提供一种-街景图片语义分割的工作路线

1.该部分是做语义分割项目的一个工作汇报，详细记录了作者的思考过程，可作为一种街景图片语义分割的工作路线分享给大家。











2.mask-rcnn在目标识别上是一种优秀算法，它能够对街道上的物体进行精确识别。





3.附本文使用的mask-rcnn的setup.py和model.py
3.1 setup.py

"""
The build/compilations setup>> pip install -r requirements.txt
>> python setup.py install
"""
import pip
import logging
import pkg_resources
try:from setuptools import setup
except ImportError:from distutils.core import setupdef _parse_requirements(file_path):pip_ver = pkg_resources.get_distribution('pip').versionpip_version = list(map(int, pip_ver.split('.')[:2]))if pip_version >= [6, 0]:raw = pip.req.parse_requirements(file_path,session=pip.download.PipSession())else:raw = pip.req.parse_requirements(file_path)return [str(i.req) for i in raw]# parse_requirements() returns generator of pip.req.InstallRequirement objects
try:install_reqs = _parse_requirements("requirements.txt")
except Exception:logging.warning('Fail load requirements file, so using default ones.')install_reqs = []setup(name='mask-rcnn',version='2.1',url='https://github.com/matterport/Mask_RCNN',author='Matterport',author_email='waleed.abdulla@gmail.com',license='MIT',description='Mask R-CNN for object detection and instance segmentation',packages=["mrcnn"],install_requires=install_reqs,include_package_data=True,python_requires='>=3.4',long_description="""This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. 
The model generates bounding boxes and segmentation masks for each instance of an object in the image. 
It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.""",classifiers=["Development Status :: 5 - Production/Stable","Environment :: Console","Intended Audience :: Developers","Intended Audience :: Information Technology","Intended Audience :: Education","Intended Audience :: Science/Research","License :: OSI Approved :: MIT License","Natural Language :: English","Operating System :: OS Independent","Topic :: Scientific/Engineering :: Artificial Intelligence","Topic :: Scientific/Engineering :: Image Recognition","Topic :: Scientific/Engineering :: Visualization","Topic :: Scientific/Engineering :: Image Segmentation",'Programming Language :: Python :: 3.4','Programming Language :: Python :: 3.5','Programming Language :: Python :: 3.6',],keywords="image instance segmentation object detection mask rcnn r-cnn tensorflow keras",
)

3.2 model.py

"""
Mask R-CNN
The main Mask R-CNN model implementation.Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""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 KMfrom 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 itprints 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 placeto make changes if needed.Batch normalization has a negative effect on training if batches are smallso this layer is often frozen (via setting in Config class) and functionsas linear layer."""def call(self, inputs, training=None):"""Note about training values:None: Train BN layers. This is the normal modeFalse: Freeze BN layers. Good when batch size is smallTrue: (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 onlyassert 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.pydef 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# Argumentsinput_tensor: input tensorkernel_size: default 3, the kernel size of middle conv layer at main pathfilters: list of integers, the nb_filters of 3 conv layer at main pathstage: integer, current stage label, used for generating layer namesblock: 'a','b'..., current block label, used for generating layer namesuse_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 = filtersconv_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 xdef 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# Argumentsinput_tensor: input tensorkernel_size: default 3, the kernel size of middle conv layer at main pathfilters: list of integers, the nb_filters of 3 conv layer at main pathstage: integer, current stage label, used for generating layer namesblock: 'a','b'..., current block label, used for generating layer namesuse_bias: Boolean. To use or not use a bias in conv layers.train_bn: Boolean. Train or freeze Batch Norm layersNote 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 = filtersconv_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 xdef resnet_graph(input_image, architecture, stage5=False, train_bn=True):"""Build a ResNet graph.architecture: Can be resnet50 or resnet101stage5: Boolean. If False, stage5 of the network is not createdtrain_bn: Boolean. Train or freeze Batch Norm layers"""assert architecture in ["resnet50", "resnet101"]# Stage 1x = 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 2x = 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 3x = 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 4x = 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 5if 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 = Nonereturn [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 updatedeltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply"""# Convert to y, x, h, wheight = boxes[:, 2] - boxes[:, 0]width = boxes[:, 3] - boxes[:, 1]center_y = boxes[:, 0] + 0.5 * heightcenter_x = boxes[:, 1] + 0.5 * width# Apply deltascenter_y += deltas[:, 