『计算机视觉』Mask-RCNN_推断网络其六：Mask生成
2024-09-30 06:07:29
一、Mask生成概览
上一节的末尾，我们已经获取了待检测图片的分类回归信息，我们将回归信息（即待检测目标的边框信息）单独提取出来，结合金字塔特征mrcnn_feature_maps，进行Mask生成工作（input_image_meta用于提取输入图片长宽，进行金字塔ROI处理，即PyramidROIAlign）。
# 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)
二、Mask生成函数
我们在『计算机视觉』Mask-RCNN_推断网络其四：FPN和ROIAlign的耦合已经介绍过了PyramidROIAlign class的内容，
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
PyramidROIAlign之后（这里会降采样一次），最终生成众多宽高等同输入ROI feat，但是是单通道的Mask输出（最后的激活函数很疯狂，relu接sigmoid，保证每个像素位置介于01之间），此时的Mask掩码输出大小为[2*MASK_POOL_SIZE, 2*MASK_POOL_SIZE]，对demo.pynb而言是28*28（源码注释给的数目是没有*2的，由于最后有一个stride为2的转置卷积，所以应该是疏忽，毕竟config的另外一个参量MASK_SHAPE值为28*28，虽然在推断网络没有使用到）。
至此，推断网络的最后一个输出——对象级别原始Mask计算了出来。
三、build函数返回
然后，我们将整个build函数构建model所需要的输入tensor和输出tensor进行打包，创建keras模型，
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
整理一下模型输出Tensor：
# num_anchors, 每张图片上生成的锚框数量
# num_rois, 每张图片上由锚框筛选出的推荐区数量，
# # 由 POST_NMS_ROIS_TRAINING 或 POST_NMS_ROIS_INFERENCE 规定
# num_detections, 每张图片上最终检测输出框，
# # 由 DETECTION_MAX_INSTANCES 规定 # detections, [batch, num_detections, (y1, x1, y2, x2, class_id, score)]
# mrcnn_class, [batch, num_rois, NUM_CLASSES] classifier probabilities
# mrcnn_bbox, [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))]
# mrcnn_mask, [batch, num_detections, MASK_POOL_SIZE, MASK_POOL_SIZE, NUM_CLASSES]
# rpn_rois, [batch, num_rois, (y1, x1, y2, x2, class_id, score)]
# rpn_class, [batch, num_anchors, 2]
# rpn_bbox [batch, num_anchors, 4]
由于我们的GPU_COUNT为1，不是多卡训练，所以不需要GPU支持（多GPU模型后面应该会单开一节讲），直接将model返回即可。
附、MaskRCNN class网络构建方法总览
再次将build函数全貌贴出，分支选项按照mode='inference'选即为本系列推断网络的内容。
############################################################
# 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] # [1024 1024 3]
if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6): # 这里就限定了下采样不会产生坐标误差
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) # 256
P4 = KL.Add(name="fpn_p4add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5),
KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c4p4')(C4)])
P3 = KL.Add(name="fpn_p3add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4),
KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c3p3')(C3)])
P2 = KL.Add(name="fpn_p2add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3),
KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c2p2')(C2)])
# Attach 3x3 conv to all P layers to get the final feature maps.
P2 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p2")(P2)
P3 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p3")(P3)
P4 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p4")(P4)
P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p5")(P5)
# P6 is used for the 5th anchor scale in RPN. Generated by
# subsampling from P5 with stride of 2.
P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5) # Note that P6 is used in RPN, but not in the classifier heads.
rpn_feature_maps = [P2, P3, P4, P5, P6]
mrcnn_feature_maps = [P2, P3, P4, P5] # Anchors
if mode == "training":
anchors = self.get_anchors(config.IMAGE_SHAPE)
# Duplicate across the batch dimension because Keras requires it
# TODO: can this be optimized to avoid duplicating the anchors?
anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)
# A hack to get around Keras's bad support for constants
anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
else:
anchors = input_anchors # RPN Model, 返回的是keras的Module对象, 注意keras中的Module对象是可call的
rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE, # 1 3 256
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])) # 保存各pyramid特征经过RPN之后的结果
# 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)) # [[logits2,……6], [class2,……6], [bbox2,……6]]
outputs = [KL.Concatenate(axis=1, name=n)(list(o))
for o, n in zip(outputs, output_names)] # [batch, num_anchors, 2/4]
# 其中num_anchors指的是全部特征层上的anchors总数
rpn_class_logits, rpn_class, rpn_bbox = outputs # Generate proposals
# Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates
# and zero padded.
# POST_NMS_ROIS_INFERENCE = 1000
# POST_NMS_ROIS_TRAINING = 2000
proposal_count = config.POST_NMS_ROIS_TRAINING if mode == "training"\
else config.POST_NMS_ROIS_INFERENCE
# [IMAGES_PER_GPU, num_rois, (y1, x1, y2, x2)]
# IMAGES_PER_GPU取代了batch，之后说的batch都是IMAGES_PER_GPU
rpn_rois = ProposalLayer(
proposal_count=proposal_count,
nms_threshold=config.RPN_NMS_THRESHOLD, # 0.7
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
# output shapes:
# mrcnn_class_logits: [batch, num_rois, NUM_CLASSES] classifier logits (before softmax)
# mrcnn_class: [batch, num_rois, NUM_CLASSES] classifier probabilities
# mrcnn_bbox(deltas): [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))]
mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\
fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,
config.POOL_SIZE, # 7
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, # 14
config.NUM_CLASSES,
train_bn=config.TRAIN_BN) # num_anchors, 每张图片上生成的锚框数量
# num_rois, 每张图片上由锚框筛选出的推荐区数量，
# # 由 POST_NMS_ROIS_TRAINING 或 POST_NMS_ROIS_INFERENCE 规定
# num_detections, 每张图片上最终检测输出框，
# # 由 DETECTION_MAX_INSTANCES 规定 # detections, [batch, num_detections, (y1, x1, y2, x2, class_id, score)]
# mrcnn_class, [batch, num_rois, NUM_CLASSES] classifier probabilities
# mrcnn_bbox, [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))]
# mrcnn_mask, [batch, num_detections, MASK_POOL_SIZE, MASK_POOL_SIZE, NUM_CLASSES]
# rpn_rois, [batch, num_rois, (y1, x1, y2, x2, class_id, score)]
# rpn_class, [batch, num_anchors, 2]
# rpn_bbox [batch, num_anchors, 4]
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
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