn_roi_iou_max = overlaps[np.arange(
+ overlaps.shape[0]), rpn_roi_iou_argmax]
+ # GT box assigned to each ROI
+ rpn_roi_gt_boxes = gt_boxes[rpn_roi_iou_argmax]
+ rpn_roi_gt_class_ids = gt_class_ids[rpn_roi_iou_argmax]
+
+ # Positive ROIs are those with >= 0.5 IoU with a GT box.
+ fg_ids = np.where(rpn_roi_iou_max > 0.5)[0]
+
+ # Negative ROIs are those with max IoU 0.1-0.5 (hard example mining)
+ # TODO: To hard example mine or not to hard example mine, that's the question
+ # bg_ids = np.where((rpn_roi_iou_max >= 0.1) & (rpn_roi_iou_max < 0.5))[0]
+ bg_ids = np.where(rpn_roi_iou_max < 0.5)[0]
+
+ # Subsample ROIs. Aim for 33% foreground.
+ # FG
+ fg_roi_count = int(config.TRAIN_ROIS_PER_IMAGE * config.ROI_POSITIVE_RATIO)
+ if fg_ids.shape[0] > fg_roi_count:
+ keep_fg_ids = np.random.choice(fg_ids, fg_roi_count, replace=False)
+ else:
+ keep_fg_ids = fg_ids
+ # BG
+ remaining = config.TRAIN_ROIS_PER_IMAGE - keep_fg_ids.shape[0]
+ if bg_ids.shape[0] > remaining:
+ keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False)
+ else:
+ keep_bg_ids = bg_ids
+ # Combine indices of ROIs to keep
+ keep = np.concatenate([keep_fg_ids, keep_bg_ids])
+ # Need more?
+ remaining = config.TRAIN_ROIS_PER_IMAGE - keep.shape[0]
+ if remaining > 0:
+ # Looks like we don't have enough samples to maintain the desired
+ # balance. Reduce requirements and fill in the rest. This is
+ # likely different from the Mask RCNN paper.
+
+ # There is a small chance we have neither fg nor bg samples.
+ if keep.shape[0] == 0:
+ # Pick bg regions with easier IoU threshold
+ bg_ids = np.where(rpn_roi_iou_max < 0.5)[0]
+ assert bg_ids.shape[0] >= remaining
+ keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False)
+ assert keep_bg_ids.shape[0] == remaining
+ keep = np.concatenate([keep, keep_bg_ids])
+ else:
+ # Fill the rest with repeated bg rois.
+ keep_extra_ids = np.random.choice(
+ keep_bg_ids, remaining, replace=True)
+ keep = np.concatenate([keep, keep_extra_ids])
+ assert keep.shape[0] == config.TRAIN_ROIS_PER_IMAGE, \
+ "keep doesn't match ROI batch size {}, {}".format(
+ keep.shape[0], config.TRAIN_ROIS_PER_IMAGE)
+
+ # Reset the gt boxes assigned to BG ROIs.
+ rpn_roi_gt_boxes[keep_bg_ids, :] = 0
+ rpn_roi_gt_class_ids[keep_bg_ids] = 0
+
+ # For each kept ROI, assign a class_id, and for FG ROIs also add bbox refinement.
+ rois = rpn_rois[keep]
+ roi_gt_boxes = rpn_roi_gt_boxes[keep]
+ roi_gt_class_ids = rpn_roi_gt_class_ids[keep]
+ roi_gt_assignment = rpn_roi_iou_argmax[keep]
+
+ # Class-aware bbox deltas. [y, x, log(h), log(w)]
+ bboxes = np.zeros((config.TRAIN_ROIS_PER_IMAGE,
+ config.NUM_CLASSES, 4), dtype=np.float32)
+ pos_ids = np.where(roi_gt_class_ids > 0)[0]
+ bboxes[pos_ids, roi_gt_class_ids[pos_ids]] = utils.box_refinement(
+ rois[pos_ids], roi_gt_boxes[pos_ids, :4])
+ # Normalize bbox refinements
+ bboxes /= config.BBOX_STD_DEV
+
+ # Generate class-specific target masks
+ masks = np.zeros((config.TRAIN_ROIS_PER_IMAGE, config.MASK_SHAPE[0], config.MASK_SHAPE[1], config.NUM_CLASSES),
+ dtype=np.float32)
+ for i in pos_ids:
+ class_id = roi_gt_class_ids[i]
+ assert class_id > 0, "class id must be greater than 0"
+ gt_id = roi_gt_assignment[i]
+ class_mask = gt_masks[:, :, gt_id]
+
+ if config.USE_MINI_MASK:
+ # Create a mask placeholder, the size of the image
+ placeholder = np.zeros(config.IMAGE_SHAPE[:2], dtype=bool)
+ # GT box
+ gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[gt_id]
+ gt_w = gt_x2 - gt_x1
+ gt_h = gt_y2 - gt_y1
+ # Resize mini mask to size of GT box
+ placeholder[gt_y1:gt_y2, gt_x1:gt_x2] = \
+ np.round(utils.resize(class_mask, (gt_h, gt_w))).astype(bool)
+ # Place the mini batch in the placeholder
+ class_mask = placeholder
+
+ # Pick part of the mask and resize it
+ y1, x1, y2, x2 = rois[i].astype(np.int32)
+ m = class_mask[y1:y2, x1:x2]
+ mask = utils.resize(m, config.MASK_SHAPE)
+ masks[i, :, :, class_id] = mask
+
+ return rois, roi_gt_class_ids, bboxes, masks
+
+
+def build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config):
+ """Given the anchors and GT boxes, compute overlaps and identify positive
+ anchors and deltas to refine them to match their corresponding GT boxes.
+
+ anchors: [num_anchors, (y1, x1, y2, x2)]
+ gt_class_ids: [num_gt_boxes] Integer class IDs.
+ gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)]
+
+ Returns:
+ rpn_match: [N] (int32) matches between anchors and GT boxes.
+ 1 = positive anchor, -1 = negative anchor, 0 = neutral
+ rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
+ """
+ # RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral
+ rpn_match = np.zeros([anchors.shape[0]], dtype=np.int32)
+ # RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))]
+ rpn_bbox = np.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4))
+
+ # Handle COCO crowds
+ # A crowd box in COCO is a bounding box around several instances. Exclude
+ # them from training. A crowd box is given a negative class ID.
+ crowd_ix = np.where(gt_class_ids < 0)[0]
+ if crowd_ix.shape[0] > 0:
+ # Filter out crowds from ground truth class IDs and boxes
+ non_crowd_ix = np.where(gt_class_ids > 0)[0]
+ crowd_boxes = gt_boxes[crowd_ix]
+ gt_class_ids = gt_class_ids[non_crowd_ix]
+ gt_boxes = gt_boxes[non_crowd_ix]
+ # Compute overlaps with crowd boxes [anchors, crowds]
+ crowd_overlaps = utils.compute_overlaps(anchors, crowd_boxes)
+ crowd_iou_max = np.amax(crowd_overlaps, axis=1)
+ no_crowd_bool = (crowd_iou_max < 0.001)
+ else:
+ # All anchors don't intersect a crowd
+ no_crowd_bool = np.ones([anchors.shape[0]], dtype=bool)
+
+ # Compute overlaps [num_anchors, num_gt_boxes]
+ overlaps = utils.compute_overlaps(anchors, gt_boxes)
+
+ # Match anchors to GT Boxes
+ # If an anchor overlaps a GT box with IoU >= 0.7 then it's positive.
+ # If an anchor overlaps a GT box with IoU < 0.3 then it's negative.
+ # Neutral anchors are those that don't match the conditions above,
+ # and they don't influence the loss function.
+ # However, don't keep any GT box unmatched (rare, but happens). Instead,
+ # match it to the closest anchor (even if its max IoU is < 0.3).
+ #
+ # 1. Set negative anchors first. They get overwritten below if a GT box is
+ # matched to them. Skip boxes in crowd areas.
+ anchor_iou_argmax = np.argmax(overlaps, axis=1)
+ anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax]
+ rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1
+ # 2. Set an anchor for each GT box (regardless of IoU value).
+ # If multiple anchors have the same IoU match all of them
+ gt_iou_argmax = np.argwhere(overlaps == np.max(overlaps, axis=0))[:,0]
+ rpn_match[gt_iou_argmax] = 1
+ # 3. Set anchors with high overlap as positive.