0] * heightcenter_x += deltas[:, 1] * widthheight *= tf.exp(deltas[:, 2])width *= tf.exp(deltas[:, 3])# Convert back to y1, x1, y2, x2y1 = center_y - 0.5 * heightx1 = center_x - 0.5 * widthy2 = y1 + heightx2 = x1 + widthresult = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")return resultdef clip_boxes_graph(boxes, window):"""boxes: [N, (y1, x1, y2, x2)]window: [4] in the form y1, x1, y2, x2"""# Splitwy1, wx1, wy2, wx2 = tf.split(window, 4)y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)# Clipy1 = 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 clippedclass ProposalLayer(KE.Layer):"""Receives anchor scores and selects a subset to pass as proposalsto the second stage. Filtering is done based on anchor scores andnon-max suppression to remove overlaps. It also applies boundingbox 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 coordinatesReturns: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 = configself.proposal_count = proposal_countself.nms_threshold = nms_thresholddef 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])# Anchorsanchors = 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").indicesscores = 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 suppressiondef 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 neededpadding = tf.maximum(self.proposal_count - tf.shape(proposals)[0], 0)proposals = tf.pad(proposals, [(0, padding), (0, 0)])return proposalsproposals = utils.batch_slice([boxes, scores], nms,self.config.IMAGES_PER_GPU)return proposalsdef 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 normalizedcoordinates. Possibly padded with zeros if not enoughboxes 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 layerconstructor."""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 coordsboxes = 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 - y1w = 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 P4image_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 levelbox_to_level.append(ix)# Stop gradient propogation to ROI proposalslevel_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 tensorpooled = tf.concat(pooled, axis=0)# Pack box_to_level mapping into one array and add another# column representing the order of pooled boxesbox_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 dimensionshape = tf.concat([tf.shape(boxes)[:2], tf.shape(pooled)[1:]], axis=0)pooled = tf.reshape(pooled, shape)return pooleddef 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 intersectionsb1_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 unionsb1_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 / unionoverlaps = tf.reshape(iou, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]])return overlapsdef detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config):"""Generates detection targets for one image. Subsamples proposals andgenerates target class IDs, bounding box deltas, and masks for each.Inputs:proposals: [POST_NMS_ROIS_TRAINING, (y1, x1, y2, x2)] in normalized coordinates. Mightbe zero padded if there are not enough proposals.gt_class_ids: [MAX_GT_INSTANCES] int class IDsgt_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 coordinatesclass_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 bboxboundaries and resized to neural network output size.Note: Returned arrays might be zero padded if not enough target ROIs."""# Assertionsasserts = [tf.Assert(tf.greater(tf.shape(proposals)[0], 0), [proposals],name="roi_assertion"),]with tf.control_dependencies(asserts):proposals = tf.identity(proposals)# Remove zero paddingproposals, _ = 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 ROIsroi_iou_max = tf.reduce_max(overlaps, axis=1)# 1. Positive ROIs are those with >= 0.5 IoU with a GT boxpositive_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 ROIspositive_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_RATIOnegative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_countnegative_indices = tf.random_shuffle(negative_indices)[:negative_count]# Gather selected ROIspositive_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 ROIsdeltas = 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 ROIroi_masks = tf.gather(transposed_masks, roi_gt_box_assignment)# Compute mask targetsboxes = positive_roisif 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_y1gt_w = gt_x2 - gt_x1y1 = (y1 - gt_y1) / gt_hx1 = (x1 - gt_x1) / gt_wy2 = (y2 - gt_y1) / gt_hx2 = (x2 - gt_x1) / gt_wboxes = 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, masksclass 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. Mightbe 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 normalizedcoordinates.gt_masks: [batch, height, width, MAX_GT_INSTANCES] of boolean typeReturns: Target ROIs and corresponding class IDs, bounding box shifts,and masks.rois: [batch, TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalizedcoordinatestarget_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 neuralnetwork 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 = configdef 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 claritynames = ["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 outputsdef 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 finaldetections.Inputs:rois: [N, (y1, x1, y2, x2)] in normalized coordinatesprobs: [N, num_classes]. Class probabilities.deltas: [N, num_classes, (dy, dx, log(dh), log(dw))]. Class-specificbounding box deltas.window: (y1, x1, y2, x2) in normalized coordinates. The part of the imagethat contains the image excluding the padding.Returns detections shaped: [num_detections, (y1, x1, y2, x2, class_id, score)] wherecoordinates are normalized."""