diff --git "a/mrcnn/model.py" "b/mrcnn/model.py" new file mode 100644--- /dev/null +++ "b/mrcnn/model.py" @@ -0,0 +1,3242 @@ +""" +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 datetime +import logging +import math +import multiprocessing +import os +import random +import re +from collections import OrderedDict + +# Requires TensorFlow 1.3+ and Keras 2.0.8+. +from distutils.version import LooseVersion + +import keras +import keras.backend as K +import keras.engine as KE +import keras.layers as KL +import keras.models as KM +import numpy as np +import tensorflow as tf + +from mrcnn import utils + +assert LooseVersion(tf.__version__) >= LooseVersion("1.3") +assert LooseVersion(keras.__version__) >= LooseVersion("2.0.8") + + +############################################################ +# Utility Functions +############################################################ + + +def log(text, array=None): + """Prints a text message. And, optionally, if a Numpy array is provided it + prints it's shape, min, and max values. + """ + if array is not None: + text = text.ljust(25) + text += "shape: {:20} ".format(str(array.shape)) + if array.size: + text += "min: {:10.5f} max: {:10.5f}".format(array.min(), array.max()) + else: + text += "min: {:10} max: {:10}".format("", "") + text += " {}".format(array.dtype) + print(text) + + +class BatchNorm(KL.BatchNormalization): + """Extends the Keras BatchNormalization class to allow a central place + to make changes if needed. + + Batch normalization has a negative effect on training if batches are small + so this layer is often frozen (via setting in Config class) and functions + as linear layer. + """ + + def call(self, inputs, training=None): + """ + Note about training values: + None: Train BN layers. This is the normal mode + False: Freeze BN layers. Good when batch size is small + True: (don't use). Set layer in training mode even when making inferences + """ + return super(self.__class__, self).call(inputs, training=training) + + +def compute_backbone_shapes(config, image_shape): + """Computes the width and height of each stage of the backbone network. + + Returns: + [N, (height, width)]. Where N is the number of stages + """ + if callable(config.BACKBONE): + return config.COMPUTE_BACKBONE_SHAPE(image_shape) + + # Currently supports ResNet only + assert config.BACKBONE in ["resnet50", "resnet101"] + return np.array( + [ + [ + int(math.ceil(image_shape[0] / stride)), + int(math.ceil(image_shape[1] / stride)), + ] + for stride in config.BACKBONE_STRIDES + ] + ) + + +############################################################ +# Resnet Graph +############################################################ + +# Code adopted from: +# https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py + + +def identity_block( + input_tensor, kernel_size, filters, stage, block, use_bias=True, train_bn=True +): + """The identity_block is the block that has no conv layer at shortcut + # Arguments + input_tensor: input tensor + kernel_size: default 3, the kernel size of middle conv layer at main path + filters: list of integers, the nb_filters of 3 conv layer at main path + stage: integer, current stage label, used for generating layer names + block: 'a','b'..., current block label, used for generating layer names + use_bias: Boolean. To use or not use a bias in conv layers. + train_bn: Boolean. Train or freeze Batch Norm layers + """ + nb_filter1, nb_filter2, nb_filter3 = filters + conv_name_base = "res" + str(stage) + block + "_branch" + bn_name_base = "bn" + str(stage) + block + "_branch" + + x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + "2a", use_bias=use_bias)( + input_tensor + ) + x = BatchNorm(name=bn_name_base + "2a")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.Conv2D( + nb_filter2, + (kernel_size, kernel_size), + padding="same", + name=conv_name_base + "2b", + use_bias=use_bias, + )(x) + x = BatchNorm(name=bn_name_base + "2b")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + "2c", use_bias=use_bias)(x) + x = BatchNorm(name=bn_name_base + "2c")(x, training=train_bn) + + x = KL.Add()([x, input_tensor]) + x = KL.Activation("relu", name="res" + str(stage) + block + "_out")(x) + return x + + +def conv_block( + input_tensor, + kernel_size, + filters, + stage, + block, + strides=(2, 2), + use_bias=True, + train_bn=True, +): + """conv_block is the block that has a conv layer at shortcut + # Arguments + input_tensor: input tensor + kernel_size: default 3, the kernel size of middle conv layer at main path + filters: list of integers, the nb_filters of 3 conv layer at main path + stage: integer, current stage label, used for generating layer names + block: 'a','b'..., current block label, used for generating layer names + use_bias: Boolean. To use or not use a bias in conv layers. + train_bn: Boolean. Train or freeze Batch Norm layers + Note that from stage 3, the first conv layer at main path is with subsample=(2,2) + And the shortcut should have subsample=(2,2) as well + """ + nb_filter1, nb_filter2, nb_filter3 = filters + conv_name_base = "res" + str(stage) + block + "_branch" + bn_name_base = "bn" + str(stage) + block + "_branch" + + x = KL.Conv2D( + nb_filter1, + (1, 1), + strides=strides, + name=conv_name_base + "2a", + use_bias=use_bias, + )(input_tensor) + x = BatchNorm(name=bn_name_base + "2a")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.Conv2D( + nb_filter2, + (kernel_size, kernel_size), + padding="same", + name=conv_name_base + "2b", + use_bias=use_bias, + )(x) + x = BatchNorm(name=bn_name_base + "2b")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + "2c", use_bias=use_bias)(x) + x = BatchNorm(name=bn_name_base + "2c")(x, training=train_bn) + + shortcut = KL.Conv2D( + nb_filter3, + (1, 1), + strides=strides, + name=conv_name_base + "1", + use_bias=use_bias, + )(input_tensor) + shortcut = BatchNorm(name=bn_name_base + "1")(shortcut, training=train_bn) + + x = KL.Add()([x, shortcut]) + x = KL.Activation("relu", name="res" + str(stage) + block + "_out")(x) + return x + + +def resnet_graph(input_image, architecture, stage5=False, train_bn=True): + """Build a ResNet graph. + architecture: Can be resnet50 or resnet101 + stage5: Boolean. If False, stage5 of the network is not created + train_bn: Boolean. Train or freeze Batch Norm layers + """ + assert architecture in ["resnet50", "resnet101"] + # Stage 1 + x = KL.ZeroPadding2D((3, 3))(input_image) + x = KL.Conv2D(64, (7, 7), strides=(2, 2), name="conv1", use_bias=True)(x) + x = BatchNorm(name="bn_conv1")(x, training=train_bn) + x = KL.Activation("relu")(x) + C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x) + # Stage 2 + x = conv_block( + x, 3, [64, 64, 256], stage=2, block="a", strides=(1, 1), train_bn=train_bn + ) + x = identity_block(x, 3, [64, 64, 256], stage=2, block="b", train_bn=train_bn) + C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block="c", train_bn=train_bn) + # Stage 3 + x = conv_block(x, 3, [128, 128, 512], stage=3, block="a", train_bn=train_bn) + x = identity_block(x, 3, [128, 128, 512], stage=3, block="b", train_bn=train_bn) + x = identity_block(x, 3, [128, 128, 512], stage=3, block="c", train_bn=train_bn) + C3 = x = identity_block( + x, 3, [128, 128, 512], stage=3, block="d", train_bn=train_bn + ) + # Stage 4 + x = conv_block(x, 3, [256, 256, 1024], stage=4, block="a", train_bn=train_bn) + block_count = {"resnet50": 5, "resnet101": 22}[architecture] + for i in range(block_count): + x = identity_block( + x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn + ) + C4 = x + # Stage 5 + if stage5: + x = conv_block(x, 3, [512, 512, 2048], stage=5, block="a", train_bn=train_bn) + x = identity_block( + x, 3, [512, 512, 2048], stage=5, block="b", train_bn=train_bn + ) + C5 = x = identity_block( + x, 3, [512, 512, 2048], stage=5, block="c", train_bn=train_bn + ) + else: + C5 = None + return [C1, C2, C3, C4, C5] + + +############################################################ +# Proposal Layer +############################################################ + + +def apply_box_deltas_graph(boxes, deltas): + """Applies the given deltas to the given boxes. + boxes: [N, (y1, x1, y2, x2)] boxes to update + deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply + """ + # Convert to y, x, h, w + height = boxes[:, 2] - boxes[:, 0] + width = boxes[:, 3] - boxes[:, 1] + center_y = boxes[:, 0] + 0.5 * height + center_x = boxes[:, 1] + 0.5 * width + # Apply deltas + center_y += deltas[:, 0] * height + center_x += deltas[:, 1] * width + height *= tf.exp(deltas[:, 2]) + width *= tf.exp(deltas[:, 3]) + # Convert back to y1, x1, y2, x2 + y1 = center_y - 0.5 * height + x1 = center_x - 0.5 * width + y2 = y1 + height + x2 = x1 + width + result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out") + return result + + +def clip_boxes_graph(boxes, window): + """ + boxes: [N, (y1, x1, y2, x2)] + window: [4] in the form y1, x1, y2, x2 + """ + # Split + wy1, wx1, wy2, wx2 = tf.split(window, 4) + y1, x1, y2, x2 = tf.split(boxes, 4, axis=1) + # Clip + y1 = tf.maximum(tf.minimum(y1, wy2), wy1) + x1 = tf.maximum(tf.minimum(x1, wx2), wx1) + y2 = tf.maximum(tf.minimum(y2, wy2), wy1) + x2 = tf.maximum(tf.minimum(x2, wx2), wx1) + clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes") + clipped.set_shape((clipped.shape[0], 4)) + return clipped + + +class ProposalLayer(KE.Layer): + """Receives anchor scores and selects a subset to pass as proposals + to the second stage. Filtering is done based on anchor scores and + non-max suppression to remove overlaps. It also applies bounding + box refinement deltas to anchors. + + Inputs: + rpn_probs: [batch, num_anchors, (bg prob, fg prob)] + rpn_bbox: [batch, num_anchors, (dy, dx, log(dh), log(dw))] + anchors: [batch, num_anchors, (y1, x1, y2, x2)] anchors in normalized coordinates + + Returns: + Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)] + """ + + def __init__(self, proposal_count, nms_threshold, config=None, **kwargs): + super(ProposalLayer, self).__init__(**kwargs) + self.config = config + self.proposal_count = proposal_count + self.nms_threshold = nms_threshold + + def call(self, inputs): + # Box Scores. Use the foreground class confidence. [Batch, num_rois, 1] + scores = inputs[0][:, :, 1] + # Box deltas [batch, num_rois, 4] + deltas = inputs[1] + deltas = deltas * np.reshape(self.config.RPN_BBOX_STD_DEV, [1, 1, 4]) + # Anchors + anchors = inputs[2] + + # Improve performance by trimming to top anchors by score + # and doing the rest on the smaller subset. + pre_nms_limit = tf.minimum(self.config.PRE_NMS_LIMIT, tf.shape(anchors)[1]) + ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True, name="top_anchors").indices + scores = utils.batch_slice( + [scores, ix], lambda x, y: tf.gather(x, y), self.config.IMAGES_PER_GPU + ) + deltas = utils.batch_slice( + [deltas, ix], lambda x, y: tf.gather(x, y), self.config.IMAGES_PER_GPU + ) + pre_nms_anchors = utils.batch_slice( + [anchors, ix], + lambda a, x: tf.gather(a, x), + self.config.IMAGES_PER_GPU, + names=["pre_nms_anchors"], + ) + + # Apply deltas to anchors to get refined anchors. + # [batch, N, (y1, x1, y2, x2)] + boxes = utils.batch_slice( + [pre_nms_anchors, deltas], + lambda x, y: apply_box_deltas_graph(x, y), + self.config.IMAGES_PER_GPU, + names=["refined_anchors"], + ) + + # Clip to image boundaries. Since we're in normalized coordinates, + # clip to 0..1 range. [batch, N, (y1, x1, y2, x2)] + window = np.array([0, 0, 1, 1], dtype=np.float32) + boxes = utils.batch_slice( + boxes, + lambda x: clip_boxes_graph(x, window), + self.config.IMAGES_PER_GPU, + names=["refined_anchors_clipped"], + ) + + # Filter out small boxes + # According to Xinlei Chen's paper, this reduces detection accuracy + # for small objects, so we're skipping it. + + # Non-max suppression + def nms(boxes, scores): + indices = tf.image.non_max_suppression( + boxes, + scores, + self.proposal_count, + self.nms_threshold, + name="rpn_non_max_suppression", + ) + proposals = tf.gather(boxes, indices) + # Pad if needed + padding = tf.maximum(self.proposal_count - tf.shape(proposals)[0], 0) + proposals = tf.pad(proposals, [(0, padding), (0, 0)]) + return proposals + + proposals = utils.batch_slice([boxes, scores], nms, self.config.IMAGES_PER_GPU) + return proposals + + def compute_output_shape(self, input_shape): + return (None, self.proposal_count, 4) + + +############################################################ +# ROIAlign Layer +############################################################ + + +def log2_graph(x): + """Implementation of Log2. TF doesn't have a native implementation.""" + return tf.log(x) / tf.log(2.0) + + +class PyramidROIAlign(KE.Layer): + """Implements ROI Pooling on multiple levels of the feature pyramid. + + Params: + - pool_shape: [pool_height, pool_width] of the output pooled regions. Usually [7, 7] + + Inputs: + - boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized + coordinates. Possibly padded with zeros if not enough + boxes to fill the array. + - image_meta: [batch, (meta data)] Image details. See compose_image_meta() + - feature_maps: List of feature maps from different levels of the pyramid. + Each is [batch, height, width, channels] + + Output: + Pooled regions in the shape: [batch, num_boxes, pool_height, pool_width, channels]. + The width and height are those specific in the pool_shape in the layer + constructor. + """ + + def __init__(self, pool_shape, **kwargs): + super(PyramidROIAlign, self).