+ rpn_match[anchor_iou_max >= 0.7] = 1
+
+ # Subsample to balance positive and negative anchors
+ # Don't let positives be more than half the anchors
+ ids = np.where(rpn_match == 1)[0]
+ extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2)
+ if extra > 0:
+ # Reset the extra ones to neutral
+ ids = np.random.choice(ids, extra, replace=False)
+ rpn_match[ids] = 0
+ # Same for negative proposals
+ ids = np.where(rpn_match == -1)[0]
+ extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE -
+ np.sum(rpn_match == 1))
+ if extra > 0:
+ # Rest the extra ones to neutral
+ ids = np.random.choice(ids, extra, replace=False)
+ rpn_match[ids] = 0
+
+ # For positive anchors, compute shift and scale needed to transform them
+ # to match the corresponding GT boxes.
+ ids = np.where(rpn_match == 1)[0]
+ ix = 0 # index into rpn_bbox
+ # TODO: use box_refinement() rather than duplicating the code here
+ for i, a in zip(ids, anchors[ids]):
+ # Closest gt box (it might have IoU < 0.7)
+ gt = gt_boxes[anchor_iou_argmax[i]]
+
+ # Convert coordinates to center plus width/height.
+ # GT Box
+ gt_h = gt[2] - gt[0]
+ gt_w = gt[3] - gt[1]
+ gt_center_y = gt[0] + 0.5 * gt_h
+ gt_center_x = gt[1] + 0.5 * gt_w
+ # Anchor
+ a_h = a[2] - a[0]
+ a_w = a[3] - a[1]
+ a_center_y = a[0] + 0.5 * a_h
+ a_center_x = a[1] + 0.5 * a_w
+
+ # Compute the bbox refinement that the RPN should predict.
+ rpn_bbox[ix] = [
+ (gt_center_y - a_center_y) / a_h,
+ (gt_center_x - a_center_x) / a_w,
+ np.log(gt_h / a_h),
+ np.log(gt_w / a_w),
+ ]
+ # Normalize
+ rpn_bbox[ix] /= config.RPN_BBOX_STD_DEV
+ ix += 1
+
+ return rpn_match, rpn_bbox
+
+
+def generate_random_rois(image_shape, count, gt_class_ids, gt_boxes):
+ """Generates ROI proposals similar to what a region proposal network
+ would generate.
+
+ image_shape: [Height, Width, Depth]
+ count: Number of ROIs to generate
+ gt_class_ids: [N] Integer ground truth class IDs
+ gt_boxes: [N, (y1, x1, y2, x2)] Ground truth boxes in pixels.
+
+ Returns: [count, (y1, x1, y2, x2)] ROI boxes in pixels.
+ """
+ # placeholder
+ rois = np.zeros((count, 4), dtype=np.int32)
+
+ # Generate random ROIs around GT boxes (90% of count)
+ rois_per_box = int(0.9 * count / gt_boxes.shape[0])
+ for i in range(gt_boxes.shape[0]):
+ gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[i]
+ h = gt_y2 - gt_y1
+ w = gt_x2 - gt_x1
+ # random boundaries
+ r_y1 = max(gt_y1 - h, 0)
+ r_y2 = min(gt_y2 + h, image_shape[0])
+ r_x1 = max(gt_x1 - w, 0)
+ r_x2 = min(gt_x2 + w, image_shape[1])
+
+ # To avoid generating boxes with zero area, we generate double what
+ # we need and filter out the extra. If we get fewer valid boxes
+ # than we need, we loop and try again.
+ while True:
+ y1y2 = np.random.randint(r_y1, r_y2, (rois_per_box * 2, 2))
+ x1x2 = np.random.randint(r_x1, r_x2, (rois_per_box * 2, 2))
+ # Filter out zero area boxes
+ threshold = 1
+ y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >=
+ threshold][:rois_per_box]
+ x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >=
+ threshold][:rois_per_box]
+ if y1y2.shape[0] == rois_per_box and x1x2.shape[0] == rois_per_box:
+ break
+
+ # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape
+ # into x1, y1, x2, y2 order
+ x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1)
+ y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1)
+ box_rois = np.hstack([y1, x1, y2, x2])
+ rois[rois_per_box * i:rois_per_box * (i + 1)] = box_rois
+
+ # Generate random ROIs anywhere in the image (10% of count)
+ remaining_count = count - (rois_per_box * gt_boxes.shape[0])
+ # To avoid generating boxes with zero area, we generate double what
+ # we need and filter out the extra. If we get fewer valid boxes
+ # than we need, we loop and try again.
+ while True:
+ y1y2 = np.random.randint(0, image_shape[0], (remaining_count * 2, 2))
+ x1x2 = np.random.randint(0, image_shape[1], (remaining_count * 2, 2))
+ # Filter out zero area boxes
+ threshold = 1
+ y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >=
+ threshold][:remaining_count]
+ x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >=
+ threshold][:remaining_count]
+ if y1y2.shape[0] == remaining_count and x1x2.shape[0] == remaining_count:
+ break
+
+ # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape
+ # into x1, y1, x2, y2 order
+ x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1)
+ y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1)
+ global_rois = np.hstack([y1, x1, y2, x2])
+ rois[-remaining_count:] = global_rois
+ return rois
+
+
+def data_generator(dataset, config, shuffle=True, augment=False, augmentation=None,
+ random_rois=0, batch_size=1, detection_targets=False,
+ no_augmentation_sources=None):
+ """A generator that returns images and corresponding target class ids,
+ bounding box deltas, and masks.
+
+ dataset: The Dataset object to pick data from
+ config: The model config object
+ shuffle: If True, shuffles the samples before every epoch
+ augment: (deprecated. Use augmentation instead). If true, apply random
+ image augmentation. Currently, only horizontal flipping is offered.
+ augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
+ For example, passing imgaug.augmenters.Fliplr(0.5) flips images
+ right/left 50% of the time.
+ random_rois: If > 0 then generate proposals to be used to train the
+ network classifier and mask heads. Useful if training
+ the Mask RCNN part without the RPN.
+ batch_size: How many images to return in each call
+ detection_targets: If True, generate detection targets (class IDs, bbox
+ deltas, and masks). Typically for debugging or visualizations because
+ in trainig detection targets are generated by DetectionTargetLayer.
+ no_augmentation_sources: Optional. List of sources to exclude for
+ augmentation. A source is string that identifies a dataset and is
+ defined in the Dataset class.
+
+ Returns a Python generator. Upon calling next() on it, the
+ generator returns two lists, inputs and outputs. The contents
+ of the lists differs depending on the received arguments:
+ inputs list:
+ - images: [batch, H, W, C]
+ - image_meta: [batch, (meta data)] Image details. See compose_image_meta()
+ - rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
+ - rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
+ - gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
+ - gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
+ - gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
+ are those of the image unless use_mini_mask is True, in which
+ case they are defined in MINI_MASK_SHAPE.
+
+ outputs list: Usually empty in regular training. But if detection_targets
+ is True then the outputs list contains target class_ids, bbox deltas,
+ and masks.
+ """
+ b = 0 # batch item index
+ image_index = -1
+ image_ids = np.copy(dataset.image_ids)
+ error_count = 0
+ no_augmentation_sources = no_augmentation_sources or []
+
+ # Anchors
+ # [anchor_count, (y1, x1, y2, x2)]
+ backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE)
+ anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
+ config.RPN_ANCHOR_RATIOS,
+ backbone_shapes,
+ config.BACKBONE_STRIDES,
+ config.RPN_ANCHOR_STRIDE)
+
+ # Keras requires a generator to run indefinitely.
+ while True:
+ try:
+ # Increment index to pick next image. Shuffle if at the start of an epoch.
+ image_index = (image_index + 1) % len(image_ids)
+ if shuffle and image_index == 0:
+ np.random.shuffle(image_ids)
+
+ # Get GT bounding boxes and masks for image.