# Class IDs per ROIclass_ids = tf.argmax(probs, axis=1, output_type=tf.int32)# Class probability of the top class of each ROIindices = tf.stack([tf.range(probs.shape[0]), class_ids], axis=1)class_scores = tf.gather_nd(probs, indices)# Class-specific bounding box deltasdeltas_specific = tf.gather_nd(deltas, indices)# Apply bounding box deltas# Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinatesrefined_rois = apply_box_deltas_graph(rois, deltas_specific * config.BBOX_STD_DEV)# Clip boxes to image windowrefined_rois = clip_boxes_graph(refined_rois, window)# TODO: Filter out boxes with zero area# Filter out background boxeskeep = tf.where(class_ids > 0)[:, 0]# Filter out low confidence boxesif 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 variablespre_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 classixs = tf.where(tf.equal(pre_nms_class_ids, class_id))[:, 0]# Apply NMSclass_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 indicesclass_keep = tf.gather(keep, tf.gather(ixs, class_keep))# Pad with -1 so returned tensors have the same shapegap = 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 shapeclass_keep.set_shape([config.DETECTION_MAX_INSTANCES])return class_keep# 2. Map over class IDsnms_keep = tf.map_fn(nms_keep_map, unique_pre_nms_class_ids,dtype=tf.int64)# 3. Merge results into one list, and remove -1 paddingnms_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_keepkeep = 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 detectionsroi_count = config.DETECTION_MAX_INSTANCESclass_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_INSTANCESgap = config.DETECTION_MAX_INSTANCES - tf.shape(detections)[0]detections = tf.pad(detections, [(0, gap), (0, 0)], "CONSTANT")return detectionsclass DetectionLayer(KE.Layer):"""Takes classified proposal boxes and their bounding box deltas andreturns the final detection boxes.Returns:[batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] wherecoordinates are normalized."""def __init__(self, config=None, **kwargs):super(DetectionLayer, self).__init__(**kwargs)self.config = configdef 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 batchdetections_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 coordinatesreturn 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 mapanchor_stride: Controls the density of anchors. Typically 1 (anchors forevery 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 beapplied to anchors."""# TODO: check if stride of 2 causes alignment issues if the feature map# is not even.# Shared convolutional base of the RPNshared = 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 sharedweights.anchors_per_location: number of anchors per pixel in the feature mapanchor_stride: Controls the density of anchors. Typically 1 (anchors forevery 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 beapplied 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 classifierand regressor heads.rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalizedcoordinates.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 resultstrain_bn: Boolean. Train or freeze Batch Norm layersfc_layers_size: Size of the 2 FC layersReturns:logits: [batch, num_rois, NUM_CLASSES] classifier logits (before softmax)probs: [batch, num_rois, NUM_CLASSES] classifier probabilitiesbbox_deltas: [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] Deltas to apply toproposal 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 headmrcnn_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_bboxdef 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 normalizedcoordinates.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 resultstrain_bn: Boolean. Train or freeze Batch Norm layersReturns: 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 layersx = 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 lossdef 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 simplifyrpn_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 lossloss = 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 lossdef 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 lossrpn_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 lossdef 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 zeropadding to fill in the array.pred_class_logits: [batch, num_rois, num_classes]active_class_ids: [batch, num_classes]. Has a value of 1 forclasses that are in the dataset of the image, and 0for 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_idspred_active = tf.gather(active_class_ids[0], pred_class_ids)# Lossloss = 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 lossdef 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 losstarget_bbox = tf.gather(target_bbox, positive_roi_ix)pred_bbox = tf.gather_nd(pred_bbox, indices)# Smooth-L1 Lossloss = 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 lossdef 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 tensorwith 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 lossy_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 randomimage 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 imagesright/left 50% of the time.use_mini_mask: If False, returns full-size masks that are the same heightand width as the original image. These can be big, for example1024x1024x100 (for 100 instances). Mini masks are smaller, typically,224x224 and are generated by extracting the bounding box of theobject 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 IDsbbox: [instance_count, (y1, x1, y2, x2)]mask: [height, width, instance_count]. The height and width are thoseof the image unless use_mini_mask is True, in which case they aredefined in MINI_MASK_SHAPE."""# Load image and maskimage = dataset.load_image(image_id)mask, class_ids = dataset.load_mask(image_id)original_shape = image.shapeimage, 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 augmentationif 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 masksMASK_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 compareimage_shape = image.shapemask_shape = mask.shape# Make augmenters deterministic to apply similarly to images and masksdet = augmentation.to_deterministic()image = det.augment_image(image)# Change mask to np.uint8 because imgaug doesn't support np.boolmask = det.augment_image(mask.astype(np.uint8),hooks=imgaug.HooksImages(activator=hook))# Verify that shapes didn't changeassert 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 boolmask = 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)) > 0mask = 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 usageif use_mini_mask:mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)# Image meta dataimage_meta = compose_image_meta(image_id, original_shape, image.shape,window, scale, active_class_ids)return image, image_meta, class_ids, bbox, maskdef 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 trainthe 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 IDsgt_boxes: [instance count, (y1, x1, y2, x2)]gt_masks: [height, width, instance count] Ground truth masks. Can be fullsize 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-specificbbox refinements.masks: [TRAIN_ROIS_PER_IMAGE, height, width, NUM_CLASSES). Class specific masks croppedto bbox boundaries and resized to neural network output size."""assert rpn_rois.shape[0] > 0assert 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 partsinstance_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 boxesrpn_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 ROIrpn_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.# FGfg_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# BGremaining = 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 keepkeep = 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 thresholdbg_ids = np.where(rpn_roi_iou_max < 0.5)[0]assert bg_ids.shape[0] >= remainingkeep_bg_ids = np.random.choice(bg_ids, remaining, replace=False)assert keep_bg_ids.shape[0] == remainingkeep = 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, :] = 0rpn_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 refinementsbboxes /= config.BBOX_STD_DEV# Generate class-specific target masksmasks = 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 imageplaceholder = np.zeros(config.IMAGE_SHAPE[:2], dtype=bool)# GT boxgt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[gt_id]gt_w = gt_x2 - gt_x1gt_h = gt_y2 - gt_y1# Resize mini mask to size of GT boxplaceholder[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 placeholderclass_mask = placeholder# Pick part of the mask and resize ity1, 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] = maskreturn rois, roi_gt_class_ids, bboxes, masksdef build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config):"""Given the anchors and GT boxes, compute overlaps and identify positiveanchors 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 = neutralrpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas."""# RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutralrpn_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 boxesnon_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 crowdno_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 themgt_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 anchorsids = np.where(rpn_match == 1)[0]extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2)if extra > 0:# Reset the extra ones to neutralids = np.random.choice(ids, extra, replace=False)rpn_match[ids] = 0# Same for negative proposalsids = 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 neutralids = 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 herefor 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 Boxgt_h = gt[2] - gt[0]gt_w = gt[3] - gt[1]gt_center_y = gt[0] + 0.5 * gt_hgt_center_x = gt[1] + 0.5 * gt_w# Anchora_h = a[2] - a[0]a_w = a[3] - a[1]a_center_y = a[0] + 0.5 * a_ha_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),]# Normalizerpn_bbox[ix] /= config.RPN_BBOX_STD_DEVix += 1return rpn_match, rpn_bboxdef generate_random_rois(image_shape, count, gt_class_ids, gt_boxes):"""Generates ROI proposals similar to what a region proposal networkwould generate.image_shape: [Height, Width, Depth]count: Number of ROIs to generategt_class_ids: [N] Integer ground truth class IDsgt_boxes: [N, (y1, x1, y2, x2)] Ground truth boxes in pixels.Returns: [count, (y1, x1, y2, x2)] ROI boxes in pixels."""