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- """
- Mask R-CNN
- The main Mask R-CNN model implementation.
- Copyright (c) 2017 Matterport, Inc.
- Licensed under the MIT License (see LICENSE for details)
- Written by Waleed Abdulla
- """
- import os
- import random
- import datetime
- import re
- import math
- import logging
- from collections import OrderedDict
- import multiprocessing
- import numpy as np
- import tensorflow as tf
- import tensorflow.keras
- import tensorflow.keras.backend as K
- import tensorflow.keras.layers as KL
- import tensorflow.keras.layers as KE
- import tensorflow.keras.models as KM
- import tensorflow.keras.utils as KU
- from tensorflow.keras.callbacks import CSVLogger
- from mrcnn import utils
- # Requires TensorFlow 1.3+ and Keras 2.0.8+.
- from distutils.version import LooseVersion
- from distutils.version import LooseVersion
- assert LooseVersion(tf.__version__) >= LooseVersion("1.3")
- #assert LooseVersion(keras.__version__) >= LooseVersion('2.0.8')
- ############################################################
- # Utility Functions
- ############################################################
- def log(text, array=None):
- """Prints a text message. And, optionally, if a Numpy array is provided it
- prints it's shape, min, and max values.
- """
- if array is not None:
- text = text.ljust(25)
- text += ("shape: {:20} ".format(str(array.shape)))
- if array.size:
- text += ("min: {:10.5f} max: {:10.5f}".format(array.min(),array.max()))
- else:
- text += ("min: {:10} max: {:10}".format("",""))
- text += " {}".format(array.dtype)
- print(text)
- class BatchNorm(KL.BatchNormalization):
- """Extends the Keras BatchNormalization class to allow a central place
- to make changes if needed.
- Batch normalization has a negative effect on training if batches are small
- so this layer is often frozen (via setting in Config class) and functions
- as linear layer.
- """
- def call(self, inputs, training=None):
- """
- Note about training values:
- None: Train BN layers. This is the normal mode
- False: Freeze BN layers. Good when batch size is small
- True: (don't use). Set layer in training mode even when making inferences
- """
- return super(self.__class__, self).call(inputs, training=training)
- def compute_backbone_shapes(config, image_shape):
- """Computes the width and height of each stage of the backbone network.
- Returns:
- [N, (height, width)]. Where N is the number of stages
- """
- if callable(config.BACKBONE):
- return config.COMPUTE_BACKBONE_SHAPE(image_shape)
- # Currently supports ResNet only
- assert config.BACKBONE in ["resnet50", "resnet101"]
- return np.array(
- [[int(math.ceil(image_shape[0] / stride)),
- int(math.ceil(image_shape[1] / stride))]
- for stride in config.BACKBONE_STRIDES])
- ############################################################
- # Resnet Graph
- ############################################################
- # Code adopted from:
- # https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
- def identity_block(input_tensor, kernel_size, filters, stage, block,
- use_bias=True, train_bn=True):
- """The identity_block is the block that has no conv layer at shortcut
- # Arguments
- input_tensor: input tensor
- kernel_size: default 3, the kernel size of middle conv layer at main path
- filters: list of integers, the nb_filters of 3 conv layer at main path
- stage: integer, current stage label, used for generating layer names
- block: 'a','b'..., current block label, used for generating layer names
- use_bias: Boolean. To use or not use a bias in conv layers.
- train_bn: Boolean. Train or freeze Batch Norm layers
- """
- nb_filter1, nb_filter2, nb_filter3 = filters
- conv_name_base = 'res' + str(stage) + block + '_branch'
- bn_name_base = 'bn' + str(stage) + block + '_branch'
- x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',
- use_bias=use_bias)(input_tensor)
- x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
- x = KL.Activation('relu')(x)
- x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
- name=conv_name_base + '2b', use_bias=use_bias)(x)
- x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
- x = KL.Activation('relu')(x)
- x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c',
- use_bias=use_bias)(x)
- x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
- x = KL.Add()([x, input_tensor])
- x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
- return x
- def conv_block(input_tensor, kernel_size, filters, stage, block,
- strides=(2, 2), use_bias=True, train_bn=True):
- """conv_block is the block that has a conv layer at shortcut
- # Arguments
- input_tensor: input tensor
- kernel_size: default 3, the kernel size of middle conv layer at main path
- filters: list of integers, the nb_filters of 3 conv layer at main path
- stage: integer, current stage label, used for generating layer names
- block: 'a','b'..., current block label, used for generating layer names
- use_bias: Boolean. To use or not use a bias in conv layers.
