Mask R-CNN（十一）：代码理解inspect_model.ipynb

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一、导包

import os
import sys
import random
import math
import re
import time
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches

#设置根目录
ROOT_DIR = os.path.abspath("../../")

#导入Mask RCNN
sys.path.append(ROOT_DIR)
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.model import log

%matplotlib inline 

#保存log和model的目录
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

#预训练权重文件的路径
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
#下载COCO预训练权重文件
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

#Shapes数据集预训练权重文件的路径
SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes.h5")

二、配置

#以下代码块二选其一即可
 
# Shapes toy数据集
# import shapes
# config = shapes.ShapesConfig()
 
# MS COCO数据集
import coco
config = coco.CocoConfig()
COCO_DIR = "path/to/COCO dataset"

#预测时，对训练时的配置做一些小的修改.
class InferenceConfig(config.__class__):
    # Run detection on one image at a time
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

config = InferenceConfig()
config.display()

三、Notebook Preferences

#选择加载神经网络的设备.
#当你同时在该设备上训练模型的时候这个参数就比较有用了 
#你可以使用CPU，将GPU留作训练用
DEVICE = "/cpu:0"  # /cpu:0 or /gpu:0

#检查model是用于训练还是用于预测
#值: 'inference' 或 'training'
# TODO: 'training'测试模式的代码还没实现
TEST_MODE = "inference"

def get_ax(rows=1, cols=1, size=16):
    """返回一个在该notebook中用于所有可视化的Matplotlib Axes array。
    提供一个中央点坐标来控制graph的尺寸。
    
    调整attribute的尺寸来控制渲染多大的图像
    """
    _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
    return ax

四、加载验证数据集

# 构造验证数据集
if config.NAME == 'shapes':
    dataset = shapes.ShapesDataset()
    dataset.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
elif config.NAME == "coco":
    dataset = coco.CocoDataset()
    dataset.load_coco(COCO_DIR, "minival")

#在使用数据集之前必须调用下面的语句
dataset.prepare()

print("Images: {}\nClasses: {}".format(len(dataset.image_ids), dataset.class_names))

五、加载model

#创建一个用于预测的model
with tf.device(DEVICE):
    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR,
                              config=config)
 
#设置weights文件路径
if config.NAME == "shapes":
    weights_path = SHAPES_MODEL_PATH
elif config.NAME == "coco":
    weights_path = COCO_MODEL_PATH
#或者取消下面的注释行，加载最近训练的模型
# weights_path = model.find_last()
 
#加载weights
print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True)

六、运行检测

image_id = random.choice(dataset.image_ids)
image, image_meta, gt_class_id, gt_bbox, gt_mask =\
    modellib.load_image_gt(dataset, config, image_id, use_mini_mask=False)
info = dataset.image_info[image_id]
print("image ID: {}.{} ({}) {}".format(info["source"], info["id"], image_id, 
                                       dataset.image_reference(image_id)))
#运行物体检测
results = model.detect([image], verbose=1)

#显示结果
ax = get_ax(1)
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            dataset.class_names, r['scores'], ax=ax,
                            title="Predictions")
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
log("gt_mask", gt_mask)

image ID: coco.392144 (34940) http://cocodataset.org/#explore?id=392144
Processing 1 images
image                    shape: (1024, 1024, 3)       min:    0.00000  max:  255.00000
molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000
image_metas              shape: (1, 89)               min:    0.00000  max: 1024.00000
gt_class_id              shape: (10,)                 min:    1.00000  max:   40.00000
gt_bbox                  shape: (10, 5)               min:    0.00000  max: 1024.00000
gt_mask                  shape: (1024, 1024, 10)      min:    0.00000  max:    1.00000

6.1 Precision-Recall

#画出precision-recall的曲线
AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id, gt_mask,
                                          r['rois'], r['class_ids'], r['scores'], r['masks'])
visualize.plot_precision_recall(AP, precisions, recalls)

# 显示ground truth和预测的网格
visualize.plot_overlaps(gt_class_id, r['class_ids'], r['scores'],
                        overlaps, dataset.class_names)

6.2 计算mAP @ IoU=50

#计算VOC-style平均精度
def compute_batch_ap(image_ids):
    APs = []
    for image_id in image_ids:
        #加载图像
        image, image_meta, gt_class_id, gt_bbox, gt_mask =\
            modellib.load_image_gt(dataset, config,
                                   image_id, use_mini_mask=False)
        #运行物体检测
        results = model.detect([image], verbose=0)
        #计算AP
        r = results[0]
        AP, precisions, recalls, overlaps =\
            utils.compute_ap(gt_bbox, gt_class_id, gt_mask,
                              r['rois'], r['class_ids'], r['scores'], r['masks'])
        APs.append(AP)
    return APs

#随机选择一些图像
image_ids = np.random.choice(dataset.image_ids, 10)
APs = compute_batch_ap(image_ids)
print("mAP @ IoU=50: ", np.mean(APs))

七、详细的预测步骤

7.1  Region Proposal Network

Region Proposal Network（RPN）在图像的众多boxes（anchors）中运行一个轻量级的二值分类，并且返回是目标物体或者不是目标物体的分数。那些获得判断是目标物体的高分数anchors将会传递给下一阶段进行分类。