# placeholderrois = 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_y1w = gt_x2 - gt_x1# random boundariesr_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 boxesthreshold = 1y1y2 = 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 orderx1, 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 boxesthreshold = 1y1y2 = 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 orderx1, 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_roisreturn roisdef 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 fromconfig: The model config objectshuffle: If True, shuffles the samples before every epochaugment: (deprecated. Use augmentation instead). If true, apply randomimage 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 imagesright/left 50% of the time.random_rois: If > 0 then generate proposals to be used to train thenetwork classifier and mask heads. Useful if trainingthe Mask RCNN part without the RPN.batch_size: How many images to return in each calldetection_targets: If True, generate detection targets (class IDs, bboxdeltas, and masks). Typically for debugging or visualizations becausein trainig detection targets are generated by DetectionTargetLayer.no_augmentation_sources: Optional. List of sources to exclude foraugmentation. A source is string that identifies a dataset and isdefined in the Dataset class.Returns a Python generator. Upon calling next() on it, thegenerator returns two lists, inputs and outputs. The contentsof 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 widthare those of the image unless use_mini_mask is True, in whichcase they are defined in MINI_MASK_SHAPE.outputs list: Usually empty in regular training. But if detection_targetsis True then the outputs list contains target class_ids, bbox deltas,and masks."""b = 0  # batch item indeximage_index = -1image_ids = np.copy(dataset.image_ids)error_count = 0no_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 augmentationif 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 Targetsrpn_match, rpn_bbox = build_rpn_targets(image.shape, anchors,gt_class_ids, gt_boxes, config)# Mask R-CNN Targetsif 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 arraysif 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 batchbatch_image_meta[b] = image_metabatch_rpn_match[b] = rpn_match[:, np.newaxis]batch_rpn_bbox[b] = rpn_bboxbatch_images[b] = mold_image(image.astype(np.float32), config)batch_gt_class_ids[b, :gt_class_ids.shape[0]] = gt_class_idsbatch_gt_boxes[b, :gt_boxes.shape[0]] = gt_boxesbatch_gt_masks[b, :, :, :gt_masks.shape[-1]] = gt_masksif random_rois:batch_rpn_rois[b] = rpn_roisif detection_targets:batch_rois[b] = roisbatch_mrcnn_class_ids[b] = mrcnn_class_idsbatch_mrcnn_bbox[b] = mrcnn_bboxbatch_mrcnn_mask[b] = mrcnn_maskb += 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 dimensionsbatch_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 batchb = 0except (GeneratorExit, KeyboardInterrupt):raiseexcept:# Log it and skip the imagelogging.exception("Error processing image {}".format(dataset.image_info[image_id]))error_count += 1if 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):"""mode: Either "training" or "inference"config: A Sub-class of the Config classmodel_dir: Directory to save training logs and trained weights"""assert mode in ['training', 'inference']self.mode = modeself.config = configself.model_dir = model_dirself.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 andoutputs of the model differ accordingly."""assert mode in ['training', 'inference']# Image size must be dividable by 2 multiple timesh, 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. ")# Inputsinput_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 GTinput_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 coordinatesinput_gt_boxes = KL.Input(shape=[None, 4], name="input_gt_boxes", dtype=tf.float32)# Normalize coordinatesgt_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 coordinatesinput_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 configP5 = 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]# Anchorsif 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 constantsanchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)else:anchors = input_anchors# RPN Modelrpn = build_rpn_model(config.RPN_ANCHOR_STRIDE,len(config.RPN_ANCHOR_RATIOS), config.TOP_DOWN_PYRAMID_SIZE)# Loop through pyramid layerslayer_outputs = []  # list of listsfor 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_INFERENCErpn_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 coordinatestarget_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 