+ image_id = image_ids[image_index]
+ # If the image source is not to be augmented pass None as augmentation
+ if dataset.image_info[image_id]['source'] in no_augmentation_sources:
+ image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
+ load_image_gt(dataset, config, image_id, augment=augment,
+ augmentation=None,
+ use_mini_mask=config.USE_MINI_MASK)
+ else:
+ image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
+ load_image_gt(dataset, config, image_id, augment=augment,
+ augmentation=augmentation,
+ use_mini_mask=config.USE_MINI_MASK)
+
+ # Skip images that have no instances. This can happen in cases
+ # where we train on a subset of classes and the image doesn't
+ # have any of the classes we care about.
+ if not np.any(gt_class_ids > 0):
+ continue
+
+ # RPN Targets
+ rpn_match, rpn_bbox = build_rpn_targets(image.shape, anchors,
+ gt_class_ids, gt_boxes, config)
+
+ # Mask R-CNN Targets
+ if random_rois:
+ rpn_rois = generate_random_rois(
+ image.shape, random_rois, gt_class_ids, gt_boxes)
+ if detection_targets:
+ rois, mrcnn_class_ids, mrcnn_bbox, mrcnn_mask =\
+ build_detection_targets(
+ rpn_rois, gt_class_ids, gt_boxes, gt_masks, config)
+
+ # Init batch arrays
+ if b == 0:
+ batch_image_meta = np.zeros(
+ (batch_size,) + image_meta.shape, dtype=image_meta.dtype)
+ batch_rpn_match = np.zeros(
+ [batch_size, anchors.shape[0], 1], dtype=rpn_match.dtype)
+ batch_rpn_bbox = np.zeros(
+ [batch_size, config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], dtype=rpn_bbox.dtype)
+ batch_images = np.zeros(
+ (batch_size,) + image.shape, dtype=np.float32)
+ batch_gt_class_ids = np.zeros(
+ (batch_size, config.MAX_GT_INSTANCES), dtype=np.int32)
+ batch_gt_boxes = np.zeros(
+ (batch_size, config.MAX_GT_INSTANCES, 4), dtype=np.int32)
+ batch_gt_masks = np.zeros(
+ (batch_size, gt_masks.shape[0], gt_masks.shape[1],
+ config.MAX_GT_INSTANCES), dtype=gt_masks.dtype)
+ if random_rois:
+ batch_rpn_rois = np.zeros(
+ (batch_size, rpn_rois.shape[0], 4), dtype=rpn_rois.dtype)
+ if detection_targets:
+ batch_rois = np.zeros(
+ (batch_size,) + rois.shape, dtype=rois.dtype)
+ batch_mrcnn_class_ids = np.zeros(
+ (batch_size,) + mrcnn_class_ids.shape, dtype=mrcnn_class_ids.dtype)
+ batch_mrcnn_bbox = np.zeros(
+ (batch_size,) + mrcnn_bbox.shape, dtype=mrcnn_bbox.dtype)
+ batch_mrcnn_mask = np.zeros(
+ (batch_size,) + mrcnn_mask.shape, dtype=mrcnn_mask.dtype)
+
+ # If more instances than fits in the array, sub-sample from them.
+ if gt_boxes.shape[0] > config.MAX_GT_INSTANCES:
+ ids = np.random.choice(
+ np.arange(gt_boxes.shape[0]), config.MAX_GT_INSTANCES, replace=False)
+ gt_class_ids = gt_class_ids[ids]
+ gt_boxes = gt_boxes[ids]
+ gt_masks = gt_masks[:, :, ids]
+
+ # Add to batch
+ batch_image_meta[b] = image_meta
+ batch_rpn_match[b] = rpn_match[:, np.newaxis]
+ batch_rpn_bbox[b] = rpn_bbox
+ batch_images[b] = mold_image(image.astype(np.float32), config)
+ batch_gt_class_ids[b, :gt_class_ids.shape[0]] = gt_class_ids
+ batch_gt_boxes[b, :gt_boxes.shape[0]] = gt_boxes
+ batch_gt_masks[b, :, :, :gt_masks.shape[-1]] = gt_masks
+ if random_rois:
+ batch_rpn_rois[b] = rpn_rois
+ if detection_targets:
+ batch_rois[b] = rois
+ batch_mrcnn_class_ids[b] = mrcnn_class_ids
+ batch_mrcnn_bbox[b] = mrcnn_bbox
+ batch_mrcnn_mask[b] = mrcnn_mask
+ b += 1
+
+ # Batch full?
+ if b >= batch_size:
+ inputs = [batch_images, batch_image_meta, batch_rpn_match, batch_rpn_bbox,
+ batch_gt_class_ids, batch_gt_boxes, batch_gt_masks]
+ outputs = []
+
+ if random_rois:
+ inputs.extend([batch_rpn_rois])
+ if detection_targets:
+ inputs.extend([batch_rois])
+ # Keras requires that output and targets have the same number of dimensions
+ batch_mrcnn_class_ids = np.expand_dims(
+ batch_mrcnn_class_ids, -1)
+ outputs.extend(
+ [batch_mrcnn_class_ids, batch_mrcnn_bbox, batch_mrcnn_mask])
+
+ yield inputs, outputs
+
+ # start a new batch
+ b = 0
+ except (GeneratorExit, KeyboardInterrupt):
+ raise
+ except:
+ # Log it and skip the image
+ logging.exception("Error processing image {}".format(
+ dataset.image_info[image_id]))
+ error_count += 1
+ if error_count > 5:
+ raise
+
+
+############################################################
+# MaskRCNN Class
+############################################################
+
+class MaskRCNN():
+ """Encapsulates the Mask RCNN model functionality.
+
+ The actual Keras model is in the keras_model property.
+ """
+
+ def __init__(self, mode, config, model_dir, k_fold_val=None):
+ """
+ mode: Either "training" or "inference"
+ config: A Sub-class of the Config class
+ model_dir: Directory to save training logs and trained weights
+ """
+ assert mode in ['training', 'inference']
+ self.mode = mode
+ self.config = config
+ self.model_dir = model_dir
+ self.k_fold_val = str(k_fold_val)
+ self.set_log_dir()
+ self.keras_model = self.build(mode=mode, config=config)
+
+
+ def build(self, mode, config):
+ """Build Mask R-CNN architecture.
+ input_shape: The shape of the input image.
+ mode: Either "training" or "inference". The inputs and
+ outputs of the model differ accordingly.
+ """
+ assert mode in ['training', 'inference']
+
+ # Image size must be dividable by 2 multiple times
+ h, w = config.IMAGE_SHAPE[:2]
+ if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):
+ raise Exception("Image size must be dividable by 2 at least 6 times "
+ "to avoid fractions when downscaling and upscaling."
+ "For example, use 256, 320, 384, 448, 512, ... etc. ")
+
+ # Inputs
+ input_image = KL.Input(
+ shape=[None, None, config.IMAGE_SHAPE[2]], name="input_image")
+ input_image_meta = KL.Input(shape=[config.IMAGE_META_SIZE],
+ name="input_image_meta")
+ if mode == "training":
+ # RPN GT
+ input_rpn_match = KL.Input(
+ shape=[None, 1], name="input_rpn_match", dtype=tf.int32)
+ input_rpn_bbox = KL.Input(
+ shape=[None, 4], name="input_rpn_bbox", dtype=tf.float32)
+
+ # Detection GT (class IDs, bounding boxes, and masks)
+ # 1. GT Class IDs (zero padded)
+ input_gt_class_ids = KL.Input(
+ shape=[None], name="input_gt_class_ids", dtype=tf.int32)
+ # 2. GT Boxes in pixels (zero padded)
+ # [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in image coordinates
+ input_gt_boxes = KL.Input(
+ shape=[None, 4], name="input_gt_boxes", dtype=tf.float32)
+ # Normalize coordinates
+ gt_boxes = KL.Lambda(lambda x: norm_boxes_graph(
+ x, K.shape(input_image)[1:3]))(input_gt_boxes)
+ # 3. GT Masks (zero padded)
+ # [batch, height, width, MAX_GT_INSTANCES]
+ if config.USE_MINI_MASK:
+ input_gt_masks = KL.Input(
+ shape=[config.MINI_MASK_SHAPE[0],
+ config.MINI_MASK_SHAPE[1], None],
+ name="input_gt_masks", dtype=bool)
+ else:
+ input_gt_masks = KL.Input(
+ shape=[config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1], None],
+ name="input_gt_masks", dtype=bool)
+ elif mode == "inference":
+ # Anchors in normalized coordinates
+ input_anchors = KL.Input(shape=[None, 4], name="input_anchors")
+
+ # Build the shared convolutional layers.