__init__(**kwargs) + self.pool_shape = tuple(pool_shape) + + def call(self, inputs): + # Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords + boxes = inputs[0] + + # Image meta + # Holds details about the image. See compose_image_meta() + image_meta = inputs[1] + + # Feature Maps. List of feature maps from different level of the + # feature pyramid. Each is [batch, height, width, channels] + feature_maps = inputs[2:] + + # Assign each ROI to a level in the pyramid based on the ROI area. + y1, x1, y2, x2 = tf.split(boxes, 4, axis=2) + h = y2 - y1 + w = x2 - x1 + # Use shape of first image. Images in a batch must have the same size. + image_shape = parse_image_meta_graph(image_meta)["image_shape"][0] + # Equation 1 in the Feature Pyramid Networks paper. Account for + # the fact that our coordinates are normalized here. + # e.g. a 224x224 ROI (in pixels) maps to P4 + image_area = tf.cast(image_shape[0] * image_shape[1], tf.float32) + roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area))) + roi_level = tf.minimum( + 5, tf.maximum(2, 4 + tf.cast(tf.round(roi_level), tf.int32)) + ) + roi_level = tf.squeeze(roi_level, 2) + + # Loop through levels and apply ROI pooling to each. P2 to P5. + pooled = [] + box_to_level = [] + for i, level in enumerate(range(2, 6)): + ix = tf.where(tf.equal(roi_level, level)) + level_boxes = tf.gather_nd(boxes, ix) + + # Box indices for crop_and_resize. + box_indices = tf.cast(ix[:, 0], tf.int32) + + # Keep track of which box is mapped to which level + box_to_level.append(ix) + + # Stop gradient propogation to ROI proposals + level_boxes = tf.stop_gradient(level_boxes) + box_indices = tf.stop_gradient(box_indices) + + # Crop and Resize + # From Mask R-CNN paper: "We sample four regular locations, so + # that we can evaluate either max or average pooling. In fact, + # interpolating only a single value at each bin center (without + # pooling) is nearly as effective." + # + # Here we use the simplified approach of a single value per bin, + # which is how it's done in tf.crop_and_resize() + # Result: [batch * num_boxes, pool_height, pool_width, channels] + pooled.append( + tf.image.crop_and_resize( + feature_maps[i], + level_boxes, + box_indices, + self.pool_shape, + method="bilinear", + ) + ) + + # Pack pooled features into one tensor + pooled = tf.concat(pooled, axis=0) + + # Pack box_to_level mapping into one array and add another + # column representing the order of pooled boxes + box_to_level = tf.concat(box_to_level, axis=0) + box_range = tf.expand_dims(tf.range(tf.shape(box_to_level)[0]), 1) + box_to_level = tf.concat([tf.cast(box_to_level, tf.int32), box_range], axis=1) + + # Rearrange pooled features to match the order of the original boxes + # Sort box_to_level by batch then box index + # TF doesn't have a way to sort by two columns, so merge them and sort. + sorting_tensor = box_to_level[:, 0] * 100000 + box_to_level[:, 1] + ix = tf.nn.top_k(sorting_tensor, k=tf.shape(box_to_level)[0]).indices[::-1] + ix = tf.gather(box_to_level[:, 2], ix) + pooled = tf.gather(pooled, ix) + + # Re-add the batch dimension + shape = tf.concat([tf.shape(boxes)[:2], tf.shape(pooled)[1:]], axis=0) + pooled = tf.reshape(pooled, shape) + return pooled + + def compute_output_shape(self, input_shape): + return input_shape[0][:2] + self.pool_shape + (input_shape[2][-1],) + + +############################################################ +# Detection Target Layer +############################################################ + + +def overlaps_graph(boxes1, boxes2): + """Computes IoU overlaps between two sets of boxes. + boxes1, boxes2: [N, (y1, x1, y2, x2)]. + """ + # 1. Tile boxes2 and repeat boxes1. This allows us to compare + # every boxes1 against every boxes2 without loops. + # TF doesn't have an equivalent to np.repeat() so simulate it + # using tf.tile() and tf.reshape. + b1 = tf.reshape( + tf.tile(tf.expand_dims(boxes1, 1), [1, 1, tf.shape(boxes2)[0]]), [-1, 4] + ) + b2 = tf.tile(boxes2, [tf.shape(boxes1)[0], 1]) + # 2. Compute intersections + b1_y1, b1_x1, b1_y2, b1_x2 = tf.split(b1, 4, axis=1) + b2_y1, b2_x1, b2_y2, b2_x2 = tf.split(b2, 4, axis=1) + y1 = tf.maximum(b1_y1, b2_y1) + x1 = tf.maximum(b1_x1, b2_x1) + y2 = tf.minimum(b1_y2, b2_y2) + x2 = tf.minimum(b1_x2, b2_x2) + intersection = tf.maximum(x2 - x1, 0) * tf.maximum(y2 - y1, 0) + # 3. Compute unions + b1_area = (b1_y2 - b1_y1) * (b1_x2 - b1_x1) + b2_area = (b2_y2 - b2_y1) * (b2_x2 - b2_x1) + union = b1_area + b2_area - intersection + # 4. Compute IoU and reshape to [boxes1, boxes2] + iou = intersection / union + overlaps = tf.reshape(iou, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]]) + return overlaps + + +def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config): + """Generates detection targets for one image. Subsamples proposals and + generates target class IDs, bounding box deltas, and masks for each. + + Inputs: + proposals: [POST_NMS_ROIS_TRAINING, (y1, x1, y2, x2)] in normalized coordinates. Might + be zero padded if there are not enough proposals. + gt_class_ids: [MAX_GT_INSTANCES] int class IDs + gt_boxes: [MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized coordinates. + gt_masks: [height, width, MAX_GT_INSTANCES] of boolean type. + + Returns: Target ROIs and corresponding class IDs, bounding box shifts, + and masks. + rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized coordinates + class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. Zero padded. + deltas: [TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw))] + masks: [TRAIN_ROIS_PER_IMAGE, height, width]. Masks cropped to bbox + boundaries and resized to neural network output size. + + Note: Returned arrays might be zero padded if not enough target ROIs. + """ + # Assertions + asserts = [ + tf.Assert( + tf.greater(tf.shape(proposals)[0], 0), [proposals], name="roi_assertion" + ), + ] + with tf.control_dependencies(asserts): + proposals = tf.identity(proposals) + + # Remove zero padding + proposals, _ = trim_zeros_graph(proposals, name="trim_proposals") + gt_boxes, non_zeros = trim_zeros_graph(gt_boxes, name="trim_gt_boxes") + gt_class_ids = tf.boolean_mask(gt_class_ids, non_zeros, name="trim_gt_class_ids") + gt_masks = tf.gather( + gt_masks, tf.where(non_zeros)[:, 0], axis=2, name="trim_gt_masks" + ) + + # Handle COCO crowds + # A crowd box in COCO is a bounding box around several instances. Exclude + # them from training. A crowd box is given a negative class ID. + crowd_ix = tf.where(gt_class_ids < 0)[:, 0] + non_crowd_ix = tf.where(gt_class_ids > 0)[:, 0] + crowd_boxes = tf.gather(gt_boxes, crowd_ix) + gt_class_ids = tf.gather(gt_class_ids, non_crowd_ix) + gt_boxes = tf.gather(gt_boxes, non_crowd_ix) + gt_masks = tf.gather(gt_masks, non_crowd_ix, axis=2) + + # Compute overlaps matrix [proposals, gt_boxes] + overlaps = overlaps_graph(proposals, gt_boxes) + + # Compute overlaps with crowd boxes [proposals, crowd_boxes] + crowd_overlaps = overlaps_graph(proposals, crowd_boxes) + crowd_iou_max = tf.reduce_max(crowd_overlaps, axis=1) + no_crowd_bool = crowd_iou_max < 0.001 + + # Determine positive and negative ROIs + roi_iou_max = tf.reduce_max(overlaps, axis=1) + # 1. Positive ROIs are those with >= 0.5 IoU with a GT box + positive_roi_bool = roi_iou_max >= 0.5 + positive_indices = tf.where(positive_roi_bool)[:, 0] + # 2. Negative ROIs are those with < 0.5 with every GT box. Skip crowds. + negative_indices = tf.where(tf.logical_and(roi_iou_max < 0.5, no_crowd_bool))[:, 0] + + # Subsample ROIs. Aim for 33% positive + # Positive ROIs + positive_count = int(config.TRAIN_ROIS_PER_IMAGE * config.ROI_POSITIVE_RATIO) + positive_indices = tf.random_shuffle(positive_indices)[:positive_count] + positive_count = tf.shape(positive_indices)[0] + # Negative ROIs. Add enough to maintain positive:negative ratio. + r = 1.0 / config.ROI_POSITIVE_RATIO + negative_count = ( + tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count + ) + negative_indices = tf.random_shuffle(negative_indices)[:negative_count] + # Gather selected ROIs + positive_rois = tf.gather(proposals, positive_indices) + negative_rois = tf.gather(proposals, negative_indices) + + # Assign positive ROIs to GT boxes. + positive_overlaps = tf.gather(overlaps, positive_indices) + roi_gt_box_assignment = tf.cond( + tf.greater(tf.shape(positive_overlaps)[1], 0), + true_fn=lambda: tf.argmax(positive_overlaps, axis=1), + false_fn=lambda: tf.cast(tf.constant([]), tf.int64), + ) + roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment) + roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment) + + # Compute bbox refinement for positive ROIs + deltas = utils.box_refinement_graph(positive_rois, roi_gt_boxes) + deltas /= config.BBOX_STD_DEV + + # Assign positive ROIs to GT masks + # Permute masks to [N, height, width, 1] + transposed_masks = tf.expand_dims(tf.transpose(gt_masks, [2, 0, 1]), -1) + # Pick the right mask for each ROI + roi_masks = tf.gather(transposed_masks, roi_gt_box_assignment) + + # Compute mask targets + boxes = positive_rois + if config.USE_MINI_MASK: + # Transform ROI coordinates from normalized image space + # to normalized mini-mask space. + y1, x1, y2, x2 = tf.split(positive_rois, 4, axis=1) + gt_y1, gt_x1, gt_y2, gt_x2 = tf.split(roi_gt_boxes, 4, axis=1) + gt_h = gt_y2 - gt_y1 + gt_w = gt_x2 - gt_x1 + y1 = (y1 - gt_y1) / gt_h + x1 = (x1 - gt_x1) / gt_w + y2 = (y2 - gt_y1) / gt_h + x2 = (x2 - gt_x1) / gt_w + boxes = tf.concat([y1, x1, y2, x2], 1) + box_ids = tf.range(0, tf.shape(roi_masks)[0]) + masks = tf.image.crop_and_resize( + tf.cast(roi_masks, tf.float32), boxes, box_ids, config.MASK_SHAPE + ) + # Remove the extra dimension from masks. + masks = tf.squeeze(masks, axis=3) + + # Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with + # binary cross entropy loss. + masks = tf.round(masks) + + # Append negative ROIs and pad bbox deltas and masks that + # are not used for negative ROIs with zeros. + rois = tf.concat([positive_rois, negative_rois], axis=0) + N = tf.shape(negative_rois)[0] + P = tf.maximum(config.TRAIN_ROIS_PER_IMAGE - tf.shape(rois)[0], 0) + rois = tf.pad(rois, [(0, P), (0, 0)]) + roi_gt_boxes = tf.pad(roi_gt_boxes, [(0, N + P), (0, 0)]) + roi_gt_class_ids = tf.pad(roi_gt_class_ids, [(0, N + P)]) + deltas = tf.pad(deltas, [(0, N + P), (0, 0)]) + masks = tf.pad(masks, [[0, N + P], (0, 0), (0, 0)]) + + return rois, roi_gt_class_ids, deltas, masks + + +class DetectionTargetLayer(KE.Layer): + """Subsamples proposals and generates target box refinement, class_ids, + and masks for each. + + Inputs: + proposals: [batch, N, (y1, x1, y2, x2)] in normalized coordinates. Might + be zero padded if there are not enough proposals. + gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs. + gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized + coordinates. + gt_masks: [batch, height, width, MAX_GT_INSTANCES] of boolean type + + Returns: Target ROIs and corresponding class IDs, bounding box shifts, + and masks. + rois: [batch, TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized + coordinates + target_class_ids: [batch, TRAIN_ROIS_PER_IMAGE]. Integer class IDs. + target_deltas: [batch, TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw)] + target_mask: [batch, TRAIN_ROIS_PER_IMAGE, height, width] + Masks cropped to bbox boundaries and resized to neural + network output size. + + Note: Returned arrays might be zero padded if not enough target ROIs. + """ + + def __init__(self, config, **kwargs): + super(DetectionTargetLayer, self).__init__(**kwargs) + self.config = config + + def call(self, inputs): + proposals = inputs[0] + gt_class_ids = inputs[1] + gt_boxes = inputs[2] + gt_masks = inputs[3] + + # Slice the batch and run a graph for each slice + # TODO: Rename target_bbox to target_deltas for clarity + names = ["rois", "target_class_ids", "target_bbox", "target_mask"] + outputs = utils.batch_slice( + [proposals, gt_class_ids, gt_boxes, gt_masks], + lambda w, x, y, z: detection_targets_graph(w, x, y, z, self.config), + self.config.IMAGES_PER_GPU, + names=names, + ) + return outputs + + def compute_output_shape(self, input_shape): + return [ + (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # rois + (None, self.config.TRAIN_ROIS_PER_IMAGE), # class_ids + (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # deltas + ( + None, + self.config.TRAIN_ROIS_PER_IMAGE, + self.config.MASK_SHAPE[0], + self.config.MASK_SHAPE[1], + ), # masks + ] + + def compute_mask(self, inputs, mask=None): + return [None, None, None, None] + + +############################################################ +# Detection Layer +############################################################ + + +def refine_detections_graph(rois, probs, deltas, window, config): + """Refine classified proposals and filter overlaps and return final + detections. + + Inputs: + rois: [N, (y1, x1, y2, x2)] in normalized coordinates + probs: [N, num_classes]. Class probabilities. + deltas: [N, num_classes, (dy, dx, log(dh), log(dw))]. Class-specific + bounding box deltas. + window: (y1, x1, y2, x2) in normalized coordinates. The part of the image + that contains the image excluding the padding. + + Returns detections shaped: [num_detections, (y1, x1, y2, x2, class_id, score)] where + coordinates are normalized. + """ + # Class IDs per ROI + class_ids = tf.argmax(probs, axis=1, output_type=tf.int32) + # Class probability of the top class of each ROI + indices = tf.stack([tf.range(probs.shape[0]), class_ids], axis=1) + class_scores = tf.gather_nd(probs, indices) + # Class-specific bounding box deltas + deltas_specific = tf.gather_nd(deltas, indices) + # Apply bounding box deltas + # Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinates + refined_rois = apply_box_deltas_graph(rois, deltas_specific * config.BBOX_STD_DEV) + # Clip boxes to image window + refined_rois = clip_boxes_graph(refined_rois, window) + + # TODO: Filter out boxes with zero area + + # Filter out background boxes + keep = tf.where(class_ids > 0)[:, 0] + # Filter out low confidence boxes + if config.DETECTION_MIN_CONFIDENCE: + conf_keep = tf.where(class_scores >= config.DETECTION_MIN_CONFIDENCE)[:, 0] + keep = tf.sets.set_intersection( + tf.expand_dims(keep, 0), tf.expand_dims(conf_keep, 0) + ) + keep = tf.sparse_tensor_to_dense(keep)[0] + + # Apply per-class NMS + # 1. Prepare variables + pre_nms_class_ids = tf.gather(class_ids, keep) + pre_nms_scores = tf.gather(class_scores, keep) + pre_nms_rois = tf.gather(refined_rois, keep) + unique_pre_nms_class_ids = tf.unique(pre_nms_class_ids)[0] + + def nms_keep_map(class_id): + """Apply Non-Maximum Suppression on ROIs of the given class.""" + # Indices of ROIs of the given class + ixs = tf.where(tf.equal(pre_nms_class_ids, class_id))[:, 0] + # Apply NMS + class_keep = tf.image.non_max_suppression( + tf.gather(pre_nms_rois, ixs), + tf.gather(pre_nms_scores, ixs), + max_output_size=config.DETECTION_MAX_INSTANCES, + iou_threshold=config.DETECTION_NMS_THRESHOLD, + ) + # Map indices + class_keep = tf.gather(keep, tf.gather(ixs, class_keep)) + # Pad with -1 so returned tensors have the same shape + gap = config.DETECTION_MAX_INSTANCES - tf.shape(class_keep)[0] + class_keep = tf.pad(class_keep, [(0, gap)], mode="CONSTANT", constant_values=-1) + # Set shape so map_fn() can infer result shape + class_keep.set_shape([config.DETECTION_MAX_INSTANCES]) + return class_keep + + # 2. Map over class IDs + nms_keep = tf.map_fn(nms_keep_map, unique_pre_nms_class_ids, dtype=tf.int64) + # 3. Merge results into one list, and remove -1 padding + nms_keep = tf.reshape(nms_keep, [-1]) + nms_keep = tf.gather(nms_keep, tf.where(nms_keep > -1)[:, 0]) + # 4. Compute intersection between keep and nms_keep + keep = tf.sets.set_intersection( + tf.expand_dims(keep, 0), tf.expand_dims(nms_keep, 0) + ) + keep = tf.sparse_tensor_to_dense(keep)[0] + # Keep top detections + roi_count = config.DETECTION_MAX_INSTANCES + class_scores_keep = tf.gather(class_scores, keep) + num_keep = tf.minimum(tf.shape(class_scores_keep)[0], roi_count) + top_ids = tf.nn.top_k(class_scores_keep, k=num_keep, sorted=True)[1] + keep = tf.gather(keep, top_ids) + + # Arrange output as [N, (y1, x1, y2, x2, class_id, score)] + # Coordinates are normalized. + detections = tf.concat( + [ + tf.gather(refined_rois, keep), + tf.to_float(tf.gather(class_ids, keep))[..., tf.newaxis], + tf.gather(class_scores, keep)[..., tf.newaxis], + ], + axis=1, + ) + + # Pad with zeros if detections < DETECTION_MAX_INSTANCES + gap = config.DETECTION_MAX_INSTANCES - tf.shape(detections)[0] + detections = tf.pad(detections, [(0, gap), (0, 0)], "CONSTANT") + return detections + + +class DetectionLayer(KE.Layer): + """Takes classified proposal boxes and their bounding box deltas and + returns the final detection boxes. + + Returns: + [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] where + coordinates are normalized. + """ + + def __init__(self, config=None, **kwargs): + super(DetectionLayer, self).__init__(**kwargs) + self.config = config + + def call(self, inputs): + rois = inputs[0] + mrcnn_class = inputs[1] + mrcnn_bbox = inputs[2] + image_meta = inputs[3] + + # Get windows of images in normalized coordinates. Windows are the area + # in the image that excludes the padding. + # Use the shape of the first image in the batch to normalize the window + # because we know that all images get resized to the same size. + m = parse_image_meta_graph(image_meta) + image_shape = m["image_shape"][0] + window = norm_boxes_graph(m["window"], image_shape[:2]) + + # Run detection refinement graph on each item in the batch + detections_batch = utils.batch_slice( + [rois, mrcnn_class, mrcnn_bbox, window], + lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config), + self.config.IMAGES_PER_GPU, + ) + + # Reshape output + # [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] in + # normalized coordinates + return tf.reshape( + detections_batch, + [self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6], + ) + + def compute_output_shape(self, input_shape): + return (None, self.config.DETECTION_MAX_INSTANCES, 6) + + +############################################################ +# Region Proposal Network (RPN) +############################################################ + + +def rpn_graph(feature_map, anchors_per_location, anchor_stride): + """Builds the computation graph of Region Proposal Network. + + feature_map: backbone features [batch, height, width, depth] + anchors_per_location: number of anchors per pixel in the feature map + anchor_stride: Controls the density of anchors. Typically 1 (anchors for + every pixel in the feature map), or 2 (every other pixel). + + Returns: + rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax) + rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities. + rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be + applied to anchors. + """ + # TODO: check if stride of 2 causes alignment issues if the feature map + # is not even. + # Shared convolutional base of the RPN + shared = KL.Conv2D( + 512, + (3, 3), + padding="same", + activation="relu", + strides=anchor_stride, + name="rpn_conv_shared", + )(feature_map) + + # Anchor Score. [batch, height, width, anchors per location * 2]. + x = KL.Conv2D( + 2 * anchors_per_location, + (1, 1), + padding="valid", + activation="linear", + name="rpn_class_raw", + )(shared) + + # Reshape to [batch, anchors, 2] + rpn_class_logits = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 2]))(x) + + # Softmax on last dimension of BG/FG. + rpn_probs = KL.Activation("softmax", name="rpn_class_xxx")(rpn_class_logits) + + # Bounding box refinement. [batch, H, W, anchors per location * depth] + # where depth is [x, y, log(w), log(h)] + x = KL.Conv2D( + anchors_per_location * 4, + (1, 1), + padding="valid", + activation="linear", + name="rpn_bbox_pred", + )(shared) + + # Reshape to [batch, anchors, 4] + rpn_bbox = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 4]))(x) + + return [rpn_class_logits, rpn_probs, rpn_bbox] + + +def build_rpn_model(anchor_stride, anchors_per_location, depth): + """Builds a Keras model of the Region Proposal Network. + It wraps the RPN graph so it can be used multiple times with shared + weights. + + anchors_per_location: number of anchors per pixel in the feature map + anchor_stride: Controls the density of anchors. Typically 1 (anchors for + every pixel in the feature map), or 2 (every other pixel). + depth: Depth of the backbone feature map. + + Returns a Keras Model object. The model outputs, when called, are: + rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax) + rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities. + rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be + applied to anchors. + """ + input_feature_map = KL.Input( + shape=[None, None, depth], name="input_rpn_feature_map" + ) + outputs = rpn_graph(input_feature_map, anchors_per_location, anchor_stride) + return KM.Model([input_feature_map], outputs, name="rpn_model") + + +############################################################ +# Feature Pyramid Network Heads +############################################################ + + +def fpn_classifier_graph( + rois, + feature_maps, + image_meta, + pool_size, + num_classes, + train_bn=True, + fc_layers_size=1024, +): + """Builds the computation graph of the feature pyramid network classifier + and regressor heads. + + rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized + coordinates. + feature_maps: List of feature maps from different layers of the pyramid, + [P2, P3, P4, P5]. Each has a different resolution. + image_meta: [batch, (meta data)] Image details. See compose_image_meta() + pool_size: The width of the square feature map generated from ROI Pooling. + num_classes: number of classes, which determines the depth of the results + train_bn: Boolean. Train or freeze Batch Norm layers + fc_layers_size: Size of the 2 FC layers + + Returns: + logits: [batch, num_rois, NUM_CLASSES] classifier logits (before softmax) + probs: [batch, num_rois, NUM_CLASSES] classifier probabilities + bbox_deltas: [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] Deltas to apply to + proposal boxes + """ + # ROI Pooling + # Shape: [batch, num_rois, POOL_SIZE, POOL_SIZE, channels] + x = PyramidROIAlign([pool_size, pool_size], name="roi_align_classifier")( + [rois, image_meta] + feature_maps + ) + # Two 1024 FC layers (implemented with Conv2D for consistency) + x = KL.TimeDistributed( + KL.Conv2D(fc_layers_size, (pool_size, pool_size), padding="valid"), + name="mrcnn_class_conv1", + )(x) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_class_bn1")(x, training=train_bn) + x = KL.Activation("relu")(x) + x = KL.TimeDistributed(KL.Conv2D(fc_layers_size, (1, 1)), name="mrcnn_class_conv2")( + x + ) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_class_bn2")(x, training=train_bn) + x = KL.Activation("relu")(x) + + shared = KL.Lambda(lambda x: K.squeeze(K.squeeze(x, 3), 2), name="pool_squeeze")(x) + + # Classifier head + mrcnn_class_logits = KL.TimeDistributed( + KL.Dense(num_classes), name="mrcnn_class_logits" + )(shared) + mrcnn_probs = KL.TimeDistributed(KL.Activation("softmax"), name="mrcnn_class")( + mrcnn_class_logits + ) + + # BBox head + # [batch, num_rois, NUM_CLASSES * (dy, dx, log(dh), log(dw))] + x = KL.TimeDistributed( + KL.Dense(num_classes * 4, activation="linear"), name="mrcnn_bbox_fc" + )(shared) + # Reshape to [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] + s = K.int_shape(x) + mrcnn_bbox = KL.Reshape((s[1], num_classes, 4), name="mrcnn_bbox")(x) + + return mrcnn_class_logits, mrcnn_probs, mrcnn_bbox + + +def build_fpn_mask_graph( + rois, feature_maps, image_meta, pool_size, num_classes, train_bn=True +): + """Builds the computation graph of the mask head of Feature Pyramid Network. + + rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized + coordinates. + feature_maps: List of feature maps from different layers of the pyramid, + [P2, P3, P4, P5]. Each has a different resolution. + image_meta: [batch, (meta data)] Image details. See compose_image_meta() + pool_size: The width of the square feature map generated from ROI Pooling. + num_classes: number of classes, which determines the depth of the results + train_bn: Boolean. Train or freeze Batch Norm layers + + Returns: Masks [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, NUM_CLASSES] + """ + # ROI Pooling + # Shape: [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, channels] + x = PyramidROIAlign([pool_size, pool_size], name="roi_align_mask")( + [rois, image_meta] + feature_maps + ) + + # Conv layers + x = KL.TimeDistributed( + KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv1" + )(x) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_mask_bn1")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.TimeDistributed( + KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv2" + )(x) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_mask_bn2")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.TimeDistributed( + KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv3" + )(x) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_mask_bn3")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.TimeDistributed( + KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv4" + )(x) + x = KL.TimeDistributed(BatchNorm(), name="mrcnn_mask_bn4")(x, training=train_bn) + x = KL.Activation("relu")(x) + + x = KL.TimeDistributed( + KL.Conv2DTranspose(256, (2, 2), strides=2, activation="relu"), + name="mrcnn_mask_deconv", + )(x) + x = KL.TimeDistributed( + KL.Conv2D(num_classes, (1, 1), strides=1, activation="sigmoid"), + name="mrcnn_mask", + )(x) + return x + + +############################################################ +# Loss Functions +############################################################ + + +def smooth_l1_loss(y_true, y_pred): + """Implements Smooth-L1 loss. + y_true and y_pred are typically: [N, 4], but could be any shape. + """ + diff = K.abs(y_true - y_pred) + less_than_one = K.cast(K.less(diff, 1.0), "float32") + loss = (less_than_one * 0.5 * diff**2) + (1 - less_than_one) * (diff - 0.5) + return loss + + +def rpn_class_loss_graph(rpn_match, rpn_class_logits): + """RPN anchor classifier loss. + + rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive, + -1=negative, 0=neutral anchor. + rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for BG/FG. + """ + # Squeeze last dim to simplify + rpn_match = tf.squeeze(rpn_match, -1) + # Get anchor classes. Convert the -1/+1 match to 0/1 values. + anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32) + # Positive and Negative anchors contribute to the loss, + # but neutral anchors (match value = 0) don't. + indices = tf.where(K.not_equal(rpn_match, 0)) + # Pick rows that contribute to the loss and filter out the rest. + rpn_class_logits = tf.gather_nd(rpn_class_logits, indices) + anchor_class = tf.gather_nd(anchor_class, indices) + # Cross entropy loss + loss = K.sparse_categorical_crossentropy( + target=anchor_class, output=rpn_class_logits, from_logits=True + ) + loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0)) + return loss + + +def rpn_bbox_loss_graph(config, target_bbox, rpn_match, rpn_bbox): + """Return the RPN bounding box loss