ROIsmrcnn_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)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)# Lossesrpn_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])mask_loss = KL.Lambda(lambda x: mrcnn_mask_loss_graph(*x), name="mrcnn_mask_loss")([target_mask, target_class_ids, mrcnn_mask])# Modelinputs = [input_image, input_image_meta,input_rpn_match, input_rpn_bbox, input_gt_class_ids, input_gt_boxes, input_gt_masks]if not config.USE_RPN_ROIS:inputs.append(input_rois)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]model = KM.Model(inputs, outputs, name='mask_rcnn')else:# Network Heads# Proposal classifier and BBox regressor headsmrcnn_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 coordinatesdetections = DetectionLayer(config, name="mrcnn_detection")([rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta])# Create masks for detectionsdetection_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')# Add multi-GPU support.if config.GPU_COUNT > 1:from mrcnn.parallel_model import ParallelModelmodel = ParallelModel(model, config.GPU_COUNT)return modeldef find_last(self):"""Finds the last checkpoint file of the last trained model in themodel directory.Returns:The path of the last checkpoint file"""# Get directory names. Each directory corresponds to a modeldir_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 errnoraise FileNotFoundError(errno.ENOENT,"Could not find model directory under {}".format(self.model_dir))# Pick last directorydir_name = os.path.join(self.model_dir, dir_names[-1])# Find the last checkpointcheckpoints = next(os.walk(dir_name))[2]checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints)checkpoints = sorted(checkpoints)if not checkpoints:import errnoraise FileNotFoundError(errno.ENOENT, "Could not find weight files in {}".format(dir_name))checkpoint = os.path.join(dir_name, checkpoints[-1])return checkpointdef load_weights(self, filepath, by_name=False, exclude=None):"""Modified version of the corresponding Keras function withthe addition of multi-GPU support and the ability to excludesome 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 savingexcept ImportError:# Keras before 2.2 used the 'topology' namespace.from keras.engine import topology as savingif exclude:by_name = Trueif 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_modellayers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\else keras_model.layers# Exclude some layersif 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 directoryself.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_fileTF_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_pathdef compile(self, learning_rate, momentum):"""Gets the model ready for training. Adds losses, regularization, andmetrics. Then calls the Keras compile() function."""# Optimizer objectoptimizer = keras.optimizers.SGD(lr=learning_rate, momentum=momentum,clipnorm=self.config.GRADIENT_CLIP_NORM)# Add Losses# First, clear previously set losses to avoid duplicationself.keras_model._losses = []self.keras_model._per_input_losses = {}loss_names = ["rpn_class_loss",  "rpn_bbox_loss","mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"]for name in loss_names:layer = self.keras_model.get_layer(name)if layer.output in self.keras_model.losses:continueloss = (tf.reduce_mean(layer.output, keepdims=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_weightsif 'gamma' not in w.name and 'beta' not in w.name]self.keras_model.add_loss(tf.add_n(reg_losses))# Compileself.keras_model.compile(optimizer=optimizer,loss=[None] * len(self.keras_model.outputs))# Add metrics for lossesfor name in loss_names:if name in self.keras_model.metrics_names:continuelayer = self.keras_model.get_layer(name)self.keras_model.metrics_names.append(name)loss = (tf.reduce_mean(layer.output, keepdims=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 matchthe 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.layersfor 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)continueif 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 = trainableelse:layer.trainable = trainable# Print trainable layer namesif 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 usesthen set a new log directory and start epochs from 0. Otherwise,extract the log directory and the epoch counter from the filename."""# Set date and epoch counter as if starting a new modelself.epoch = 0now = datetime.datetime.now()# If we have a model path with date and epochs use themif 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 epochself.epoch = int(m.group(6)) - 1 + 1print('Re-starting from epoch %d' % self.epoch)# Directory for training logsself.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_{}_*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 withepochs: Number of training epochs. Note that previous training epochsare considered to be done alreay, so this actually determinesthe epochs to train in total rather than in this particaularcall.