+ # Bottom-up Layers
+ # Returns a list of the last layers of each stage, 5 in total.
+ # Don't create the thead (stage 5), so we pick the 4th item in the list.
+ if callable(config.BACKBONE):
+ _, C2, C3, C4, C5 = config.BACKBONE(input_image, stage5=True,
+ train_bn=config.TRAIN_BN)
+ else:
+ _, C2, C3, C4, C5 = resnet_graph(input_image, config.BACKBONE,
+ stage5=True, train_bn=config.TRAIN_BN)
+ # Top-down Layers
+ # TODO: add assert to varify feature map sizes match what's in config
+ P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c5p5')(C5)
+ P4 = KL.Add(name="fpn_p4add")([
+ KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5),
+ KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c4p4')(C4)])
+ P3 = KL.Add(name="fpn_p3add")([
+ KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4),
+ KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c3p3')(C3)])
+ P2 = KL.Add(name="fpn_p2add")([
+ KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3),
+ KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c2p2')(C2)])
+ # Attach 3x3 conv to all P layers to get the final feature maps.
+ P2 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p2")(P2)
+ P3 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p3")(P3)
+ P4 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p4")(P4)
+ P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p5")(P5)
+ # P6 is used for the 5th anchor scale in RPN. Generated by
+ # subsampling from P5 with stride of 2.
+ P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5)
+
+ # Note that P6 is used in RPN, but not in the classifier heads.
+ rpn_feature_maps = [P2, P3, P4, P5, P6]
+ mrcnn_feature_maps = [P2, P3, P4, P5]
+
+ # Anchors
+ if mode == "training":
+ anchors = self.get_anchors(config.IMAGE_SHAPE)
+ # Duplicate across the batch dimension because Keras requires it
+ # TODO: can this be optimized to avoid duplicating the anchors?
+ anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)
+ # A hack to get around Keras's bad support for constants
+ anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
+ else:
+ anchors = input_anchors
+
+ # RPN Model
+ rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE,
+ len(config.RPN_ANCHOR_RATIOS), config.TOP_DOWN_PYRAMID_SIZE)
+ # Loop through pyramid layers
+ layer_outputs = [] # list of lists
+ for p in rpn_feature_maps:
+ layer_outputs.append(rpn([p]))
+ # Concatenate layer outputs
+ # Convert from list of lists of level outputs to list of lists
+ # of outputs across levels.
+ # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]
+ output_names = ["rpn_class_logits", "rpn_class", "rpn_bbox"]
+ outputs = list(zip(*layer_outputs))
+ outputs = [KL.Concatenate(axis=1, name=n)(list(o))
+ for o, n in zip(outputs, output_names)]
+
+ rpn_class_logits, rpn_class, rpn_bbox = outputs
+
+ # Generate proposals
+ # Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates
+ # and zero padded.
+ proposal_count = config.POST_NMS_ROIS_TRAINING if mode == "training"\
+ else config.POST_NMS_ROIS_INFERENCE
+ rpn_rois = ProposalLayer(
+ proposal_count=proposal_count,
+ nms_threshold=config.RPN_NMS_THRESHOLD,
+ name="ROI",
+ config=config)([rpn_class, rpn_bbox, anchors])
+
+ if mode == "training":
+ # Class ID mask to mark class IDs supported by the dataset the image
+ # came from.
+ active_class_ids = KL.Lambda(
+ lambda x: parse_image_meta_graph(x)["active_class_ids"]
+ )(input_image_meta)
+
+ if not config.USE_RPN_ROIS:
+ # Ignore predicted ROIs and use ROIs provided as an input.
+ input_rois = KL.Input(shape=[config.POST_NMS_ROIS_TRAINING, 4],
+ name="input_roi", dtype=np.int32)
+ # Normalize coordinates
+ target_rois = KL.Lambda(lambda x: norm_boxes_graph(
+ x, K.shape(input_image)[1:3]))(input_rois)
+ else:
+ target_rois = rpn_rois
+
+ # Generate detection targets
+ # Subsamples proposals and generates target outputs for training
+ # Note that proposal class IDs, gt_boxes, and gt_masks are zero
+ # padded. Equally, returned rois and targets are zero padded.
+ rois, target_class_ids, target_bbox, target_mask =\
+ DetectionTargetLayer(config, name="proposal_targets")([
+ target_rois, input_gt_class_ids, gt_boxes, input_gt_masks])
+
+ # Network Heads
+ # TODO: verify that this handles zero padded ROIs
+ mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\
+ fpn_classifier_graph(rois, mrcnn_feature_maps, input_image_meta,
+ config.POOL_SIZE, config.NUM_CLASSES,
+ train_bn=config.TRAIN_BN,
+ fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE)
+ # Add mask detection
+ if config.GENERATE_MASKS:
+ mrcnn_mask = build_fpn_mask_graph(rois, mrcnn_feature_maps,
+ input_image_meta,
+ config.MASK_POOL_SIZE,
+ config.NUM_CLASSES,
+ train_bn=config.TRAIN_BN)
+
+ # TODO: clean up (use tf.identify if necessary)
+ output_rois = KL.Lambda(lambda x: x * 1, name="output_rois")(rois)
+
+ # Losses
+ rpn_class_loss = KL.Lambda(lambda x: rpn_class_loss_graph(*x), name="rpn_class_loss")(
+ [input_rpn_match, rpn_class_logits])
+ rpn_bbox_loss = KL.Lambda(lambda x: rpn_bbox_loss_graph(config, *x), name="rpn_bbox_loss")(
+ [input_rpn_bbox, input_rpn_match, rpn_bbox])
+ class_loss = KL.Lambda(lambda x: mrcnn_class_loss_graph(*x), name="mrcnn_class_loss")(
+ [target_class_ids, mrcnn_class_logits, active_class_ids])
+ bbox_loss = KL.Lambda(lambda x: mrcnn_bbox_loss_graph(*x), name="mrcnn_bbox_loss")(
+ [target_bbox, target_class_ids, mrcnn_bbox])
+ # Add mask detection
+ if config.GENERATE_MASKS:
+ mask_loss = KL.Lambda(lambda x: mrcnn_mask_loss_graph(*x), name="mrcnn_mask_loss")(
+ [target_mask, target_class_ids, mrcnn_mask])
+
+ # Model
+ inputs = [input_image, input_image_meta,
+ input_rpn_match, input_rpn_bbox, input_gt_class_ids, input_gt_boxes]
+ # Add mask detection
+ #if config.GENERATE_MASKS:
+ inputs.append(input_gt_masks)
+ if not config.USE_RPN_ROIS:
+ inputs.append(input_rois)
+ # distinction between with or without mask detection
+ if config.GENERATE_MASKS:
+ outputs = [rpn_class_logits, rpn_class, rpn_bbox,
+ mrcnn_class_logits, mrcnn_class, mrcnn_bbox, mrcnn_mask,
+ rpn_rois, output_rois,
+ rpn_class_loss, rpn_bbox_loss, class_loss, bbox_loss, mask_loss]
+ else:
+ outputs = [rpn_class_logits, rpn_class, rpn_bbox,
+ mrcnn_class_logits, mrcnn_class, mrcnn_bbox,
+ rpn_rois, output_rois,
+ rpn_class_loss, rpn_bbox_loss, class_loss, bbox_loss]
+
+ model = KM.Model(inputs, outputs, name='mask_rcnn')
+ else:
+ # Network Heads
+ # Proposal classifier and BBox regressor heads
+ mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\
+ fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,
+ config.POOL_SIZE, config.NUM_CLASSES,
+ train_bn=config.TRAIN_BN,
+ fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE)
+
+ # Detections
+ # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in
+ # normalized coordinates
+ detections = DetectionLayer(config, name="mrcnn_detection")(
+ [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta])
+
+ # distinction between with or without mask detection
+ if config.GENERATE_MASKS:
+ # Create masks for detections
+ detection_boxes = KL.Lambda(lambda x: x[..., :4])(detections)
+ mrcnn_mask = build_fpn_mask_graph(detection_boxes, mrcnn_feature_maps,
+ input_image_meta,
+ config.MASK_POOL_SIZE,
+ config.NUM_CLASSES,
+ train_bn=config.TRAIN_BN)
+
+ model = KM.Model([input_image, input_image_meta, input_anchors],
+ [detections, mrcnn_class, mrcnn_bbox,
+ mrcnn_mask, rpn_rois, rpn_class, rpn_bbox],
+ name='mask_rcnn')
+ else:
+ model = KM.Model([input_image, input_image_meta, input_anchors],
+ [detections, mrcnn_class, mrcnn_bbox,
+ rpn_rois, rpn_class, rpn_bbox],
+ name='mask_rcnn')
+
+ # Add multi-GPU support.