- train_bn: Boolean. Train or freeze Batch Norm layers
- Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
- And the shortcut should have subsample=(2,2) as well
- """
- nb_filter1, nb_filter2, nb_filter3 = filters
- conv_name_base = 'res' + str(stage) + block + '_branch'
- bn_name_base = 'bn' + str(stage) + block + '_branch'
- x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,
- name=conv_name_base + '2a', use_bias=use_bias)(input_tensor)
- x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn)
- x = KL.Activation('relu')(x)
- x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
- name=conv_name_base + '2b', use_bias=use_bias)(x)
- x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn)
- x = KL.Activation('relu')(x)
- x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
- '2c', use_bias=use_bias)(x)
- x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn)
- shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,
- name=conv_name_base + '1', use_bias=use_bias)(input_tensor)
- shortcut = BatchNorm(name=bn_name_base + '1')(shortcut, training=train_bn)
- x = KL.Add()([x, shortcut])
- x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)
- return x
- def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
- """Build a ResNet graph.
- architecture: Can be resnet50 or resnet101
- stage5: Boolean. If False, stage5 of the network is not created
- train_bn: Boolean. Train or freeze Batch Norm layers
- """
- assert architecture in ["resnet50", "resnet101"]
- # Stage 1
- x = KL.ZeroPadding2D((3, 3))(input_image)
- x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
- x = BatchNorm(name='bn_conv1')(x, training=train_bn)
- x = KL.Activation('relu')(x)
- C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
- # Stage 2
- x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn)
- x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn)
- C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn)
- # Stage 3
- x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn)
- x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn)
- x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn)
- C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn)
- # Stage 4
- x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn)
- block_count = {"resnet50": 5, "resnet101": 22}[architecture]
- for i in range(block_count):
- x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn)
- C4 = x
- # Stage 5
- if stage5:
- x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn)
- x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn)
- C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn)
- else:
- C5 = None
- return [C1, C2, C3, C4, C5]
- ############################################################
- # Proposal Layer
- ############################################################
- def apply_box_deltas_graph(boxes, deltas):
- """Applies the given deltas to the given boxes.
- boxes: [N, (y1, x1, y2, x2)] boxes to update
- deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply
- """
- # Convert to y, x, h, w
- height = boxes[:, 2] - boxes[:, 0]
- width = boxes[:, 3] - boxes[:, 1]
- center_y = boxes[:, 0] + 0.5 * height
- center_x = boxes[:, 1] + 0.5 * width
- # Apply deltas
- center_y += deltas[:, 0] * height
- center_x += deltas[:, 1] * width
- height *= tf.exp(deltas[:, 2])
- width *= tf.exp(deltas[:, 3])
- # Convert back to y1, x1, y2, x2
- y1 = center_y - 0.5 * height
- x1 = center_x - 0.5 * width
- y2 = y1 + height
- x2 = x1 + width
- result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")
- return result
- def clip_boxes_graph(boxes, window):
- """
- boxes: [N, (y1, x1, y2, x2)]
- window: [4] in the form y1, x1, y2, x2
- """
- # Split
- wy1, wx1, wy2, wx2 = tf.split(window, 4)
- y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)
- # Clip
- y1 = tf.maximum(tf.minimum(y1, wy2), wy1)
- x1 = tf.maximum(tf.minimum(x1, wx2), wx1)
- y2 = tf.maximum(tf.minimum(y2, wy2), wy1)
- x2 = tf.maximum(tf.minimum(x2, wx2), wx1)
- clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes")
- clipped.set_shape((clipped.shape[0], 4))
- return clipped
- class ProposalLayer(KE.Layer):
- """Receives anchor scores and selects a subset to pass as proposals
- to the second stage. Filtering is done based on anchor scores and
- non-max suppression to remove overlaps. It also applies bounding
- box refinement deltas to anchors.