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 networkall: All the layers3+: Train Resnet stage 3 and up4+: Train Resnet stage 4 and up5+: Train Resnet stage 5 and upaugmentation: 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 complexaugmentations as well. This augmentation applies 50% of thetime, and when it does it flips images right/left half the timeand 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 calledwith the keras fit_generator method. Must be list of type keras.callbacks.no_augmentation_sources: Optional. List of sources to exclude foraugmentation. A source is string that identifies a dataset and isdefined in the Dataset class."""assert self.mode == "training", "Create model in training mode."# Pre-defined layer regular expressionslayer_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 generatorstrain_generator = data_generator(train_dataset, self.config, shuffle=True,augmentation=augmentation,batch_size=self.config.BATCH_SIZE,no_augmentation_sources=no_augmentation_sources)val_generator = data_generator(val_dataset, self.config, shuffle=True,batch_size=self.config.BATCH_SIZE)# Create log_dir if it does not existif not os.path.exists(self.log_dir):os.makedirs(self.log_dir)# Callbackscallbacks = [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 listif custom_callbacks:callbacks += custom_callbacks# Trainlog("\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-353124009if os.name is 'nt':workers = 0else:workers = multiprocessing.cpu_count()self.keras_model.fit_generator(train_generator,initial_epoch=self.epoch,epochs=epochs,steps_per_epoch=self.config.STEPS_PER_EPOCH,callbacks=callbacks,validation_data=val_generator,validation_steps=self.config.VALIDATION_STEPS,max_queue_size=100,workers=workers,use_multiprocessing=True,)self.epoch = max(self.epoch, epochs)def mold_inputs(self, images):"""Takes a list of images and modifies them to the format expectedas an input to the neural network.images: List of image matrices [height,width,depth]. Images can havedifferent 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 theoriginal 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_metaimage_meta = compose_image_meta(0, image.shape, molded_image.shape, window, scale,np.zeros([self.config.NUM_CLASSES], dtype=np.int32))# Appendmolded_images.append(molded_image)windows.append(window)image_metas.append(image_meta)# Pack into arraysmolded_images = np.stack(molded_images)image_metas = np.stack(image_metas)windows = np.stack(windows)return molded_images, image_metas, windowsdef unmold_detections(self, detections, mrcnn_mask, original_image_shape,image_shape, window):"""Reformats the detections of one image from the format of the neuralnetwork output to a format suitable for use in the rest of theapplication.detections: [N, (y1, x1, y2, x2, class_id, score)] in normalized coordinatesmrcnn_mask: [N, height, width, num_classes]original_image_shape: [H, W, C] Original image shape before resizingimage_shape: [H, W, C] Shape of the image after resizing and paddingwindow: [y1, x1, y2, x2] Pixel coordinates of box in the image where the realimage is excluding the padding.Returns:boxes: [N, (y1, x1, y2, x2)] Bounding boxes in pixelsclass_ids: [N] Integer class IDs for each bounding boxscores: [N] Float probability scores of the class_idmasks: [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 masksboxes = detections[:N, :4]class_ids = detections[:N, 4].astype(np.int32)scores = detections[:N, 5]masks = mrcnn_mask[np.arange(N), :, :, class_ids]# Translate normalized coordinates in the resized image to pixel# coordinates in the original image before resizingwindow = utils.norm_boxes(window, image_shape[:2])wy1, wx1, wy2, wx2 = windowshift = np.array([wy1, wx1, wy1, wx1])wh = wy2 - wy1  # window heightww = wx2 - wx1  # window widthscale = np.array([wh, ww, wh, ww])# Convert boxes to normalized coordinates on the windowboxes = np.divide(boxes - shift, scale)# Convert boxes to pixel coordinates on the original imageboxes = utils.denorm_boxes(boxes, original_image_shape[:2])# Filter out detections with zero area. Happens in early training when# network weights are still randomexclude_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)masks = np.delete(masks, exclude_ix, axis=0)N = class_ids.shape[0]# Resize masks to original image size and set boundary threshold.full_masks = []for i in range(N):# Convert neural network mask to full size maskfull_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_masksdef detect(self, images, verbose=0):"""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 boxesclass_ids: [N] int class IDsscores: [N] float probability scores for the class IDsmasks: [H, W, N] instance binary masks"""assert self.mode == "inference", "Create model in inference mode."assert len(images) == self.config.BATCH_SIZE, "len(images) must be equal to BATCH_SIZE"if verbose:log("Processing {} images".format(len(images)))for image in images:log("image", image)# Mold inputs to format expected by the neural networkmolded_images, image_metas, windows = self.mold_inputs(images)# Validate image sizes# All images in a batch MUST be of the same sizeimage_shape = molded_images[0].shapefor 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."