+ if config.GPU_COUNT > 1:
+ from mrcnn.parallel_model import ParallelModel
+ model = ParallelModel(model, config.GPU_COUNT)
+
+ return model
+
+ def find_last(self):
+ """Finds the last checkpoint file of the last trained model in the
+ model directory.
+ Returns:
+ The path of the last checkpoint file
+ """
+ # Get directory names. Each directory corresponds to a model
+ dir_names = next(os.walk(self.model_dir))[1]
+ key = self.config.NAME.lower()
+ dir_names = filter(lambda f: f.startswith(key), dir_names)
+ dir_names = sorted(dir_names)
+ if not dir_names:
+ import errno
+ raise FileNotFoundError(
+ errno.ENOENT,
+ "Could not find model directory under {}".format(self.model_dir))
+ # Pick last directory
+ dir_name = os.path.join(self.model_dir, dir_names[-1])
+ # Find the last checkpoint
+ checkpoints = next(os.walk(dir_name))[2]
+ checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints)
+ checkpoints = sorted(checkpoints)
+ if not checkpoints:
+ import errno
+ raise FileNotFoundError(
+ errno.ENOENT, "Could not find weight files in {}".format(dir_name))
+ checkpoint = os.path.join(dir_name, checkpoints[-1])
+ return checkpoint
+
+ def load_weights(self, filepath, by_name=False, exclude=None):
+ """Modified version of the corresponding Keras function with
+ the addition of multi-GPU support and the ability to exclude
+ some layers from loading.
+ exclude: list of layer names to exclude
+ """
+ import h5py
+ # Conditional import to support versions of Keras before 2.2
+ # TODO: remove in about 6 months (end of 2018)
+ try:
+ from keras.engine import saving
+ except ImportError:
+ # Keras before 2.2 used the 'topology' namespace.
+ from keras.engine import topology as saving
+
+ if exclude:
+ by_name = True
+
+ if h5py is None:
+ raise ImportError('`load_weights` requires h5py.')
+ f = h5py.File(filepath, mode='r')
+ if 'layer_names' not in f.attrs and 'model_weights' in f:
+ f = f['model_weights']
+
+ # In multi-GPU training, we wrap the model. Get layers
+ # of the inner model because they have the weights.
+ keras_model = self.keras_model
+ layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\
+ else keras_model.layers
+
+ # Exclude some layers
+ if exclude:
+ layers = filter(lambda l: l.name not in exclude, layers)
+
+ if by_name:
+ saving.load_weights_from_hdf5_group_by_name(f, layers)
+ else:
+ saving.load_weights_from_hdf5_group(f, layers)
+ if hasattr(f, 'close'):
+ f.close()
+
+ # Update the log directory
+ self.set_log_dir(filepath)
+
+ def get_imagenet_weights(self):
+ """Downloads ImageNet trained weights from Keras.
+ Returns path to weights file.
+ """
+ from keras.utils.data_utils import get_file
+ TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/'\
+ 'releases/download/v0.2/'\
+ 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
+ weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
+ TF_WEIGHTS_PATH_NO_TOP,
+ cache_subdir='models',
+ md5_hash='a268eb855778b3df3c7506639542a6af')
+ return weights_path
+
+ def compile(self, learning_rate, momentum):
+ """Gets the model ready for training. Adds losses, regularization, and
+ metrics. Then calls the Keras compile() function.
+ """
+ # Optimizer object
+ optimizer = keras.optimizers.SGD(
+ lr=learning_rate, momentum=momentum,
+ clipnorm=self.config.GRADIENT_CLIP_NORM)
+ # Add Losses
+ # First, clear previously set losses to avoid duplication
+ self.keras_model._losses = []
+ self.keras_model._per_input_losses = {}
+ # distinction between with or without mask detection
+ if self.config.GENERATE_MASKS:
+ loss_names = [
+ "rpn_class_loss", "rpn_bbox_loss",
+ "mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"]
+ else:
+ loss_names = [
+ "rpn_class_loss", "rpn_bbox_loss",
+ "mrcnn_class_loss", "mrcnn_bbox_loss"]
+ for name in loss_names:
+ layer = self.keras_model.get_layer(name)
+ if layer.output in self.keras_model.losses:
+ continue
+ loss = (
+ tf.reduce_mean(layer.output, keep_dims=True)
+ * self.config.LOSS_WEIGHTS.get(name, 1.))
+ self.keras_model.add_loss(loss)
+
+ # Add L2 Regularization
+ # Skip gamma and beta weights of batch normalization layers.
+ reg_losses = [
+ keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) / tf.cast(tf.size(w), tf.float32)
+ for w in self.keras_model.trainable_weights
+ if 'gamma' not in w.name and 'beta' not in w.name]
+ self.keras_model.add_loss(tf.add_n(reg_losses))
+
+ # Compile
+ self.keras_model.compile(
+ optimizer=optimizer,
+ loss=[None] * len(self.keras_model.outputs))
+
+ # Add metrics for losses
+ for name in loss_names:
+ if name in self.keras_model.metrics_names:
+ continue
+ layer = self.keras_model.get_layer(name)
+ self.keras_model.metrics_names.append(name)
+ loss = (
+ tf.reduce_mean(layer.output, keep_dims=True)
+ * self.config.LOSS_WEIGHTS.get(name, 1.))
+ self.keras_model.metrics_tensors.append(loss)
+
+ def set_trainable(self, layer_regex, keras_model=None, indent=0, verbose=1):
+ """Sets model layers as trainable if their names match
+ the given regular expression.
+ """
+ # Print message on the first call (but not on recursive calls)
+ if verbose > 0 and keras_model is None:
+ log("Selecting layers to train")
+
+ keras_model = keras_model or self.keras_model
+
+ # In multi-GPU training, we wrap the model. Get layers
+ # of the inner model because they have the weights.
+ layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\
+ else keras_model.layers
+
+ for layer in layers:
+ # Is the layer a model?
+ if layer.__class__.__name__ == 'Model':
+ print("In model: ", layer.name)
+ self.set_trainable(
+ layer_regex, keras_model=layer, indent=indent + 4)
+ continue
+
+ if not layer.weights:
+ continue
+ # Is it trainable?
+ trainable = bool(re.fullmatch(layer_regex, layer.name))
+ # Update layer. If layer is a container, update inner layer.
+ if layer.__class__.__name__ == 'TimeDistributed':
+ layer.layer.trainable = trainable
+ else:
+ layer.trainable = trainable
+ # Print trainable layer names
+ if trainable and verbose > 0:
+ log("{}{:20} ({})".format(" " * indent, layer.name,
+ layer.__class__.__name__))
+
+ def set_log_dir(self, model_path=None):
+ """Sets the model log directory and epoch counter.
+
+ model_path: If None, or a format different from what this code uses
+ then set a new log directory and start epochs from 0. Otherwise,
+ extract the log directory and the epoch counter from the file
+ name.
+ """
+ # Set date and epoch counter as if starting a new model
+ self.epoch = 0
+ now = datetime.datetime.now()
+
+ # If we have a model path with date and epochs use them
+ if model_path:
+ # Continue from we left of. Get epoch and date from the file name
+ # A sample model path might look like:
+ # \path\to\logs\coco20171029T2315\mask_rcnn_coco_0001.h5 (Windows)
+ # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5 (Linux)
+ regex = r".*[/\\][\w-]+(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})[/\\]mask\_rcnn\_[\w-]+(\d{4})\.h5"
+ m = re.match(regex, model_path)
+ if m:
+ now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),
+ int(m.group(4)), int(m.group(5)))
+ # Epoch number in file is 1-based, and in Keras code it's 0-based.