graph. + + config: the model config object. + target_bbox: [batch, max positive anchors, (dy, dx, log(dh), log(dw))]. + Uses 0 padding to fill in unsed bbox deltas. + rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive, + -1=negative, 0=neutral anchor. + rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))] + """ + # Positive anchors contribute to the loss, but negative and + # neutral anchors (match value of 0 or -1) don't. + rpn_match = K.squeeze(rpn_match, -1) + indices = tf.where(K.equal(rpn_match, 1)) + + # Pick bbox deltas that contribute to the loss + rpn_bbox = tf.gather_nd(rpn_bbox, indices) + + # Trim target bounding box deltas to the same length as rpn_bbox. + batch_counts = K.sum(K.cast(K.equal(rpn_match, 1), tf.int32), axis=1) + target_bbox = batch_pack_graph(target_bbox, batch_counts, config.IMAGES_PER_GPU) + + loss = smooth_l1_loss(target_bbox, rpn_bbox) + + loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0)) + return loss + + +def mrcnn_class_loss_graph(target_class_ids, pred_class_logits, active_class_ids): + """Loss for the classifier head of Mask RCNN. + + target_class_ids: [batch, num_rois]. Integer class IDs. Uses zero + padding to fill in the array. + pred_class_logits: [batch, num_rois, num_classes] + active_class_ids: [batch, num_classes]. Has a value of 1 for + classes that are in the dataset of the image, and 0 + for classes that are not in the dataset. + """ + # During model building, Keras calls this function with + # target_class_ids of type float32. Unclear why. Cast it + # to int to get around it. + target_class_ids = tf.cast(target_class_ids, "int64") + + # Find predictions of classes that are not in the dataset. + pred_class_ids = tf.argmax(pred_class_logits, axis=2) + # TODO: Update this line to work with batch > 1. Right now it assumes all + # images in a batch have the same active_class_ids + pred_active = tf.gather(active_class_ids[0], pred_class_ids) + + # Loss + loss = tf.nn.sparse_softmax_cross_entropy_with_logits( + labels=target_class_ids, logits=pred_class_logits + ) + + # Erase losses of predictions of classes that are not in the active + # classes of the image. + loss = loss * pred_active + + # Computer loss mean. Use only predictions that contribute + # to the loss to get a correct mean. + loss = tf.reduce_sum(loss) / tf.reduce_sum(pred_active) + return loss + + +def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox): + """Loss for Mask R-CNN bounding box refinement. + + target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))] + target_class_ids: [batch, num_rois]. Integer class IDs. + pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))] + """ + # Reshape to merge batch and roi dimensions for simplicity. + target_class_ids = K.reshape(target_class_ids, (-1,)) + target_bbox = K.reshape(target_bbox, (-1, 4)) + pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4)) + + # Only positive ROIs contribute to the loss. And only + # the right class_id of each ROI. Get their indices. + positive_roi_ix = tf.where(target_class_ids > 0)[:, 0] + positive_roi_class_ids = tf.cast( + tf.gather(target_class_ids, positive_roi_ix), tf.int64 + ) + indices = tf.stack([positive_roi_ix, positive_roi_class_ids], axis=1) + + # Gather the deltas (predicted and true) that contribute to loss + target_bbox = tf.gather(target_bbox, positive_roi_ix) + pred_bbox = tf.gather_nd(pred_bbox, indices) + + # Smooth-L1 Loss + loss = K.switch( + tf.size(target_bbox) > 0, + smooth_l1_loss(y_true=target_bbox, y_pred=pred_bbox), + tf.constant(0.0), + ) + loss = K.mean(loss) + return loss + + +def mrcnn_mask_loss_graph(target_masks, target_class_ids, pred_masks): + """Mask binary cross-entropy loss for the masks head. + + target_masks: [batch, num_rois, height, width]. + A float32 tensor of values 0 or 1. Uses zero padding to fill array. + target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded. + pred_masks: [batch, proposals, height, width, num_classes] float32 tensor + with values from 0 to 1. + """ + # Reshape for simplicity. Merge first two dimensions into one. + target_class_ids = K.reshape(target_class_ids, (-1,)) + mask_shape = tf.shape(target_masks) + target_masks = K.reshape(target_masks, (-1, mask_shape[2], mask_shape[3])) + pred_shape = tf.shape(pred_masks) + pred_masks = K.reshape( + pred_masks, (-1, pred_shape[2], pred_shape[3], pred_shape[4]) + ) + # Permute predicted masks to [N, num_classes, height, width] + pred_masks = tf.transpose(pred_masks, [0, 3, 1, 2]) + + # Only positive ROIs contribute to the loss. And only + # the class specific mask of each ROI. + positive_ix = tf.where(target_class_ids > 0)[:, 0] + positive_class_ids = tf.cast(tf.gather(target_class_ids, positive_ix), tf.int64) + indices = tf.stack([positive_ix, positive_class_ids], axis=1) + + # Gather the masks (predicted and true) that contribute to loss + y_true = tf.gather(target_masks, positive_ix) + y_pred = tf.gather_nd(pred_masks, indices) + + # Compute binary cross entropy. If no positive ROIs, then return 0. + # shape: [batch, roi, num_classes] + loss = K.switch( + tf.size(y_true) > 0, + K.binary_crossentropy(target=y_true, output=y_pred), + tf.constant(0.0), + ) + loss = K.mean(loss) + return loss + + +############################################################ +# Data Generator +############################################################ + + +def load_image_gt( + dataset, config, image_id, augment=False, augmentation=None, use_mini_mask=False +): + """Load and return ground truth data for an image (image, mask, bounding boxes). + + augment: (deprecated. Use augmentation instead). If true, apply random + image augmentation. Currently, only horizontal flipping is offered. + augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation. + For example, passing imgaug.augmenters.Fliplr(0.5) flips images + right/left 50% of the time. + use_mini_mask: If False, returns full-size masks that are the same height + and width as the original image. These can be big, for example + 1024x1024x100 (for 100 instances). Mini masks are smaller, typically, + 224x224 and are generated by extracting the bounding box of the + object and resizing it to MINI_MASK_SHAPE. + + Returns: + image: [height, width, 3] + shape: the original shape of the image before resizing and cropping. + class_ids: [instance_count] Integer class IDs + bbox: [instance_count, (y1, x1, y2, x2)] + mask: [height, width, instance_count]. The height and width are those + of the image unless use_mini_mask is True, in which case they are + defined in MINI_MASK_SHAPE. + """ + # Load image and mask + image = dataset.load_image(image_id) + mask, class_ids = dataset.load_mask(image_id) + original_shape = image.shape + image, window, scale, padding, crop = utils.resize_image( + image, + min_dim=config.IMAGE_MIN_DIM, + min_scale=config.IMAGE_MIN_SCALE, + max_dim=config.IMAGE_MAX_DIM, + mode=config.IMAGE_RESIZE_MODE, + ) + mask = utils.resize_mask(mask, scale, padding, crop) + + # Random horizontal flips. + # TODO: will be removed in a future update in favor of augmentation + if augment: + logging.warning("'augment' is deprecated. Use 'augmentation' instead.") + if random.randint(0, 1): + image = np.fliplr(image) + mask = np.fliplr(mask) + + # Augmentation + # This requires the imgaug lib (https://github.com/aleju/imgaug) + if augmentation: + import imgaug + + # Augmenters that are safe to apply to masks + # Some, such as Affine, have settings that make them unsafe, so always + # test your augmentation on masks + MASK_AUGMENTERS = [ + "Sequential", + "SomeOf", + "OneOf", + "Sometimes", + "Fliplr", + "Flipud", + "CropAndPad", + "Affine", + "PiecewiseAffine", + ] + + def hook(images, augmenter, parents, default): + """Determines which augmenters to apply to masks.""" + return augmenter.__class__.__name__ in MASK_AUGMENTERS + + # Store shapes before augmentation to compare + image_shape = image.shape + mask_shape = mask.shape + # Make augmenters deterministic to apply similarly to images and masks + det = augmentation.to_deterministic() + image = det.augment_image(image) + # Change mask to np.uint8 because imgaug doesn't support np.bool + mask = det.augment_image( + mask.astype(np.uint8), hooks=imgaug.HooksImages(activator=hook) + ) + # Verify that shapes didn't change + assert image.shape == image_shape, "Augmentation shouldn't change image size" + assert mask.shape == mask_shape, "Augmentation shouldn't change mask size" + # Change mask back to bool + mask = mask.astype(np.bool) + + # Note that some boxes might be all zeros if the corresponding mask got cropped out. + # and here is to filter them out + _idx = np.sum(mask, axis=(0, 1)) > 0 + mask = mask[:, :, _idx] + class_ids = class_ids[_idx] + # Bounding boxes. Note that some boxes might be all zeros + # if the corresponding mask got cropped out. + # bbox: [num_instances, (y1, x1, y2, x2)] + bbox = utils.extract_bboxes(mask) + + # Active classes + # Different datasets have different classes, so track the + # classes supported in the dataset of this image. + active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32) + source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]["source"]] + active_class_ids[source_class_ids] = 1 + + # Resize masks to smaller size to reduce memory usage + if use_mini_mask: + mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE) + + # Image meta data + image_meta = compose_image_meta( + image_id, original_shape, image.shape, window, scale, active_class_ids + ) + + return image, image_meta, class_ids, bbox, mask + + +def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config): + """Generate targets for training Stage 2 classifier and mask heads. + This is not used in normal training. It's useful for debugging or to train + the Mask RCNN heads without using the RPN head. + + Inputs: + rpn_rois: [N, (y1, x1, y2, x2)] proposal boxes. + gt_class_ids: [instance count] Integer class IDs + gt_boxes: [instance count, (y1, x1, y2, x2)] + gt_masks: [height, width, instance count] Ground truth masks. Can be full + size or mini-masks. + + Returns: + rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] + class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. + bboxes: [TRAIN_ROIS_PER_IMAGE, NUM_CLASSES, (y, x, log(h), log(w))]. Class-specific + bbox refinements. + masks: [TRAIN_ROIS_PER_IMAGE, height, width, NUM_CLASSES). Class specific masks cropped + to bbox boundaries and resized to neural network output size. + """ + assert rpn_rois.shape[0] > 0 + assert gt_class_ids.dtype == np.int32, "Expected int but got {}".format( + gt_class_ids.dtype + ) + assert gt_boxes.dtype == np.int32, "Expected int but got {}".format(gt_boxes.dtype) + assert gt_masks.dtype == np.bool_, "Expected bool but got {}".format(gt_masks.dtype) + + # It's common to add GT Boxes to ROIs but we don't do that here because + # according to XinLei Chen's paper, it doesn't help. + + # Trim empty padding in gt_boxes and gt_masks parts + instance_ids = np.where(gt_class_ids > 0)[0] + assert instance_ids.shape[0] > 0, "Image must contain instances." + gt_class_ids = gt_class_ids[instance_ids] + gt_boxes = gt_boxes[instance_ids] + gt_masks = gt_masks[:, :, instance_ids] + + # Compute areas of ROIs and ground truth boxes. + rpn_roi_area = (rpn_rois[:, 2] - rpn_rois[:, 0]) * (rpn_rois[:, 3] - rpn_rois[:, 1]) + gt_box_area = (gt_boxes[:, 2] - gt_boxes[:, 0]) * (gt_boxes[:, 3] - gt_boxes[:, 1]) + + # Compute overlaps [rpn_rois, gt_boxes] + overlaps = np.zeros((rpn_rois.shape[0], gt_boxes.shape[0])) + for i in range(overlaps.shape[1]): + gt = gt_boxes[i] + overlaps[:, i] = utils.compute_iou(gt, rpn_rois, gt_box_area[i], rpn_roi_area) + + # Assign ROIs to GT boxes + rpn_roi_iou_argmax = np.argmax(overlaps, axis=1) + rpn_roi_iou_max = overlaps[np.arange(overlaps.shape[0]), rpn_roi_iou_argmax] + # GT box assigned to each ROI + rpn_roi_gt_boxes = gt_boxes[rpn_roi_iou_argmax] + rpn_roi_gt_class_ids = gt_class_ids[rpn_roi_iou_argmax] + + # Positive ROIs are those with >= 0.5 IoU with a GT box. + fg_ids = np.where(rpn_roi_iou_max > 0.5)[0] + + # Negative ROIs are those with max IoU 0.1-0.5 (hard example mining) + # TODO: To hard example mine or not to hard example mine, that's the question + # bg_ids = np.where((rpn_roi_iou_max >= 0.1) & (rpn_roi_iou_max < 0.5))[0] + bg_ids = np.where(rpn_roi_iou_max < 0.5)[0] + + # Subsample ROIs. Aim for 33% foreground. + # FG + fg_roi_count = int(config.TRAIN_ROIS_PER_IMAGE * config.ROI_POSITIVE_RATIO) + if fg_ids.shape[0] > fg_roi_count: + keep_fg_ids = np.random.choice(fg_ids, fg_roi_count, replace=False) + else: + keep_fg_ids = fg_ids + # BG + remaining = config.TRAIN_ROIS_PER_IMAGE - keep_fg_ids.shape[0] + if bg_ids.shape[0] > remaining: + keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False) + else: + keep_bg_ids = bg_ids + # Combine indices of ROIs to keep + keep = np.concatenate([keep_fg_ids, keep_bg_ids]) + # Need more? + remaining = config.TRAIN_ROIS_PER_IMAGE - keep.shape[0] + if remaining > 0: + # Looks like we don't have enough samples to maintain the desired + # balance. Reduce requirements and fill in the rest. This is + # likely different from the Mask RCNN paper. + + # There is a small chance we have neither fg nor bg samples. + if keep.shape[0] == 0: + # Pick bg regions with easier IoU threshold + bg_ids = np.where(rpn_roi_iou_max < 0.5)[0] + assert bg_ids.shape[0] >= remaining + keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False) + assert keep_bg_ids.shape[0] == remaining + keep = np.concatenate([keep, keep_bg_ids]) + else: + # Fill the rest with repeated