# Anchorsanchors = 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 detectiondetections, _, _, mrcnn_mask, _, _, _ =\self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)# Process detectionsresults = []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,})return resultsdef detect_molded(self, molded_images, image_metas, verbose=0):"""Runs the detection pipeline, but expect inputs that aremolded already. Used mostly for debugging and inspectingthe 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 boxesclass_ids: [N] int class IDsscores: [N] float probability scores for the class IDsmasks: [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 sizeimage_shape = molded_images[0].shapefor g in molded_images[1:]:assert g.shape == image_shape, "Images must have the same size"# Anchorsanchors = 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 detectiondetections, _, _, mrcnn_mask, _, _, _ =\self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)# Process detectionsresults = []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,})return resultsdef 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 sameif not hasattr(self, "_anchor_cache"):self._anchor_cache = {}if not tuple(image_shape) in self._anchor_cache:# Generate Anchorsa = 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 itself.anchors = a# Normalize coordinatesself._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 findchecked: For internal use. A list of tensors that were alreadysearched 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 loopsif len(checked) > 500:return None# Convert name to a regex and allow matching a number prefix# because Keras adds them automaticallyif isinstance(name, str):name = re.compile(name.replace("/", r"(\_\d+)*/"))parents = tensor.op.inputsfor p in parents:if p in checked:continueif bool(re.fullmatch(name, p.name)):return pchecked.append(p)a = self.ancestor(p, name, checked)if a is not None:return areturn Nonedef find_trainable_layer(self, layer):"""If a layer is encapsulated by another layer, this functiondigs through the encapsulation and returns the layer that holdsthe weights."""if layer.__class__.__name__ == 'TimeDistributed':return self.find_trainable_layer(layer.layer)return layerdef get_trainable_layers(self):"""Returns a list of layers that have weights."""layers = []# Loop through all layersfor l in self.keras_model.layers:# If layer is a wrapper, find inner trainable layerl = self.find_trainable_layer(l)# Include layer if it has weightsif l.get_weights():layers.append(l)return layersdef run_graph(self, images, outputs, image_metas=None):"""Runs a sub-set of the computation graph that computes the givenoutputs.image_metas: If provided, the images are assumed to be alreadymolded (i.e. resized, padded, and normalized)outputs: List of tuples (name, tensor) to compute. The tensors aresymbolic TensorFlow tensors and the names are for easy tracking.Returns an ordered dict of results. Keys are the names received in theinput and values are Numpy arrays."""model = self.keras_model# Organize desired outputs into an ordered dictoutputs = OrderedDict(outputs)for o in outputs.values():assert o is not None# Build a Keras function to run parts of the computation graphinputs = model.inputsif model.uses_learning_phase and not isinstance(K.learning_phase(), int):inputs += [K.learning_phase()]kf = K.function(model.inputs, list(outputs.values()))# Prepare inputsif image_metas is None:molded_images, image_metas, _ = self.mold_inputs(images)else:molded_images = imagesimage_shape = molded_images[0].shape# Anchorsanchors = 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 inferenceif 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 paddingwindow: (y1, x1, y2, x2) in pixels. The area of the image where the realimage 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 whichthe image came. Useful if training on images from multiple datasetswhere not all classes are present in all datasets."""meta = np.array([image_id] +                  # size=1list(original_image_shape) +  # size=3list(image_shape) +           # size=3list(window) +                # size=4 (y1, x1, y2, x2) in image cooredinates[scale] +                     # size=1list(active_class_ids)        # size=num_classes)return metadef 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_CLASSESReturns 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 pixelsscale = 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_CLASSESReturns 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 pixelsscale = 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 subtractsthe mean pixel and converts it to float. Expects imagecolors in RGB order."""return images.astype(np.float32) - config.MEAN_PIXELdef 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] andare 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_zerosdef batch_pack_graph(x, counts, num_rows):"""Picks different number of values from each rowin 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 coordinatesshape: [..., (height, width)] in pixelsNote: In pixel coordinates (y2, x2) is outside the box. But in normalizedcoordinates 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 coordinatesshape: [..., (height, width)] in pixelsNote: In pixel coordinates (y2, x2) is outside the box. But in normalizedcoordinates 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)