+ # So, adjust for that then increment by one to start from the next epoch
+ self.epoch = int(m.group(6)) - 1 + 1
+ print('Re-starting from epoch %d' % self.epoch)
+
+ # Directory for training logs
+ self.log_dir = os.path.join(self.model_dir, "{}{:%Y%m%dT%H%M}".format(
+ self.config.NAME.lower(), now))
+
+ # Path to save after each epoch. Include placeholders that get filled by Keras.
+ self.checkpoint_path = os.path.join(self.log_dir, "mask_rcnn" + self.k_fold_val + "_{}_*epoch*.h5".format(
+ self.config.NAME.lower()))
+ self.checkpoint_path = self.checkpoint_path.replace(
+ "*epoch*", "{epoch:04d}")
+
+ def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
+ augmentation=None, custom_callbacks=None, no_augmentation_sources=None):
+ """Train the model.
+ train_dataset, val_dataset: Training and validation Dataset objects.
+ learning_rate: The learning rate to train with
+ epochs: Number of training epochs. Note that previous training epochs
+ are considered to be done alreay, so this actually determines
+ the epochs to train in total rather than in this particaular
+ call.
+ layers: Allows selecting wich layers to train. It can be:
+ - A regular expression to match layer names to train
+ - One of these predefined values:
+ heads: The RPN, classifier and mask heads of the network
+ all: All the layers
+ 3+: Train Resnet stage 3 and up
+ 4+: Train Resnet stage 4 and up
+ 5+: Train Resnet stage 5 and up
+ augmentation: Optional. An imgaug (https://github.com/aleju/imgaug)
+ augmentation. For example, passing imgaug.augmenters.Fliplr(0.5)
+ flips images right/left 50% of the time. You can pass complex
+ augmentations as well. This augmentation applies 50% of the
+ time, and when it does it flips images right/left half the time
+ and adds a Gaussian blur with a random sigma in range 0 to 5.
+
+ augmentation = imgaug.augmenters.Sometimes(0.5, [
+ imgaug.augmenters.Fliplr(0.5),
+ imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0))
+ ])
+ custom_callbacks: Optional. Add custom callbacks to be called
+ with the keras fit_generator method. Must be list of type keras.callbacks.
+ no_augmentation_sources: Optional. List of sources to exclude for
+ augmentation. A source is string that identifies a dataset and is
+ defined in the Dataset class.
+ """
+ assert self.mode == "training", "Create model in training mode."
+
+ # Pre-defined layer regular expressions
+ layer_regex = {
+ # all layers but the backbone
+ "heads": r"(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
+ # From a specific Resnet stage and up
+ "3+": r"(res3.*)|(bn3.*)|(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
+ "4+": r"(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
+ "5+": r"(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)",
+ # All layers
+ "all": ".*",
+ }
+ if layers in layer_regex.keys():
+ layers = layer_regex[layers]
+
+ # Data generators
+ train_generator = data_generator(train_dataset, self.config, shuffle=True,
+ augmentation=augmentation,
+ batch_size=self.config.BATCH_SIZE,
+ no_augmentation_sources=no_augmentation_sources)
+ if val_dataset:
+ val_generator = data_generator(val_dataset, self.config, shuffle=True,
+ batch_size=self.config.BATCH_SIZE)
+
+ # Create log_dir if it does not exist
+ if not os.path.exists(self.log_dir):
+ os.makedirs(self.log_dir)
+
+ # Callbacks
+ callbacks = [
+ keras.callbacks.TensorBoard(log_dir=self.log_dir,
+ histogram_freq=0, write_graph=True, write_images=False),
+ keras.callbacks.ModelCheckpoint(self.checkpoint_path,
+ verbose=0, save_weights_only=True),
+ ]
+
+ # Add custom callbacks to the list
+ if custom_callbacks:
+ callbacks += custom_callbacks
+
+ # Train
+ log("\nStarting at epoch {}. LR={}\n".format(self.epoch, learning_rate))
+ log("Checkpoint Path: {}".format(self.checkpoint_path))
+ self.set_trainable(layers)
+ self.compile(learning_rate, self.config.LEARNING_MOMENTUM)
+
+ # Work-around for Windows: Keras fails on Windows when using
+ # multiprocessing workers. See discussion here:
+ # https://github.com/matterport/Mask_RCNN/issues/13#issuecomment-353124009
+ if os.name is 'nt':
+ workers = 1
+ else:
+ workers = multiprocessing.cpu_count()
+
+ if val_dataset:
+ self.keras_model.fit_generator(
+ train_generator,
+ initial_epoch=self.epoch,
+ epochs=epochs,
+ steps_per_epoch=self.config.TRAINING_STEPS,
+ callbacks=callbacks,
+ validation_data=val_generator,
+ validation_steps=self.config.VALIDATION_STEPS,
+ max_queue_size=100,
+ workers=0,
+ use_multiprocessing=False,
+ )
+ else:
+ self.keras_model.fit_generator(
+ train_generator,
+ initial_epoch=self.epoch,
+ epochs=epochs,
+ steps_per_epoch=self.config.TRAINING_STEPS,
+ callbacks=callbacks,
+ max_queue_size=100,
+ workers=0,
+ use_multiprocessing=False,
+ )
+ self.epoch = max(self.epoch, epochs)
+
+ def mold_inputs(self, images):
+ """Takes a list of images and modifies them to the format expected
+ as an input to the neural network.
+ images: List of image matrices [height,width,depth]. Images can have
+ different sizes.
+
+ Returns 3 Numpy matrices:
+ molded_images: [N, h, w, 3]. Images resized and normalized.
+ image_metas: [N, length of meta data]. Details about each image.
+ windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
+ original image (padding excluded).
+ """
+ molded_images = []
+ image_metas = []
+ windows = []
+ for image in images:
+ # Resize image
+ # TODO: move resizing to mold_image()
+ molded_image, window, scale, padding, crop = utils.resize_image(
+ image,
+ min_dim=self.config.IMAGE_MIN_DIM,
+ min_scale=self.config.IMAGE_MIN_SCALE,
+ max_dim=self.config.IMAGE_MAX_DIM,
+ mode=self.config.IMAGE_RESIZE_MODE)
+ molded_image = mold_image(molded_image, self.config)
+ # Build image_meta
+ image_meta = compose_image_meta(
+ 0, image.shape, molded_image.shape, window, scale,
+ np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
+ # Append
+ molded_images.append(molded_image)
+ windows.append(window)
+ image_metas.append(image_meta)
+ # Pack into arrays
+ molded_images = np.stack(molded_images)
+ image_metas = np.stack(image_metas)
+ windows = np.stack(windows)
+ return molded_images, image_metas, windows
+
+ def unmold_detections(self, detections, mrcnn_mask, original_image_shape,
+ image_shape, window):
+ """Reformats the detections of one image from the format of the neural
+ network output to a format suitable for use in the rest of the
+ application.
+
+ detections: [N, (y1, x1, y2, x2, class_id, score)] in normalized coordinates
+ mrcnn_mask: [N, height, width, num_classes]
+ original_image_shape: [H, W, C] Original image shape before resizing
+ image_shape: [H, W, C] Shape of the image after resizing and padding
+ window: [y1, x1, y2, x2] Pixel coordinates of box in the image where the real
+ image is excluding the padding.
+
+ Returns:
+ boxes: [N, (y1, x1, y2, x2)] Bounding boxes in pixels
+ class_ids: [N] Integer class IDs for each bounding box
+ scores: [N] Float probability scores of the class_id
+ masks: [height, width, num_instances] Instance masks
+ """
+ # How many detections do we have?
+ # Detections array is padded with zeros. Find the first class_id == 0.