- Inputs:
- rpn_probs: [batch, num_anchors, (bg prob, fg prob)]
- rpn_bbox: [batch, num_anchors, (dy, dx, log(dh), log(dw))]
- anchors: [batch, num_anchors, (y1, x1, y2, x2)] anchors in normalized coordinates
- Returns:
- Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)]
- """
- def __init__(self, proposal_count, nms_threshold, config=None, **kwargs):
- super(ProposalLayer, self).__init__(**kwargs)
- self.config = config
- self.proposal_count = proposal_count
- self.nms_threshold = nms_threshold
- def get_config(self):
- config = super(ProposalLayer, self).get_config()
- config["config"] = self.config.to_dict()
- config["proposal_count"] = self.proposal_count
- config["nms_threshold"] = self.nms_threshold
- return config
- 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 get_config(self):
- config = super(DetectionLayer, self).get_config()
- config["config"] = self.config.to_dict()
- return 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])
- [self.config.IMAGES_PER_GPU, 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)
- if s[1] is None:
- mrcnn_bbox = KL.Reshape((-1, num_classes, 4), name="mrcnn_bbox")(x)
- else:
- 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)
- anchors = tf.constant(anchors, name="anchors")
- 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 tensorflow.keras.engine import saving
- except ImportError:
- # Keras before 2.2 used the 'topology' namespace.
- from tensorflow.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 tensorflow.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 = tensorflow.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.))
- self.keras_model.add_loss(loss)
- # Add L2 Regularization
- # Skip gamma and beta weights of batch normalization layers.
- reg_losses = [
- tensnsorflow.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.))
- 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, best_only=False):
- """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.
- best_only: if True will only save the best model as defined by the minimum validation loss. Defualt is False.
- output_save_path: Where to save the epoch history as a CSV
- """
- 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)
- if best_only:
- log("Saving best model only...")
- # Callbacks
- callbacks = [
- tensorsorflow.keras.callbacks.TensorBoard(log_dir=self.log_dir,
- histogram_freq=0, write_graph=True, write_images=False),
- tensorflow.keras.callbacks.ModelCheckpoint(self.checkpoint_path,
- verbose=0, save_weights_only=True, monitor="val_loss", save_best_only=True, mode="min"),
- ]
- else:
- log("Saving all model epochs...")
- # Callbacks
- callbacks = [
- tensorflow.keras.callbacks.TensorBoard(log_dir=self.log_dir,
- histogram_freq=0, write_graph=True, write_images=False),
- tensororflow.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.)
- outputs_np = kf(model_in)
- # Pack the generated Numpy arrays into a a dict and log the results.
- outputs_np = OrderedDict([(k, v)
- for k, v in zip(outputs.keys(), outputs_np)])
- for k, v in outputs_np.items():
- log(k, v)
- return outputs_np
- ############################################################
- # Data Formatting
- ############################################################
- def compose_image_meta(image_id, original_image_shape, image_shape,
- window, scale, active_class_ids):
- """Takes attributes of an image and puts them in one 1D array.
- image_id: An int ID of the image. Useful for debugging.
- original_image_shape: [H, W, C] before resizing or padding.
- image_shape: [H, W, C] after resizing and padding
- window: (y1, x1, y2, x2) in pixels. The area of the image where the real
- image is (excluding the padding)
- scale: The scaling factor applied to the original image (float32)
- active_class_ids: List of class_ids available in the dataset from which
- the image came. Useful if training on images from multiple datasets
- where not all classes are present in all datasets.
- """
- meta = np.array(
- [image_id] + # size=1
- list(original_image_shape) + # size=3
- list(image_shape) + # size=3
- list(window) + # size=4 (y1, x1, y2, x2) in image cooredinates
- [scale] + # size=1
- list(active_class_ids) # size=num_classes
- )
- return meta
- def parse_image_meta(meta):
- """Parses an array that contains image attributes to its components.