bg rois. + keep_extra_ids = np.random.choice(keep_bg_ids, remaining, replace=True) + keep = np.concatenate([keep, keep_extra_ids]) + assert ( + keep.shape[0] == config.TRAIN_ROIS_PER_IMAGE + ), "keep doesn't match ROI batch size {}, {}".format( + keep.shape[0], config.TRAIN_ROIS_PER_IMAGE + ) + + # Reset the gt boxes assigned to BG ROIs. + rpn_roi_gt_boxes[keep_bg_ids, :] = 0 + rpn_roi_gt_class_ids[keep_bg_ids] = 0 + + # For each kept ROI, assign a class_id, and for FG ROIs also add bbox refinement. + rois = rpn_rois[keep] + roi_gt_boxes = rpn_roi_gt_boxes[keep] + roi_gt_class_ids = rpn_roi_gt_class_ids[keep] + roi_gt_assignment = rpn_roi_iou_argmax[keep] + + # Class-aware bbox deltas. [y, x, log(h), log(w)] + bboxes = np.zeros( + (config.TRAIN_ROIS_PER_IMAGE, config.NUM_CLASSES, 4), dtype=np.float32 + ) + pos_ids = np.where(roi_gt_class_ids > 0)[0] + bboxes[pos_ids, roi_gt_class_ids[pos_ids]] = utils.box_refinement( + rois[pos_ids], roi_gt_boxes[pos_ids, :4] + ) + # Normalize bbox refinements + bboxes /= config.BBOX_STD_DEV + + # Generate class-specific target masks + masks = np.zeros( + ( + config.TRAIN_ROIS_PER_IMAGE, + config.MASK_SHAPE[0], + config.MASK_SHAPE[1], + config.NUM_CLASSES, + ), + dtype=np.float32, + ) + for i in pos_ids: + class_id = roi_gt_class_ids[i] + assert class_id > 0, "class id must be greater than 0" + gt_id = roi_gt_assignment[i] + class_mask = gt_masks[:, :, gt_id] + + if config.USE_MINI_MASK: + # Create a mask placeholder, the size of the image + placeholder = np.zeros(config.IMAGE_SHAPE[:2], dtype=bool) + # GT box + gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[gt_id] + gt_w = gt_x2 - gt_x1 + gt_h = gt_y2 - gt_y1 + # Resize mini mask to size of GT box + placeholder[gt_y1:gt_y2, gt_x1:gt_x2] = np.round( + utils.resize(class_mask, (gt_h, gt_w)) + ).astype(bool) + # Place the mini batch in the placeholder + class_mask = placeholder + + # Pick part of the mask and resize it + y1, x1, y2, x2 = rois[i].astype(np.int32) + m = class_mask[y1:y2, x1:x2] + mask = utils.resize(m, config.MASK_SHAPE) + masks[i, :, :, class_id] = mask + + return rois, roi_gt_class_ids, bboxes, masks + + +def build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config): + """Given the anchors and GT boxes, compute overlaps and identify positive + anchors and deltas to refine them to match their corresponding GT boxes. + + anchors: [num_anchors, (y1, x1, y2, x2)] + gt_class_ids: [num_gt_boxes] Integer class IDs. + gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)] + + Returns: + rpn_match: [N] (int32) matches between anchors and GT boxes. + 1 = positive anchor, -1 = negative anchor, 0 = neutral + rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas. + """ + # RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral + rpn_match = np.zeros([anchors.shape[0]], dtype=np.int32) + # RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))] + rpn_bbox = np.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4)) + + # Handle COCO crowds + # A crowd box in COCO is a bounding box around several instances. Exclude + # them from training. A crowd box is given a negative class ID. + crowd_ix = np.where(gt_class_ids < 0)[0] + if crowd_ix.shape[0] > 0: + # Filter out crowds from ground truth class IDs and boxes + non_crowd_ix = np.where(gt_class_ids > 0)[0] + crowd_boxes = gt_boxes[crowd_ix] + gt_class_ids = gt_class_ids[non_crowd_ix] + gt_boxes = gt_boxes[non_crowd_ix] + # Compute overlaps with crowd boxes [anchors, crowds] + crowd_overlaps = utils.compute_overlaps(anchors, crowd_boxes) + crowd_iou_max = np.amax(crowd_overlaps, axis=1) + no_crowd_bool = crowd_iou_max < 0.001 + else: + # All anchors don't intersect a crowd + no_crowd_bool = np.ones([anchors.shape[0]], dtype=bool) + + # Compute overlaps [num_anchors, num_gt_boxes] + overlaps = utils.compute_overlaps(anchors, gt_boxes) + + # Match anchors to GT Boxes + # If an anchor overlaps a GT box with IoU >= 0.7 then it's positive. + # If an anchor overlaps a GT box with IoU < 0.3 then it's negative. + # Neutral anchors are those that don't match the conditions above, + # and they don't influence the loss function. + # However, don't keep any GT box unmatched (rare, but happens). Instead, + # match it to the closest anchor (even if its max IoU is < 0.3). + # + # 1. Set negative anchors first. They get overwritten below if a GT box is + # matched to them. Skip boxes in crowd areas. + anchor_iou_argmax = np.argmax(overlaps, axis=1) + anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax] + rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1 + # 2. Set an anchor for each GT box (regardless of IoU value). + # If multiple anchors have the same IoU match all of them + gt_iou_argmax = np.argwhere(overlaps == np.max(overlaps, axis=0))[:, 0] + rpn_match[gt_iou_argmax] = 1 + # 3. Set anchors with high overlap as positive. + rpn_match[anchor_iou_max >= 0.7] = 1 + + # Subsample to balance positive and negative anchors + # Don't let positives be more than half the anchors + ids = np.where(rpn_match == 1)[0] + extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2) + if extra > 0: + # Reset the extra ones to neutral + ids = np.random.choice(ids, extra, replace=False) + rpn_match[ids] = 0 + # Same for negative proposals + ids = np.where(rpn_match == -1)[0] + extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE - np.sum(rpn_match == 1)) + if extra > 0: + # Rest the extra ones to neutral + ids = np.random.choice(ids, extra, replace=False) + rpn_match[ids] = 0 + + # For positive anchors, compute shift and scale needed to transform them + # to match the corresponding GT boxes. + ids = np.where(rpn_match == 1)[0] + ix = 0 # index into rpn_bbox + # TODO: use box_refinement() rather than duplicating the code here + for i, a in zip(ids, anchors[ids]): + # Closest gt box (it might have IoU < 0.7) + gt = gt_boxes[anchor_iou_argmax[i]] + + # Convert coordinates to center plus width/height. + # GT Box + gt_h = gt[2] - gt[0] + gt_w = gt[3] - gt[1] + gt_center_y = gt[0] + 0.5 * gt_h + gt_center_x = gt[1] + 0.5 * gt_w + # Anchor + a_h = a[2] - a[0] + a_w = a[3] - a[1] + a_center_y = a[0] + 0.5 * a_h + a_center_x = a[1] + 0.5 * a_w + + # Compute the bbox refinement that the RPN should predict. + rpn_bbox[ix] = [ + (gt_center_y - a_center_y) / a_h, + (gt_center_x - a_center_x) / a_w, + np.log(gt_h / a_h), + np.log(gt_w / a_w), + ] + # Normalize + rpn_bbox[ix] /= config.RPN_BBOX_STD_DEV + ix += 1 + + return rpn_match, rpn_bbox + + +def generate_random_rois(image_shape, count, gt_class_ids, gt_boxes): + """Generates ROI proposals similar to what a region proposal network + would generate. + + image_shape: [Height, Width, Depth] + count: Number of ROIs to generate + gt_class_ids: [N] Integer ground truth class IDs + gt_boxes: [N, (y1, x1, y2, x2)] Ground truth boxes in pixels. + + Returns: [count, (y1, x1, y2, x2)] ROI boxes in pixels. + """ + # placeholder + rois = np.zeros((count, 4), dtype=np.int32) + + # Generate random ROIs around GT boxes (90% of count) + rois_per_box = int(0.9 * count / gt_boxes.shape[0]) + for i in range(gt_boxes.shape[0]): + gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[i] + h = gt_y2 - gt_y1 + w = gt_x2 - gt_x1 + # random boundaries + r_y1 = max(gt_y1 - h, 0) + r_y2 = min(gt_y2 + h, image_shape[0]) + r_x1 = max(gt_x1 - w, 0) + r_x2 = min(gt_x2 + w, image_shape[1]) + + # To avoid generating boxes with zero area, we generate double what + # we need and filter out the extra. If we get fewer valid boxes + # than we need, we loop and try again. + while True: + y1y2 = np.random.randint(r_y1, r_y2, (rois_per_box * 2, 2)) + x1x2 = np.random.randint(r_x1, r_x2, (rois_per_box * 2, 2)) + # Filter out zero area boxes + threshold = 1 + y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >= threshold][:rois_per_box] + x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >= threshold][:rois_per_box] + if y1y2.shape[0] == rois_per_box and x1x2.shape[0] == rois_per_box: + break + + # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape + # into x1, y1, x2, y2 order + x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1) + y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1) + box_rois = np.hstack([y1, x1, y2, x2]) + rois[rois_per_box * i : rois_per_box * (i + 1)] = box_rois + + # Generate random ROIs anywhere in the image (10% of count) + remaining_count = count - (rois_per_box * gt_boxes.shape[0]) + # To avoid generating boxes with zero area, we generate double what + # we need and filter out the extra. If we get fewer valid boxes + # than we need, we loop and try again. + while True: + y1y2 = np.random.randint(0, image_shape[0], (remaining_count * 2, 2)) + x1x2 = np.random.randint(0, image_shape[1], (remaining_count * 2, 2)) + # Filter out zero area boxes + threshold = 1 + y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >= threshold][:remaining_count] + x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >= threshold][:remaining_count] + if y1y2.shape[0] == remaining_count and x1x2.shape[0] == remaining_count: + break + + # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape + # into x1, y1, x2, y2 order + x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1) + y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1) + global_rois = np.hstack([y1, x1, y2, x2]) + rois[-remaining_count:] = global_rois + return rois + + +def data_generator( + dataset, + config, + shuffle=True, + augment=False, + augmentation=None, + random_rois=0, + batch_size=1, + detection_targets=False, + no_augmentation_sources=None, +): + """A generator that returns images and corresponding target class ids, + bounding box deltas, and masks. + + dataset: The Dataset object to pick data from + config: The model config object + shuffle: If True, shuffles the samples before every epoch + augment: (deprecated. Use augmentation instead). If true, apply random + image augmentation. Currently, only horizontal flipping is offered. + augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation. + For example, passing imgaug.augmenters.Fliplr(0.5) flips images + right/left 50% of the time. + random_rois: If > 0 then generate proposals to be used to train the + network classifier and mask heads. Useful if training + the Mask RCNN part without the RPN. + batch_size: How many images to return in each call + detection_targets: If True, generate detection targets (class IDs, bbox + deltas, and masks). Typically for debugging or visualizations because + in trainig detection targets are generated by DetectionTargetLayer. + no_augmentation_sources: Optional. List of sources to exclude for + augmentation. A source is string that identifies a dataset and is + defined in the Dataset class. + + Returns a Python generator. Upon calling next() on it, the + generator returns two lists, inputs and outputs. The contents + of the lists differs depending on the received arguments: + inputs list: + - images: [batch, H, W, C] + - image_meta: [batch, (meta data)] Image details. See compose_image_meta() + - rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral) + - rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas. + - gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs + - gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] + - gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width + are those of the image unless use_mini_mask is True, in which + case they are defined in MINI_MASK_SHAPE. + + outputs list: Usually empty in regular training. But if detection_targets + is True then the outputs list contains target class_ids, bbox deltas, + and masks. + """ + b = 0 # batch item index + image_index = -1 + image_ids = np.copy(dataset.image_ids) + error_count = 0 + no_augmentation_sources = no_augmentation_sources or [] + + # Anchors + # [anchor_count, (y1, x1, y2, x2)] + backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE) + anchors = utils.generate_pyramid_anchors( + config.RPN_ANCHOR_SCALES, + config.RPN_ANCHOR_RATIOS, + backbone_shapes, + config.BACKBONE_STRIDES, + config.RPN_ANCHOR_STRIDE, + ) + + # Keras requires a generator to run indefinitely. + while True: + try: + # Increment index to pick next image. Shuffle if at the start of an epoch. + image_index = (image_index + 1) % len(image_ids) + if shuffle and image_index == 0: + np.random.shuffle(image_ids) + + # Get GT bounding boxes and masks for image. + image_id = image_ids[image_index] + + # If the image source is not to be augmented pass None as augmentation + if dataset.image_info[image_id]["source"] in no_augmentation_sources: + image, image_meta, gt_class_ids, gt_boxes, gt_masks = load_image_gt( + dataset, + config, + image_id, + augment=augment, + augmentation=None, + use_mini_mask=config.USE_MINI_MASK, + ) + else: + image, image_meta, gt_class_ids, gt_boxes, gt_masks = load_image_gt( + dataset, + config, + image_id, + augment=augment, + augmentation=augmentation, + use_mini_mask=config.USE_MINI_MASK, + ) + + # Skip images that have no instances. This can happen in cases + # where we train on a subset of classes and the image doesn't + # have any of the classes we care about. + if not np.any(gt_class_ids > 0): + continue + + # RPN Targets + rpn_match, rpn_bbox = build_rpn_targets( + image.shape, anchors, gt_class_ids, gt_boxes, config + ) + + # Mask R-CNN Targets + if random_rois: + rpn_rois = generate_random_rois( + image.shape, random_rois, gt_class_ids, gt_boxes + ) + if detection_targets: + ( + rois, + mrcnn_class_ids, + mrcnn_bbox, + mrcnn_mask, + ) = build_detection_targets( + rpn_rois, gt_class_ids, gt_boxes, gt_masks, config + ) + + # Init batch arrays + if b == 0: + batch_image_meta = np.zeros( + (batch_size,) + image_meta.shape, dtype=image_meta.dtype + ) + batch_rpn_match = np.zeros( + [batch_size, anchors.shape[0], 1], dtype=rpn_match.dtype + ) + batch_rpn_bbox = np.zeros( + [batch_size, config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], + dtype=rpn_bbox.dtype, + ) + batch_images = np.zeros((batch_size,) + image.shape, dtype=np.float32) + batch_gt_class_ids = np.zeros( + (batch_size, config.MAX_GT_INSTANCES), dtype=np.int32 + ) + batch_gt_boxes = np.zeros( + (batch_size, config.MAX_GT_INSTANCES, 4), dtype=np.int32 + ) + batch_gt_masks = np.zeros( + ( + batch_size, + gt_masks.shape[0], + gt_masks.shape[1], + config.MAX_GT_INSTANCES, + ), + dtype=gt_masks.dtype, + ) + if random_rois: + batch_rpn_rois = np.zeros( + (batch_size, rpn_rois.shape[0], 4), dtype=rpn_rois.dtype + ) + if detection_targets: + batch_rois = np.zeros( + (batch_size,) + rois.shape, dtype=rois.dtype + ) + batch_mrcnn_class_ids = np.zeros( + (batch_size,) + mrcnn_class_ids.shape, + dtype=mrcnn_class_ids.dtype, + ) + batch_mrcnn_bbox = np.zeros( + (batch_size,) + mrcnn_bbox.shape, dtype=mrcnn_bbox.dtype + ) + batch_mrcnn_mask = np.zeros( + (batch_size,) + mrcnn_mask.shape, dtype=mrcnn_mask.dtype + ) + + # If more instances than fits in the array, sub-sample from them. + if gt_boxes.shape[0] > config.MAX_GT_INSTANCES: + ids = np.random.choice( + np.arange(gt_boxes.shape[0]), config.MAX_GT_INSTANCES, replace=False + ) + gt_class_ids = gt_class_ids[ids] + gt_boxes = gt_boxes[ids] + gt_masks = gt_masks[:, :, ids] + + # Add to batch + batch_image_meta[b] = image_meta + batch_rpn_match[b] = rpn_match[:, np.newaxis] + batch_rpn_bbox[b] = rpn_bbox + batch_images[b] = mold_image(image.astype(np.float32), config) + batch_gt_class_ids[b, : gt_class_ids.shape[0]] = gt_class_ids + batch_gt_boxes[b, : gt_boxes.shape[0]] = gt_boxes + batch_gt_masks[b, :, :, : gt_masks.shape[-1]] = gt_masks + if random_rois: + batch_rpn_rois[b] = rpn_rois + if detection_targets: + batch_rois[b] = rois + batch_mrcnn_class_ids[b] = mrcnn_class_ids + batch_mrcnn_bbox[b] = mrcnn_bbox + batch_mrcnn_mask[b] = mrcnn_mask + b += 1 + + # Batch full? + if b >= batch_size: + inputs = [ + batch_images, + batch_image_meta, + batch_rpn_match, + batch_rpn_bbox, + batch_gt_class_ids, + batch_gt_boxes, + batch_gt_masks, + ] + outputs = [] + + if random_rois: + inputs.extend([batch_rpn_rois]) + if detection_targets: + inputs.extend([batch_rois]) + # Keras requires that output and targets have the same number of dimensions + batch_mrcnn_class_ids = np.expand_dims( + batch_mrcnn_class_ids, -1 + ) + outputs.extend( + [batch_mrcnn_class_ids, batch_mrcnn_bbox, batch_mrcnn_mask] + ) + + yield inputs, outputs + + # start a new batch + b = 0 + except (GeneratorExit, KeyboardInterrupt): + raise + except: + # Log it and skip the image + logging.exception( + "Error processing image {}".format(dataset.image_info[image_id]) + ) + error_count += 1 + if error_count > 5: + raise + + +############################################################ +# MaskRCNN Class +############################################################ + + +class MaskRCNN: + """Encapsulates the Mask RCNN model functionality. + + The actual Keras model is in the keras_model property. + """ + + def __init__(self, mode, config, model_dir): + """ + mode: Either "training" or "inference" + config: A Sub-class of the Config class + model_dir: Directory to save training logs and trained weights + """ + assert mode in ["training", "inference"] + self.mode = mode + self.config = config + self.model_dir = model_dir + self.set_log_dir() + self.keras_model = self.build(mode=mode, config=config) + + def build(self, mode, config): + """Build Mask R-CNN architecture. + input_shape: The shape of the input image. + mode: Either "training" or "inference". The inputs and + outputs of the model differ accordingly. + """ + assert mode in ["training", "inference"] + + # Image size must be dividable by 2 multiple times + h, w = config.IMAGE_SHAPE[:2] + if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6): + raise Exception( + "Image size must be dividable by 2 at least 6 times " + "to avoid fractions when downscaling and upscaling." + "For example, use 256, 320, 384, 448, 512, ... etc. " + ) + + # Inputs + input_image = KL.Input( + shape=[None, None, config.IMAGE_SHAPE[2]], name="input_image" + ) + input_image_meta = KL.Input( + shape=[config.IMAGE_META_SIZE], name="input_image_meta" + ) + if mode == "training": + # RPN GT + input_rpn_match = KL.Input( + shape=[None, 1], name="input_rpn_match", dtype=tf.int32 + ) + input_rpn_bbox = KL.Input( + shape=[None, 4], name="input_rpn_bbox", dtype=tf.float32 + ) + + # Detection GT (class IDs, bounding boxes, and masks) + # 1. GT Class IDs (zero padded) + input_gt_class_ids = KL.Input( + shape=[None], name="input_gt_class_ids", dtype=tf.int32 + ) + # 2. GT Boxes in pixels (zero padded) + # [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in image coordinates + input_gt_boxes = KL.Input( + shape=[None, 4], name="input_gt_boxes", dtype=tf.float32 + ) + # Normalize coordinates + gt_boxes = KL.Lambda( + lambda x: norm_boxes_graph(x, K.shape(input_image)[1:3]) + )(input_gt_boxes) + # 3. GT Masks (zero padded) + # [batch, height, width, MAX_GT_INSTANCES] + if config.USE_MINI_MASK: + input_gt_masks = KL.Input( + shape=[config.MINI_MASK_SHAPE[0], config.MINI_MASK_SHAPE[1], None], + name="input_gt_masks", + dtype=bool, + ) + else: + input_gt_masks = KL.Input( + shape=[config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1], None], + name="input_gt_masks", + dtype=bool, + ) + elif mode == "inference": + # Anchors in normalized coordinates + input_anchors = KL.Input(shape=[None, 4], name="input_anchors") + + # Build the shared convolutional layers. + # Bottom-up Layers + # Returns a list of the last layers of each stage, 5 in total. + # Don't create the thead (stage 5), so we pick the 4th item in the list. + if callable(config.BACKBONE): + _, C2, C3, C4, C5 = config.BACKBONE( + input_image, stage5=True, train_bn=config.TRAIN_BN + ) + else: + _, C2, C3, C4, C5 = resnet_graph( + input_image, config.BACKBONE, stage5=True, train_bn=config.TRAIN_BN + ) + # Top-down Layers + # TODO: add assert to varify feature map sizes match what's in config + P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name="fpn_c5p5")(C5) + P4 = KL.Add(name="fpn_p4add")( + [ + KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5), + KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name="fpn_c4p4")(C4), + ] + ) + P3 = KL.Add(name="fpn_p3add")( + [ + KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4), + KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name="fpn_c3p3")(C3), + ] + ) + P2 = KL.Add(name="fpn_p2add")( + [ + KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3), + KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name="fpn_c2p2")(C2), + ] + ) + # Attach 3x3 conv to all P layers to get the final feature maps. + P2 = KL.Conv2D( + config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p2" + )(P2) + P3 = KL.Conv2D( + config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p3" + )(P3) + P4 = KL.Conv2D( + config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p4" + )(P4) + P5 = KL.Conv2D( + config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p5" + )(P5) + # P6 is used for the 5th anchor scale in RPN. Generated by + # subsampling from P5 with stride of 2. + P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5) + + # Note that P6 is used in RPN, but not in the classifier heads. + rpn_feature_maps = [P2, P3, P4, P5, P6] + mrcnn_feature_maps = [P2, P3, P4, P5] + + # Anchors + if mode == "training": + anchors = self.get_anchors(config.IMAGE_SHAPE) + # Duplicate across the batch dimension because Keras requires it + # TODO: can this be optimized to avoid duplicating the anchors? + anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape) + # A hack to get around Keras's bad support for constants + anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")( + input_image + ) + else: + anchors = input_anchors + + # RPN Model + rpn = build_rpn_model( + config.RPN_ANCHOR_STRIDE, + len(config.RPN_ANCHOR_RATIOS), + config.TOP_DOWN_PYRAMID_SIZE, + ) + # Loop through pyramid layers + layer_outputs = [] # list of lists + for p in rpn_feature_maps: + layer_outputs.append(rpn([p])) + # Concatenate layer outputs + # Convert from list of lists of level outputs to list of lists + # of outputs across levels. + # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]] + output_names = ["rpn_class_logits", "rpn_class", "rpn_bbox"] + outputs = list(zip(*layer_outputs)) + outputs = [ + KL.Concatenate(axis=1, name=n)(list(o)) + for o, n in zip(outputs, output_names) + ] + + rpn_class_logits, rpn_class, rpn_bbox = outputs + + # Generate proposals + # Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates + # and zero padded. + proposal_count = ( + config.POST_NMS_ROIS_TRAINING + if mode == "training" + else config.POST_NMS_ROIS_INFERENCE + ) + rpn_rois = ProposalLayer( + proposal_count=proposal_count, + nms_threshold=config.RPN_NMS_THRESHOLD, + name="ROI", + config=config, + )([rpn_class, rpn_bbox, anchors]) + + if mode == "training": + # Class ID mask to mark class IDs supported by the dataset the image + # came from. + active_class_ids = KL.Lambda( + lambda x: parse_image_meta_graph(x)["active_class_ids"] + )(input_image_meta) + + if not config.USE_RPN_ROIS: + # Ignore predicted ROIs and use ROIs provided as an input. + input_rois = KL.Input( + shape=[config.POST_NMS_ROIS_TRAINING, 4], + name="input_roi", + dtype=np.int32, + ) + # Normalize coordinates + target_rois = KL.Lambda( + lambda x: norm_boxes_graph(x, K.shape(input_image)[1:3]) + )(input_rois) + else: + target_rois = rpn_rois + + # Generate detection targets + # Subsamples proposals and generates target outputs for training + # Note that proposal class IDs, gt_boxes, and gt_masks are zero + # padded. Equally, returned rois and targets are zero padded. + rois, target_class_ids, target_bbox, target_mask = DetectionTargetLayer( + config, name="proposal_targets" + )([target_rois, input_gt_class_ids, gt_boxes, input_gt_masks]) + + # Network Heads + # TODO: verify that this handles zero padded ROIs + mrcnn_class_logits, mrcnn_class, mrcnn_bbox = fpn_classifier_graph( + rois, + mrcnn_feature_maps, + input_image_meta, + config.POOL_SIZE, + config.NUM_CLASSES, + train_bn=config.TRAIN_BN, + fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE, + ) + + mrcnn_mask = build_fpn_mask_graph( + rois, + mrcnn_feature_maps, + input_image_meta, + config.MASK_POOL_SIZE, + config.NUM_CLASSES, + train_bn=config.TRAIN_BN, + ) + + # TODO: clean up (use tf.identify if necessary) + output_rois = KL.Lambda(lambda x: x * 1, name="output_rois")(rois) + + # Losses + rpn_class_loss = KL.Lambda( + lambda x: rpn_class_loss_graph(*x), name="rpn_class_loss" + )([input_rpn_match, rpn_class_logits]) + rpn_bbox_loss = KL.Lambda( + lambda x: rpn_bbox_loss_graph(config, *x), name="rpn_bbox_loss" + )([input_rpn_bbox, input_rpn_match, rpn_bbox]) + class_loss = KL.Lambda( + lambda x: mrcnn_class_loss_graph(*x), name="mrcnn_class_loss" + )([target_class_ids, mrcnn_class_logits, active_class_ids]) + bbox_loss = KL.Lambda( + lambda x: mrcnn_bbox_loss_graph(*x), name="mrcnn_bbox_loss" + )([target_bbox, target_class_ids, mrcnn_bbox]) + mask_loss = KL.Lambda( + lambda x: mrcnn_mask_loss_graph(*x), name="mrcnn_mask_loss" + )([target_mask, target_class_ids, mrcnn_mask]) + + # Model + inputs = [ + input_image, + input_image_meta, + input_rpn_match, + input_rpn_bbox, + input_gt_class_ids, + input_gt_boxes, + 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 heads + mrcnn_class_logits, mrcnn_class, mrcnn_bbox = fpn_classifier_graph( + rpn_rois, + mrcnn_feature_maps, + input_image_meta, + config.POOL_SIZE, + config.NUM_CLASSES, + train_bn=config.TRAIN_BN, + fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE, + ) + + # Detections + # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in + # normalized coordinates + detections = DetectionLayer(config, name="mrcnn_detection")( + [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta] + ) + + # Create masks for detections + detection_boxes = KL.Lambda(lambda x: x[..., :4])(detections) + mrcnn_mask = build_fpn_mask_graph( + detection_boxes, + mrcnn_feature_maps, + input_image_meta, + config.MASK_POOL_SIZE, + config.NUM_CLASSES, + train_bn=config.TRAIN_BN, + ) + + model = KM.Model( + [input_image, input_image_meta, input_anchors], + [ + detections, + mrcnn_class, + mrcnn_bbox, + mrcnn_mask, + rpn_rois, + rpn_class, + rpn_bbox, + ], + name="mask_rcnn", + ) + + # Add multi-GPU support. + if config.GPU_COUNT > 1: + from mrcnn.parallel_model import ParallelModel + + model = ParallelModel(model, config.GPU_COUNT) + + return model + + def find_last(self): + """Finds the last checkpoint file of the last trained model in the + model directory. + Returns: + The path of the last checkpoint file + """ + # Get directory names. Each directory corresponds to a model + dir_names = next(os.walk(self.model_dir))[1] + key = self.config.NAME.lower() + dir_names = filter(lambda f: f.startswith(key), dir_names) + dir_names = sorted(dir_names) + if not dir_names: + import errno + + raise FileNotFoundError( + errno.ENOENT, + "Could not find model directory under {}".format(self.model_dir), + ) + # Pick last directory + dir_name = os.path.join(self.model_dir, dir_names[-1]) + # Find the last checkpoint + checkpoints = next(os.walk(dir_name))[2] + checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints) + checkpoints = sorted(checkpoints) + if not checkpoints: + import errno + + raise FileNotFoundError( + errno.ENOENT, "Could not find weight files in {}".format(dir_name) + ) + checkpoint = os.path.join(dir_name, checkpoints[-1]) + return checkpoint + + def load_weights(self, filepath, by_name=False, exclude=None): + """Modified version of the corresponding Keras function with + the addition of multi-GPU support and the ability to exclude + some layers from loading. + exclude: list of layer names to exclude + """ + import h5py + + # Conditional import to support versions of Keras before 2.2 + # TODO: remove in about 6 months (end of 2018) + try: + from keras.engine import saving + except ImportError: + # Keras before 2.2 used the 'topology' namespace. + from keras.engine import topology as saving + + if exclude: + by_name = True + + if h5py is None: + raise ImportError("`load_weights` requires h5py.") + f = h5py.File(filepath, mode="r") + if "layer_names" not in f.attrs and "model_weights" in f: + f = f["model_weights"] + + # In multi-GPU training, we wrap the model. Get layers + # of the inner model because they have the weights. + keras_model = self.keras_model + layers = ( + keras_model.inner_model.layers + if hasattr(keras_model, "inner_model") + else keras_model.layers + ) + + # Exclude some layers + if exclude: + layers = filter(lambda l: l.name not in exclude, layers) + + if by_name: + saving.load_weights_from_hdf5_group_by_name(f, layers) + else: + saving.load_weights_from_hdf5_group(f, layers) + if hasattr(f, "close"): + f.close() + + # Update the log directory + self.set_log_dir(filepath) + + def get_imagenet_weights(self): + """Downloads ImageNet trained weights from Keras. + Returns path to weights file. + """ + from keras.utils.data_utils import get_file + + TF_WEIGHTS_PATH_NO_TOP = ( + "https://github.com/fchollet/deep-learning-models/" + "releases/download/v0.2/" + "resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5" + ) + weights_path = get_file( + "resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5", + TF_WEIGHTS_PATH_NO_TOP, + cache_subdir="models", + md5_hash="a268eb855778b3df3c7506639542a6af", + ) + return weights_path + + def compile(self, learning_rate, momentum): + """Gets the model ready for training. Adds losses, regularization, and + metrics. Then calls the Keras compile() function. + """ + # Optimizer object + optimizer = keras.optimizers.SGD( + lr=learning_rate, momentum=momentum, clipnorm=self.config.GRADIENT_CLIP_NORM + ) + # Add Losses + # First, clear previously set losses to avoid duplication + self.keras_model._losses = [] + self.keras_model._per_input_losses = {} + 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: + continue + loss = tf.reduce_mean( + layer.output, keepdims=True + ) * self.config.LOSS_WEIGHTS.get(name, 1.0) + self.keras_model.add_loss(loss) + + # Add L2 Regularization + # Skip gamma and beta weights of batch normalization layers. + reg_losses = [ + keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) + / tf.cast(tf.size(w), tf.float32) + for w in self.keras_model.trainable_weights + if "gamma" not in w.name and "beta" not in w.name + ] + self.keras_model.add_loss(tf.add_n(reg_losses)) + + # Compile + self.keras_model.compile( + optimizer=optimizer, loss=[None] * len(self.keras_model.outputs) + ) + + # Add metrics for losses + for name in loss_names: + if name in self.keras_model.metrics_names: + continue + layer = self.keras_model.get_layer(name) + self.keras_model.metrics_names.append(name) + loss = tf.reduce_mean( + layer.output, keepdims=True + ) * self.config.LOSS_WEIGHTS.get(name, 1.0) + self.keras_model.metrics_tensors.append(loss) + + def set_trainable(self, layer_regex, keras_model=None, indent=0, verbose=1): + """Sets model layers as trainable if their names match + the given regular expression. + """ + # Print message on the first call (but not on recursive calls) + if verbose > 0 and keras_model is None: + log("Selecting layers to train") + + keras_model = keras_model or self.keras_model + + # In multi-GPU training, we wrap the model. Get layers + # of the inner model because they have the weights. + layers = ( + keras_model.inner_model.layers + if hasattr(keras_model, "inner_model") + else keras_model.layers + ) + + for layer in layers: + # Is the layer a model? + if layer.__class__.__name__ == "Model": + print("In model: ", layer.name) + self.set_trainable(layer_regex, keras_model=layer, indent=indent + 4) + continue + + if not layer.weights: + continue + # Is it trainable? + trainable = bool(re.fullmatch(layer_regex, layer.name)) + # Update layer. If layer is a container, update inner layer. + if layer.__class__.__name__ == "TimeDistributed": + layer.layer.trainable = trainable + else: + layer.trainable = trainable + # Print trainable layer names + if trainable and verbose > 0: + log( + "{}{:20} ({})".format( + " " * indent, layer.name, layer.__class__.__name__ + ) + ) + + def set_log_dir(self, model_path=None): + """Sets the model log directory and epoch counter. + + model_path: If None, or a format different from what this code uses + then set a new log directory and start epochs from 0. Otherwise, + extract the log directory and the epoch counter from the file + name. + """ + # Set date and epoch counter as if starting a new model + self.epoch = 0 + now = datetime.datetime.now() + + # If we have a model path with date and epochs use them + if model_path: + # Continue from we left of. Get epoch and date from the file name + # A sample model path might look like: + # \path\to\logs\coco20171029T2315\mask_rcnn_coco_0001.h5 (Windows) + # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5 (Linux) + regex = r".*[/\\][\w-]+(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})[/\\]mask\_rcnn\_[\w-]+(\d{4})\.h5" + m = re.match(regex, model_path) + if m: + now = datetime.datetime( + int(m.group(1)), + int(m.group(2)), + int(m.group(3)), + int(m.group(4)), + int(m.group(5)), + ) + # Epoch number in file is 1-based, and in Keras code it's 0-based. + # So, adjust for that then increment by one to start from the next epoch + self.epoch = int(m.group(6)) - 1 + 1 + print("Re-starting from epoch %d" % self.epoch) + + # Directory for training logs + self.log_dir = os.path.join( + self.model_dir, "{}{:%Y%m%dT%H%M}".format(self.config.NAME.lower(), now) + ) + + # Path to save after each epoch. Include placeholders that get filled by Keras. + self.checkpoint_path = os.path.join( + self.log_dir, "mask_rcnn_{}_*epoch*.h5".format(self.config.NAME.lower()) + ) + self.checkpoint_path = self.checkpoint_path.replace("*epoch*", "{epoch:04d}") + + def train( + self, + train_dataset, + val_dataset, + learning_rate, + epochs, + layers, + augmentation=None, + custom_callbacks=None, + no_augmentation_sources=None, + ): + """Train the model. + train_dataset, val_dataset: Training and validation Dataset objects. + learning_rate: The learning rate to train with + epochs: Number of training epochs. Note that previous training epochs + are considered to be done alreay, so this actually determines + the epochs to train in total rather than in this particaular + call. + layers: Allows selecting wich layers to train. It can be: + - A regular expression to match layer names to train + - One of these predefined values: + heads: The RPN, classifier and mask heads of the network + all: All the layers + 3+: Train Resnet stage 3 and up + 4+: Train Resnet stage 4 and up + 5+: Train Resnet stage 5 and up + augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) + augmentation. For example, passing imgaug.augmenters.Fliplr(0.5) + flips images right/left 50% of the time. You can pass complex + augmentations as well. This augmentation applies 50% of the + time, and when it does it flips images right/left half the time + and adds a Gaussian blur with a random sigma in range 0 to 5. + + augmentation = imgaug.augmenters.Sometimes(0.5, [ + imgaug.augmenters.Fliplr(0.5), + imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0)) + ]) + custom_callbacks: Optional. Add custom callbacks to be called + with the keras fit_generator method. Must be list of type keras.callbacks. + no_augmentation_sources: Optional. List of sources to exclude for + augmentation. A source is string that identifies a dataset and is + defined in the Dataset class. + """ + assert self.mode == "training", "Create model in training mode." + + # Pre-defined layer regular expressions + layer_regex = { + # all layers but the backbone + "heads": r"(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", + # From a specific Resnet stage and up + "3+": r"(res3.*)|(bn3.*)|(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", + "4+": r"(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", + "5+": r"(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", + # All layers + "all": ".*", + } + if layers in layer_regex.keys(): + layers = layer_regex[layers] + + # Data generators + train_generator = data_generator( + train_dataset, + self.config, + shuffle=True, + augmentation=augmentation, + batch_size=self.config.BATCH_SIZE, + no_augmentation_sources=no_augmentation_sources, + ) + val_generator = data_generator( + val_dataset, self.config, shuffle=True, batch_size=self.config.BATCH_SIZE + ) + + # Create log_dir if it does not exist + if not os.path.exists(self.log_dir): + os.makedirs(self.log_dir) + + # Callbacks + callbacks = [ + keras.callbacks.TensorBoard( + log_dir=self.log_dir, + histogram_freq=0, + write_graph=True, + write_images=False, + ), + keras.callbacks.ModelCheckpoint( + self.checkpoint_path, verbose=0, save_weights_only=True + ), + ] + + # Add custom callbacks to the list + if custom_callbacks: + callbacks += custom_callbacks + + # Train + log("\nStarting at epoch {}. LR={}\n".format(self.epoch, learning_rate)) + log("Checkpoint Path: {}".format(self.checkpoint_path)) + self.set_trainable(layers) + self.compile(learning_rate, self.config.LEARNING_MOMENTUM) + + # Work-around for Windows: Keras fails on Windows when using + # multiprocessing workers. See discussion here: + # https://github.com/matterport/Mask_RCNN/issues/13#issuecomment-353124009 + if os.name is "nt": + workers = 0 + else: + 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 expected + as an input to the neural network. + images: List of image matrices [height,width,depth]. Images can have + different sizes. + + Returns 3 Numpy matrices: + molded_images: [N, h, w, 3]. Images resized and normalized. + image_metas: [N, length of meta data]. Details about each image. + windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the + original image (padding excluded). + """ + molded_images = [] + image_metas = [] + windows = [] + for image in images: + # Resize image + # TODO: move resizing to mold_image() + molded_image, window, scale, padding, crop = utils.resize_image( + image, + min_dim=self.config.IMAGE_MIN_DIM, + min_scale=self.config.IMAGE_MIN_SCALE, + max_dim=self.config.IMAGE_MAX_DIM, + mode=self.config.IMAGE_RESIZE_MODE, + ) + molded_image = mold_image(molded_image, self.config) + # Build image_meta + image_meta = compose_image_meta( + 0, + image.shape, + molded_image.shape, + window, + scale, + np.zeros([self.config.NUM_CLASSES], dtype=np.int32), + ) + # Append + molded_images.append(molded_image) + windows.append(window) + image_metas.append(image_meta) + # Pack into arrays + molded_images = np.stack(molded_images) + image_metas = np.stack(image_metas) + windows = np.stack(windows) + return molded_images, image_metas, windows + + def unmold_detections( + self, detections, mrcnn_mask, original_image_shape, image_shape, window + ): + """Reformats the detections of one image from the format of the neural + network output to a format suitable for use in the rest of the + application. + + detections: [N, (y1, x1, y2, x2, class_id, score)] in normalized coordinates + mrcnn_mask: [N, height, width, num_classes] + original_image_shape: [H, W, C] Original image shape before resizing + image_shape: [H, W, C] Shape of the image after resizing and padding + window: [y1, x1, y2, x2] Pixel coordinates of box in the image where the real + image is excluding the padding. + + Returns: + boxes: [N, (y1, x1, y2, x2)] Bounding boxes in pixels + class_ids: [N] Integer class IDs for each bounding box + scores: [N] Float probability scores of the class_id + masks: [height, width, num_instances] Instance masks + """ + # How many detections do we have? + # Detections array is padded with zeros. Find the first class_id == 0. + zero_ix = np.where(detections[:, 4] == 0)[0] + N = zero_ix[0] if zero_ix.shape[0] > 0 else detections.shape[0] + + # Extract boxes, class_ids, scores, and class-specific masks + boxes = detections[:N, :4] + class_ids = detections[:N, 4].astype(np.int32) + scores = detections[:N, 5] + masks = mrcnn_mask[np.arange(N), :, :, class_ids] + + # Translate normalized coordinates in the resized image to pixel + # coordinates in the original image before resizing + window = utils.norm_boxes(window, image_shape[:2]) + wy1, wx1, wy2, wx2 = window + shift = np.array([wy1, wx1, wy1, wx1]) + wh = wy2 - wy1 # window height + ww = wx2 - wx1 # window width + scale = np.array([wh, ww, wh, ww]) + # Convert boxes to normalized coordinates on the window + boxes = np.divide(boxes - shift, scale) + # Convert boxes to pixel coordinates on the original image + boxes = utils.denorm_boxes(boxes, original_image_shape[:2]) + + # Filter out detections with zero area. Happens