+ zero_ix = np.where(detections[:, 4] == 0)[0]
+ N = zero_ix[0] if zero_ix.shape[0] > 0 else detections.shape[0]
+
+ # Extract boxes, class_ids, scores, and class-specific masks
+ boxes = detections[:N, :4]
+ class_ids = detections[:N, 4].astype(np.int32)
+ scores = detections[:N, 5]
+ # Add mask detection
+ if self.config.GENERATE_MASKS:
+ masks = mrcnn_mask[np.arange(N), :, :, class_ids]
+
+ # Translate normalized coordinates in the resized image to pixel
+ # coordinates in the original image before resizing
+ window = utils.norm_boxes(window, image_shape[:2])
+ wy1, wx1, wy2, wx2 = window
+ shift = np.array([wy1, wx1, wy1, wx1])
+ wh = wy2 - wy1 # window height
+ ww = wx2 - wx1 # window width
+ scale = np.array([wh, ww, wh, ww])
+ # Convert boxes to normalized coordinates on the window
+ boxes = np.divide(boxes - shift, scale)
+ # Convert boxes to pixel coordinates on the original image
+ boxes = utils.denorm_boxes(boxes, original_image_shape[:2])
+
+ # Filter out detections with zero area. Happens in early training when
+ # network weights are still random
+ exclude_ix = np.where(
+ (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0]
+ if exclude_ix.shape[0] > 0:
+ boxes = np.delete(boxes, exclude_ix, axis=0)
+ class_ids = np.delete(class_ids, exclude_ix, axis=0)
+ scores = np.delete(scores, exclude_ix, axis=0)
+ # Add mask detection
+ if self.config.GENERATE_MASKS:
+ masks = np.delete(masks, exclude_ix, axis=0)
+ N = class_ids.shape[0]
+
+ # distinction between with or without mask detection
+ if self.config.GENERATE_MASKS:
+ # Resize masks to original image size and set boundary threshold.
+ full_masks = []
+ for i in range(N):
+ # Convert neural network mask to full size mask
+ full_mask = utils.unmold_mask(masks[i], boxes[i], original_image_shape)
+ full_masks.append(full_mask)
+ full_masks = np.stack(full_masks, axis=-1)\
+ if full_masks else np.empty(original_image_shape[:2] + (0,))
+
+ return boxes, class_ids, scores, full_masks
+ else:
+ return boxes, class_ids, scores
+
+ def detect(self, images, filename, verbose=0): # Dennis: filename hinzugefügt
+ """Runs the detection pipeline.
+
+ images: List of images, potentially of different sizes.
+
+ Returns a list of dicts, one dict per image. The dict contains:
+ rois: [N, (y1, x1, y2, x2)] detection bounding boxes
+ class_ids: [N] int class IDs
+ scores: [N] float probability scores for the class IDs
+ masks: [H, W, N] instance binary masks
+ """
+ assert self.mode == "inference", "Create model in inference mode."
+ print(len(images))
+ assert len(
+ images) == self.config.BATCH_SIZE, "len(images) must be equal to BATCH_SIZE" # Dennis_neu: images) == 6, images) == self.config.BATCH_SIZE
+
+ if verbose:
+ log("Processing {} images".format(len(images)))
+ for image in images:
+ ausgabe = "image_{}".format(filename) # Dennis: hinzugefügt
+ log(ausgabe, image) # Dennis: log("image", image)
+
+ # Mold inputs to format expected by the neural network
+ molded_images, image_metas, windows = self.mold_inputs(images)
+
+ # Validate image sizes
+ # All images in a batch MUST be of the same size
+ image_shape = molded_images[0].shape
+ for g in molded_images[1:]:
+ assert g.shape == image_shape,\
+ "After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes."
+
+ # Anchors
+ anchors = self.get_anchors(image_shape)
+ # Duplicate across the batch dimension because Keras requires it
+ # TODO: can this be optimized to avoid duplicating the anchors?
+ anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)
+
+ if verbose:
+ log("molded_images", molded_images)
+ log("image_metas", image_metas)
+ log("anchors", anchors)
+ # Run object detection
+ # distinction between with or without mask detection
+ if self.config.GENERATE_MASKS:
+ detections, _, _, mrcnn_mask, _, _, _ =\
+ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)
+ # Process detections
+ results = []
+ for i, image in enumerate(images):
+ final_rois, final_class_ids, final_scores, final_masks =\
+ self.unmold_detections(detections[i], mrcnn_mask[i],
+ image.shape, molded_images[i].shape,
+ windows[i])
+ results.append({
+ "rois": final_rois,
+ "class_ids": final_class_ids,
+ "scores": final_scores,
+ "masks": final_masks,
+ })
+ else:
+ detections, _, _, _, _, _ =\
+ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)
+ # Process detections
+ results = []
+ for i, image in enumerate(images):
+ final_rois, final_class_ids, final_scores =\
+ self.unmold_detections(detections[i], None,
+ image.shape, molded_images[i].shape,
+ windows[i])
+ results.append({
+ "rois": final_rois,
+ "class_ids": final_class_ids,
+ "scores": final_scores,
+ })
+ return results
+
+ def detect_molded(self, molded_images, image_metas, verbose=0):
+ """Runs the detection pipeline, but expect inputs that are
+ molded already. Used mostly for debugging and inspecting
+ the model.
+
+ molded_images: List of images loaded using load_image_gt()
+ image_metas: image meta data, also returned by load_image_gt()
+
+ Returns a list of dicts, one dict per image. The dict contains:
+ rois: [N, (y1, x1, y2, x2)] detection bounding boxes
+ class_ids: [N] int class IDs
+ scores: [N] float probability scores for the class IDs
+ masks: [H, W, N] instance binary masks
+ """
+ assert self.mode == "inference", "Create model in inference mode."
+ assert len(molded_images) == self.config.BATCH_SIZE,\
+ "Number of images must be equal to BATCH_SIZE"
+
+ if verbose:
+ log("Processing {} images".format(len(molded_images)))
+ for image in molded_images:
+ log("image", image)
+
+ # Validate image sizes
+ # All images in a batch MUST be of the same size
+ image_shape = molded_images[0].shape
+ for g in molded_images[1:]:
+ assert g.shape == image_shape, "Images must have the same size"
+
+ # Anchors
+ anchors = self.get_anchors(image_shape)
+ # Duplicate across the batch dimension because Keras requires it
+ # TODO: can this be optimized to avoid duplicating the anchors?
+ anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)
+
+ if verbose:
+ log("molded_images", molded_images)
+ log("image_metas", image_metas)
+ log("anchors", anchors)
+ # Run object detection
+ # distinction between with or without mask detection
+ if self.config.GENERATE_MASKS:
+ detections, _, _, mrcnn_mask, _, _, _ =\
+ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)
+ # Process detections
+ results = []
+ for i, image in enumerate(molded_images):
+ window = [0, 0, image.shape[0], image.shape[1]]
+ final_rois, final_class_ids, final_scores, final_masks =\
+ self.unmold_detections(detections[i], mrcnn_mask[i],
+ image.shape, molded_images[i].shape,
+ window)
+ results.append({
+ "rois": final_rois,
+ "class_ids": final_class_ids,
+ "scores": final_scores,
+ "masks": final_masks,
+ })
+ else:
+ detections, _, _, _, _, _ =\
+ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)
+ # Process detections
+ results = []
+ for i, image in enumerate(molded_images):
+ window = [0, 0, image.shape[0], image.shape[1]]
+ final_rois, final_class_ids, final_scores =\
+ self.unmold_detections(detections[i], mrcnn_mask[i],
+ image.shape, molded_images[i].shape,
+ window)
+ results.append({
+ "rois": final_rois,
+ "class_ids": final_class_ids,
+ "scores": final_scores,
+ })
+ return results
+
+ def get_anchors(self, image_shape):
+ """Returns anchor pyramid for the given image size."""
+ backbone_shapes = compute_backbone_shapes(self.config, image_shape)
+ # Cache anchors and reuse if image shape is the same
+ if not hasattr(self, "_anchor_cache"):
+ self._anchor_cache = {}
+ if not tuple(image_shape) in self._anchor_cache:
+ # Generate Anchors
+ a = utils.generate_pyramid_anchors(
+ self.config.RPN_ANCHOR_SCALES,
+ self.config.RPN_ANCHOR_RATIOS,
+ backbone_shapes,
+ self.config.BACKBONE_STRIDES,
+ self.config.RPN_ANCHOR_STRIDE)
+ # Keep a copy of the latest anchors in pixel coordinates because
+ # it's used in inspect_model notebooks.
+ # TODO: Remove this after the notebook are refactored to not use it
+ self.anchors = a
+ # Normalize coordinates
+ self._anchor_cache[tuple(image_shape)] = utils.norm_boxes(a, image_shape[:2])
+ return self._anchor_cache[tuple(image_shape)]
+
+ def ancestor(self, tensor, name, checked=None):
+ """Finds the ancestor of a TF tensor in the computation graph.