- See compose_image_meta() for more details.
- meta: [batch, meta length] where meta length depends on NUM_CLASSES
- Returns a dict of the parsed values.
- """
- image_id = meta[:, 0]
- original_image_shape = meta[:, 1:4]
- image_shape = meta[:, 4:7]
- window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels
- scale = meta[:, 11]
- active_class_ids = meta[:, 12:]
- return {
- "image_id": image_id.astype(np.int32),
- "original_image_shape": original_image_shape.astype(np.int32),
- "image_shape": image_shape.astype(np.int32),
- "window": window.astype(np.int32),
- "scale": scale.astype(np.float32),
- "active_class_ids": active_class_ids.astype(np.int32),
- }
- def parse_image_meta_graph(meta):
- """Parses a tensor that contains image attributes to its components.
- See compose_image_meta() for more details.
- meta: [batch, meta length] where meta length depends on NUM_CLASSES
- Returns a dict of the parsed tensors.
- """
- image_id = meta[:, 0]
- original_image_shape = meta[:, 1:4]
- image_shape = meta[:, 4:7]
- window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels
- scale = meta[:, 11]
- active_class_ids = meta[:, 12:]
- return {
- "image_id": image_id,
- "original_image_shape": original_image_shape,
- "image_shape": image_shape,
- "window": window,
- "scale": scale,
- "active_class_ids": active_class_ids,
- }
- def mold_image(images, config):
- """Expects an RGB image (or array of images) and subtracts
- the mean pixel and converts it to float. Expects image
- colors in RGB order.
- """
- return images.astype(np.float32) - config.MEAN_PIXEL
- def unmold_image(normalized_images, config):
- """Takes a image normalized with mold() and returns the original."""
- return (normalized_images + config.MEAN_PIXEL).astype(np.uint8)
- ############################################################
- # Miscellenous Graph Functions
- ############################################################
- def trim_zeros_graph(boxes, name='trim_zeros'):
- """Often boxes are represented with matrices of shape [N, 4] and
- are padded with zeros. This removes zero boxes.
- boxes: [N, 4] matrix of boxes.
- non_zeros: [N] a 1D boolean mask identifying the rows to keep
- """
- non_zeros = tf.cast(tf.reduce_sum(tf.abs(boxes), axis=1), tf.bool)
- boxes = tf.boolean_mask(boxes, non_zeros, name=name)
- return boxes, non_zeros
- def batch_pack_graph(x, counts, num_rows):
- """Picks different number of values from each row
- in x depending on the values in counts.
- """
- outputs = []
- for i in range(num_rows):
- outputs.append(x[i, :counts[i]])
- return tf.concat(outputs, axis=0)
- def norm_boxes_graph(boxes, shape):
- """Converts boxes from pixel coordinates to normalized coordinates.
- boxes: [..., (y1, x1, y2, x2)] in pixel coordinates
- shape: [..., (height, width)] in pixels
- Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
- coordinates it's inside the box.
- Returns:
- [..., (y1, x1, y2, x2)] in normalized coordinates
- """
- h, w = tf.split(tf.cast(shape, tf.float32), 2)
- scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0)
- shift = tf.constant([0., 0., 1., 1.])
- return tf.divide(boxes - shift, scale)
- def denorm_boxes_graph(boxes, shape):
- """Converts boxes from normalized coordinates to pixel coordinates.
- boxes: [..., (y1, x1, y2, x2)] in normalized coordinates
- shape: [..., (height, width)] in pixels
- Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
- coordinates it's inside the box.
- Returns:
- [..., (y1, x1, y2, x2)] in pixel coordinates
- """
- h, w = tf.split(tf.cast(shape, tf.float32), 2)
- scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0)
- shift = tf.constant([0., 0., 1., 1.])
- return tf.cast(tf.round(tf.multiply(boxes, scale) + shift), tf.int32)
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