in early training when + # network weights are still random + exclude_ix = np.where( + (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0 + )[0] + if exclude_ix.shape[0] > 0: + boxes = np.delete(boxes, exclude_ix, axis=0) + class_ids = np.delete(class_ids, exclude_ix, axis=0) + scores = np.delete(scores, exclude_ix, axis=0) + 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 mask + full_mask = utils.unmold_mask(masks[i], boxes[i], original_image_shape) + full_masks.append(full_mask) + full_masks = ( + np.stack(full_masks, axis=-1) + if full_masks + else np.empty(original_image_shape[:2] + (0,)) + ) + + return boxes, class_ids, scores, full_masks + + def 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 boxes + class_ids: [N] int class IDs + scores: [N] float probability scores for the class IDs + masks: [H, W, N] instance binary masks + """ + assert self.mode == "inference", "Create model in inference mode." + assert ( + len(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 network + molded_images, image_metas, windows = self.mold_inputs(images) + + # Validate image sizes + # All images in a batch MUST be of the same size + image_shape = molded_images[0].shape + for g in molded_images[1:]: + assert ( + g.shape == image_shape + ), "After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes." + + # Anchors + anchors = self.get_anchors(image_shape) + # Duplicate across the batch dimension because Keras requires it + # TODO: can this be optimized to avoid duplicating the anchors? + anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) + + if verbose: + log("molded_images", molded_images) + log("image_metas", image_metas) + log("anchors", anchors) + # Run object detection + detections, _, _, mrcnn_mask, _, _, _ = self.keras_model.predict( + [molded_images, image_metas, anchors], verbose=0 + ) + # Process detections + results = [] + for i, image in enumerate(images): + ( + final_rois, + final_class_ids, + final_scores, + final_masks, + ) = self.unmold_detections( + detections[i], + mrcnn_mask[i], + image.shape, + molded_images[i].shape, + windows[i], + ) + results.append( + { + "rois": final_rois, + "class_ids": final_class_ids, + "scores": final_scores, + "masks": final_masks, + } + ) + return results + + def detect_molded(self, molded_images, image_metas, verbose=0): + """Runs the detection pipeline, but expect inputs that are + molded already. Used mostly for debugging and inspecting + the model. + + molded_images: List of images loaded using load_image_gt() + image_metas: image meta data, also returned by load_image_gt() + + Returns a list of dicts, one dict per image. The dict contains: + rois: [N, (y1, x1, y2, x2)] detection bounding boxes + class_ids: [N] int class IDs + scores: [N] float probability scores for the class IDs + masks: [H, W, N] instance binary masks + """ + assert self.mode == "inference", "Create model in inference mode." + assert ( + len(molded_images) == self.config.BATCH_SIZE + ), "Number of images must be equal to BATCH_SIZE" + + if verbose: + log("Processing {} images".format(len(molded_images))) + for image in molded_images: + log("image", image) + + # Validate image sizes + # All images in a batch MUST be of the same size + image_shape = molded_images[0].shape + for g in molded_images[1:]: + assert g.shape == image_shape, "Images must have the same size" + + # Anchors + anchors = self.get_anchors(image_shape) + # Duplicate across the batch dimension because Keras requires it + # TODO: can this be optimized to avoid duplicating the anchors? + anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) + + if verbose: + log("molded_images", molded_images) + log("image_metas", image_metas) + log("anchors", anchors) + # Run object detection + detections, _, _, mrcnn_mask, _, _, _ = self.keras_model.predict( + [molded_images, image_metas, anchors], verbose=0 + ) + # Process detections + results = [] + for i, image in enumerate(molded_images): + window = [0, 0, image.shape[0], image.shape[1]] + ( + final_rois, + final_class_ids, + final_scores, + final_masks, + ) = self.unmold_detections( + detections[i], + mrcnn_mask[i], + image.shape, + molded_images[i].shape, + window, + ) + results.append( + { + "rois": final_rois, + "class_ids": final_class_ids, + "scores": final_scores, + "masks": final_masks, + } + ) + return results + + def get_anchors(self, image_shape): + """Returns anchor pyramid for the given image size.""" + backbone_shapes = compute_backbone_shapes(self.config, image_shape) + # Cache anchors and reuse if image shape is the same + if not hasattr(self, "_anchor_cache"): + self._anchor_cache = {} + if not tuple(image_shape) in self._anchor_cache: + # Generate Anchors + a = utils.generate_pyramid_anchors( + self.config.RPN_ANCHOR_SCALES, + self.config.RPN_ANCHOR_RATIOS, + backbone_shapes, + self.config.BACKBONE_STRIDES, + self.config.RPN_ANCHOR_STRIDE, + ) + # Keep a copy of the latest anchors in pixel coordinates because + # it's used in inspect_model notebooks. + # TODO: Remove this after the notebook are refactored to not use it + self.anchors = a + # Normalize coordinates + self._anchor_cache[tuple(image_shape)] = utils.norm_boxes( + a, image_shape[:2] + ) + return self._anchor_cache[tuple(image_shape)] + + def ancestor(self, tensor, name, checked=None): + """Finds the ancestor of a TF tensor in the computation graph. + tensor: TensorFlow symbolic tensor. + name: Name of ancestor tensor to find + checked: For internal use. A list of tensors that were already + searched to avoid loops in traversing the graph. + """ + checked = checked if checked is not None else [] + # Put a limit on how deep we go to avoid very long loops + if len(checked) > 500: + return None + # Convert name to a regex and allow matching a number prefix + # because Keras adds them automatically + if isinstance(name, str): + name = re.compile(name.replace("/", r"(\_\d+)*/")) + + parents = tensor.op.inputs + for p in parents: + if p in checked: + continue + if bool(re.fullmatch(name, p.name)): + return p + checked.append(p) + a = self.ancestor(p, name, checked) + if a is not None: + return a + return None + + def find_trainable_layer(self, layer): + """If a layer is encapsulated by another layer, this function + digs through the encapsulation and returns the layer that holds + the weights. + """ + if layer.__class__.__name__ == "TimeDistributed": + return self.find_trainable_layer(layer.layer) + return layer + + def get_trainable_layers(self): + """Returns a list of layers that have weights.""" + layers = [] + # Loop through all layers + for l in self.keras_model.layers: + # If layer is a wrapper, find inner trainable layer + l = self.find_trainable_layer(l) + # Include layer if it has weights + if l.get_weights(): + layers.append(l) + return layers + + def run_graph(self, images, outputs, image_metas=None): + """Runs a sub-set of the computation graph that computes the given + outputs. + + image_metas: If provided, the images are assumed to be already + molded (i.e. resized, padded, and normalized) + + outputs: List of tuples (name, tensor) to compute. The tensors are + symbolic TensorFlow tensors and the names are for easy tracking. + + Returns an ordered dict of results. Keys are the names received in the + input and values are Numpy arrays. + """ + model = self.keras_model + + # Organize desired outputs into an ordered dict + outputs = OrderedDict(outputs) + for o in outputs.values(): + assert o is not None + + # Build a Keras function to run parts of the computation graph + inputs = model.inputs + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + inputs += [K.learning_phase()] + kf = K.function(model.inputs, list(outputs.values())) + + # Prepare inputs + if image_metas is None: + molded_images, image_metas, _ = self.mold_inputs(images) + else: + molded_images = images + image_shape = molded_images[0].shape + # Anchors + anchors = self.get_anchors(image_shape) + # Duplicate across the batch dimension because Keras requires it + # TODO: can this be optimized to avoid duplicating the anchors? + anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) + model_in = [molded_images, image_metas, anchors] + + # Run inference + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + model_in.append(0.0) + outputs_np = kf(model_in) + + # Pack the generated Numpy arrays into a a dict and log the results. + outputs_np = OrderedDict([(k, v) for k, v in zip(outputs.keys(), outputs_np)]) + for k, v in outputs_np.items(): + log(k, v) + return outputs_np + + +############################################################ +# Data Formatting +############################################################ + + +def compose_image_meta( + image_id, original_image_shape, image_shape, window, scale, active_class_ids +): + """Takes attributes of an image and puts them in one 1D array. + + image_id: An int ID of the image. Useful for debugging. + original_image_shape: [H, W, C] before resizing or padding. + image_shape: [H, W, C] after resizing and padding + window: (y1, x1, y2, x2) in pixels. The area of the image where the real + image is (excluding the padding) + scale: The scaling factor applied to the original image (float32) + active_class_ids: List of class_ids available in the dataset from which + the image came. Useful if training on images from multiple datasets + where not all classes are present in all datasets. + """ + meta = np.array( + [image_id] + + list(original_image_shape) # size=1 + + list(image_shape) # size=3 + + list(window) # size=3 + + [scale] # size=4 (y1, x1, y2, x2) in image cooredinates + + list(active_class_ids) # size=1 # size=num_classes + ) + return meta + + +def parse_image_meta(meta): + """Parses an array that contains image attributes to its components. + See compose_image_meta() for more details. + + meta: [batch, meta length] where meta length depends on NUM_CLASSES + + Returns a dict of the parsed values. + """ + image_id = meta[:, 0] + original_image_shape = meta[:, 1:4] + image_shape = meta[:, 4:7] + window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels + scale = meta[:, 11] + active_class_ids = meta[:, 12:] + return { + "image_id": image_id.astype(np.int32), + "original_image_shape": original_image_shape.astype(np.int32), + "image_shape": image_shape.astype(np.int32), + "window": window.astype(np.int32), + "scale": scale.astype(np.float32), + "active_class_ids": active_class_ids.astype(np.int32), + } + + +def parse_image_meta_graph(meta): + """Parses a tensor that contains image attributes to its components. + See compose_image_meta() for more details. + + meta: [batch, meta length] where meta length depends on NUM_CLASSES + + Returns a dict of the parsed tensors. + """ + image_id = meta[:, 0] + original_image_shape = meta[:, 1:4] + image_shape = meta[:, 4:7] + window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels + scale = meta[:, 11] + active_class_ids = meta[:, 12:] + return { + "image_id": image_id, + "original_image_shape": original_image_shape, + "image_shape": image_shape, + "window": window, + "scale": scale, + "active_class_ids": active_class_ids, + } + + +def mold_image(images, config): + """Expects an RGB image (or array of images) and subtracts + the mean pixel and converts it to float. Expects image + colors in RGB order. + """ + return images.astype(np.float32) - config.MEAN_PIXEL + + +def unmold_image(normalized_images, config): + """Takes a image normalized with mold() and returns the original.""" + return (normalized_images + config.MEAN_PIXEL).astype(np.uint8) + + +############################################################ +# Miscellenous Graph Functions +############################################################ + + +def trim_zeros_graph(boxes, name="trim_zeros"): + """Often boxes are represented with matrices of shape [N, 4] and + are padded with zeros. This removes zero boxes. + + boxes: [N, 4] matrix of boxes. + non_zeros: [N] a 1D boolean mask identifying the rows to keep + """ + non_zeros = tf.cast(tf.reduce_sum(tf.abs(boxes), axis=1), tf.bool) + boxes = tf.boolean_mask(boxes, non_zeros, name=name) + return boxes, non_zeros + + +def batch_pack_graph(x, counts, num_rows): + """Picks different number of values from each row + in x depending on the values in counts. + """ + outputs = [] + for i in range(num_rows): + outputs.append(x[i, : counts[i]]) + return tf.concat(outputs, axis=0) + + +def norm_boxes_graph(boxes, shape): + """Converts boxes from pixel coordinates to normalized coordinates. + boxes: [..., (y1, x1, y2, x2)] in pixel coordinates + shape: [..., (height, width)] in pixels + + Note: In pixel coordinates (y2, x2) is outside the box. But in normalized + coordinates it's inside the box. + + Returns: + [..., (y1, x1, y2, x2)] in normalized coordinates + """ + h, w = tf.split(tf.cast(shape, tf.float32), 2) + scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0) + shift = tf.constant([0.0, 0.0, 1.0, 1.0]) + return tf.divide(boxes - shift, scale) + + +def denorm_boxes_graph(boxes, shape): + """Converts boxes from normalized coordinates to pixel coordinates. + boxes: [..., (y1, x1, y2, x2)] in normalized coordinates + shape: [..., (height, width)] in pixels + + Note: In pixel coordinates (y2, x2) is outside the box. But in normalized + coordinates it's inside the box. + + Returns: + [..., (y1, x1, y2, x2)] in pixel coordinates + """ + h, w = tf.split(tf.cast(shape, tf.float32), 2) + scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0) + shift = tf.constant([0.0, 0.0, 1.0, 1.0]) + return tf.cast(tf.round(tf.multiply(boxes, scale) + shift), tf.int32)