+ tensor: TensorFlow symbolic tensor.
+ name: Name of ancestor tensor to find
+ checked: For internal use. A list of tensors that were already
+ searched to avoid loops in traversing the graph.
+ """
+ checked = checked if checked is not None else []
+ # Put a limit on how deep we go to avoid very long loops
+ if len(checked) > 500:
+ return None
+ # Convert name to a regex and allow matching a number prefix
+ # because Keras adds them automatically
+ if isinstance(name, str):
+ name = re.compile(name.replace("/", r"(\_\d+)*/"))
+
+ parents = tensor.op.inputs
+ for p in parents:
+ if p in checked:
+ continue
+ if bool(re.fullmatch(name, p.name)):
+ return p
+ checked.append(p)
+ a = self.ancestor(p, name, checked)
+ if a is not None:
+ return a
+ return None
+
+ def find_trainable_layer(self, layer):
+ """If a layer is encapsulated by another layer, this function
+ digs through the encapsulation and returns the layer that holds
+ the weights.
+ """
+ if layer.__class__.__name__ == 'TimeDistributed':
+ return self.find_trainable_layer(layer.layer)
+ return layer
+
+ def get_trainable_layers(self):
+ """Returns a list of layers that have weights."""
+ layers = []
+ # Loop through all layers
+ for l in self.keras_model.layers:
+ # If layer is a wrapper, find inner trainable layer
+ l = self.find_trainable_layer(l)
+ # Include layer if it has weights
+ if l.get_weights():
+ layers.append(l)
+ return layers
+
+ def run_graph(self, images, outputs, image_metas=None):
+ """Runs a sub-set of the computation graph that computes the given
+ outputs.
+
+ image_metas: If provided, the images are assumed to be already
+ molded (i.e. resized, padded, and normalized)
+
+ outputs: List of tuples (name, tensor) to compute. The tensors are
+ symbolic TensorFlow tensors and the names are for easy tracking.
+
+ Returns an ordered dict of results. Keys are the names received in the
+ input and values are Numpy arrays.
+ """
+ model = self.keras_model
+
+ # Organize desired outputs into an ordered dict
+ outputs = OrderedDict(outputs)
+ for o in outputs.values():
+ assert o is not None
+
+ # Build a Keras function to run parts of the computation graph
+ inputs = model.inputs
+ if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
+ inputs += [K.learning_phase()]
+ kf = K.function(model.inputs, list(outputs.values()))
+
+ # Prepare inputs
+ if image_metas is None:
+ molded_images, image_metas, _ = self.mold_inputs(images)
+ else:
+ molded_images = images
+ image_shape = molded_images[0].shape
+ # Anchors
+ anchors = self.get_anchors(image_shape)
+ # Duplicate across the batch dimension because Keras requires it
+ # TODO: can this be optimized to avoid duplicating the anchors?
+ anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)
+ model_in = [molded_images, image_metas, anchors]
+
+ # Run inference
+ if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
+ model_in.append(0.)
+ outputs_np = kf(model_in)
+
+ # Pack the generated Numpy arrays into a a dict and log the results.
+ outputs_np = OrderedDict([(k, v)
+ for k, v in zip(outputs.keys(), outputs_np)])
+ for k, v in outputs_np.items():
+ log(k, v)
+ return outputs_np
+
+
+############################################################
+# Data Formatting
+############################################################
+
+def compose_image_meta(image_id, original_image_shape, image_shape,
+ window, scale, active_class_ids):
+ """Takes attributes of an image and puts them in one 1D array.
+
+ image_id: An int ID of the image. Useful for debugging.
+ original_image_shape: [H, W, C] before resizing or padding.
+ image_shape: [H, W, C] after resizing and padding
+ window: (y1, x1, y2, x2) in pixels. The area of the image where the real
+ image is (excluding the padding)
+ scale: The scaling factor applied to the original image (float32)
+ active_class_ids: List of class_ids available in the dataset from which
+ the image came. Useful if training on images from multiple datasets
+ where not all classes are present in all datasets.
+ """
+ meta = np.array(
+ [image_id] + # size=1
+ list(original_image_shape) + # size=3
+ list(image_shape) + # size=3
+ list(window) + # size=4 (y1, x1, y2, x2) in image cooredinates
+ [scale] + # size=1
+ list(active_class_ids) # size=num_classes
+ )
+ return meta
+
+
+def parse_image_meta(meta):
+ """Parses an array that contains image attributes to its components.
+ See compose_image_meta() for more details.
+
+ meta: [batch, meta length] where meta length depends on NUM_CLASSES
+
+ Returns a dict of the parsed values.
+ """
+ image_id = meta[:, 0]
+ original_image_shape = meta[:, 1:4]
+ image_shape = meta[:, 4:7]
+ window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels
+ scale = meta[:, 11]
+ active_class_ids = meta[:, 12:]
+ return {
+ "image_id": image_id.astype(np.int32),
+ "original_image_shape": original_image_shape.astype(np.int32),
+ "image_shape": image_shape.astype(np.int32),
+ "window": window.astype(np.int32),
+ "scale": scale.astype(np.float32),
+ "active_class_ids": active_class_ids.astype(np.int32),
+ }
+
+
+def parse_image_meta_graph(meta):
+ """Parses a tensor that contains image attributes to its components.
+ See compose_image_meta() for more details.
+
+ meta: [batch, meta length] where meta length depends on NUM_CLASSES
+
+ Returns a dict of the parsed tensors.
+ """
+ image_id = meta[:, 0]
+ original_image_shape = meta[:, 1:4]
+ image_shape = meta[:, 4:7]
+ window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels
+ scale = meta[:, 11]
+ active_class_ids = meta[:, 12:]
+ return {
+ "image_id": image_id,
+ "original_image_shape": original_image_shape,
+ "image_shape": image_shape,
+ "window": window,
+ "scale": scale,
+ "active_class_ids": active_class_ids,
+ }
+
+
+def mold_image(images, config):
+ """Expects an RGB image (or array of images) and subtracts
+ the mean pixel and converts it to float. Expects image
+ colors in RGB order.
+ """
+ return images.astype(np.float32) - config.MEAN_PIXEL
+
+
+def unmold_image(normalized_images, config):
+ """Takes a image normalized with mold() and returns the original."""
+ return (normalized_images + config.MEAN_PIXEL).astype(np.uint8)
+
+
+############################################################
+# Miscellenous Graph Functions
+############################################################
+
+def trim_zeros_graph(boxes, name='trim_zeros'):
+ """Often boxes are represented with matrices of shape [N, 4] and
+ are padded with zeros. This removes zero boxes.
+
+ boxes: [N, 4] matrix of boxes.
+ non_zeros: [N] a 1D boolean mask identifying the rows to keep
+ """
+ non_zeros = tf.cast(tf.reduce_sum(tf.abs(boxes), axis=1), tf.bool)
+ boxes = tf.boolean_mask(boxes, non_zeros, name=name)
+ return boxes, non_zeros
+
+
+def batch_pack_graph(x, counts, num_rows):
+ """Picks different number of values from each row
+ in x depending on the values in counts.
+ """
+ outputs = []
+ for i in range(num_rows):
+ outputs.append(x[i, :counts[i]])
+ return tf.concat(outputs, axis=0)
+
+
+def norm_boxes_graph(boxes, shape):
+ """Converts boxes from pixel coordinates to normalized coordinates.
+ boxes: [..., (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:
+ [..., (y1, x1, y2, x2)] in normalized coordinates
+ """
+ h, w = tf.split(tf.cast(shape, tf.float32), 2)
+ scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0)
+ shift = tf.constant([0., 0., 1., 1.])
+ return tf.divide(boxes - shift, scale)
+
+
+def denorm_boxes_graph(boxes, shape):
+ """Converts boxes from normalized coordinates to pixel coordinates.
+ boxes: [..., (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:
+ [..., (y1, x1, y2, x2)] in pixel coordinates
+ """
+ h, w = tf.split(tf.cast(shape, tf.float32), 2)
+ scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0)
+ shift = tf.constant([0., 0., 1., 1.])
+ return tf.cast(tf.round(tf.multiply(boxes, scale) + shift), tf.int32)