通常，即使是positive anchors也不会覆盖全部物体。所以RPN还会进行一个回归优化来获得更正确的边界，包括平移和缩放anchors。

7.1.1 RPN Targets

RPN targets是RPN的训练值。为了生成这些targets，我们用覆盖全图的不同尺度的anchors，然后计算这些anchors和ground truth的IoU。与ground truth的IoU>=0.7的认为是positive anchors，IoU<0.3的是negative anchors。IoU>=0.3并且IoU<0.7的是neutral anchors，将不会用于训练。

为了训练一个RPN regressor，我们还需要计算能使anchor完全覆盖ground truth的偏移和缩放量。

#生成RPN trainig targets
# target_rpn_match=1是positive anchors, -1是negative anchors
# 0是neutral anchors.
target_rpn_match, target_rpn_bbox = modellib.build_rpn_targets(
    image.shape, model.anchors, gt_class_id, gt_bbox, model.config)
log("target_rpn_match", target_rpn_match)
log("target_rpn_bbox", target_rpn_bbox)

positive_anchor_ix = np.where(target_rpn_match[:] == 1)[0]
negative_anchor_ix = np.where(target_rpn_match[:] == -1)[0]
neutral_anchor_ix = np.where(target_rpn_match[:] == 0)[0]
positive_anchors = model.anchors[positive_anchor_ix]
negative_anchors = model.anchors[negative_anchor_ix]
neutral_anchors = model.anchors[neutral_anchor_ix]
log("positive_anchors", positive_anchors)
log("negative_anchors", negative_anchors)
log("neutral anchors", neutral_anchors)

#将refinement deltas应用于positive anchors
refined_anchors = utils.apply_box_deltas(
    positive_anchors,
    target_rpn_bbox[:positive_anchors.shape[0]] * model.config.RPN_BBOX_STD_DEV)
log("refined_anchors", refined_anchors, )

#显示refinement (点)之前的positive anchors和refinement (线)之后的positive anchors.
visualize.draw_boxes(image, boxes=positive_anchors, refined_boxes=refined_anchors, ax=get_ax())

target_rpn_match         shape: (65472,)              min:   -1.00000  max:    1.00000
target_rpn_bbox          shape: (256, 4)              min:   -5.19860  max:    2.59641
positive_anchors         shape: (14, 4)               min:    5.49033  max:  973.25483
negative_anchors         shape: (242, 4)              min:  -22.62742  max: 1038.62742
neutral anchors          shape: (65216, 4)            min: -362.03867  max: 1258.03867
refined_anchors          shape: (14, 4)               min:    0.00000  max: 1023.99994

7.1.2 RPN预测

#运行RPN sub-graph
pillar = model.keras_model.get_layer("ROI").output  # node to start searching from

nms_node = model.ancestor(pillar, "ROI/rpn_non_max_suppression:0")
if nms_node is None:
    nms_node = model.ancestor(pillar, "ROI/rpn_non_max_suppression/NonMaxSuppressionV2:0")

rpn = model.run_graph([image], [
    ("rpn_class", model.keras_model.get_layer("rpn_class").output),
    ("pre_nms_anchors", model.ancestor(pillar, "ROI/pre_nms_anchors:0")),
    ("refined_anchors", model.ancestor(pillar, "ROI/refined_anchors:0")),
    ("refined_anchors_clipped", model.ancestor(pillar, "ROI/refined_anchors_clipped:0")),
    ("post_nms_anchor_ix", nms_node),
    ("proposals", model.keras_model.get_layer("ROI").output),
])

#显示得分较高的anchors(refinement之前)
limit = 100
sorted_anchor_ids = np.argsort(rpn['rpn_class'][:,:,1].flatten())[::-1]
visualize.draw_boxes(image, boxes=model.anchors[sorted_anchor_ids[:limit]], ax=get_ax())

#显示refinement之后的anchors.之后将超出图像边界的裁剪掉
limit = 50
ax = get_ax(1, 2)
visualize.draw_boxes(image, boxes=rpn["pre_nms_anchors"][0, :limit], 
           refined_boxes=rpn["refined_anchors"][0, :limit], ax=ax[0])
visualize.draw_boxes(image, refined_boxes=rpn["refined_anchors_clipped"][0, :limit], ax=ax[1])

#显示NMS优化后的anchors
limit = 50
ixs = rpn["post_nms_anchor_ix"][:limit]
visualize.draw_boxes(image, refined_boxes=rpn["refined_anchors_clipped"][0, ixs], ax=get_ax())

#显示最终的proposals
#这和前一步的结果一样(NMS优化)， 但是将坐标规范化到[0, 1].
limit = 50
#显示之后转化回图像坐标
h, w = config.IMAGE_SHAPE[:2]
proposals = rpn['proposals'][0, :limit] * np.array([h, w, h, w])
visualize.draw_boxes(image, refined_boxes=proposals, ax=get_ax())

#测试RPN recall (被anchors覆盖的物体的比例)
#这里我们用三种不同的方法测试recall:
# - 全部anchors
# - 所有refined anchors
# - NMS之后的Refined anchors
iou_threshold = 0.7

recall, positive_anchor_ids = utils.compute_recall(model.anchors, gt_bbox, iou_threshold)
print("All Anchors ({:5})       Recall: {:.3f}  Positive anchors: {}".format(
    model.anchors.shape[0], recall, len(positive_anchor_ids)))

recall, positive_anchor_ids = utils.compute_recall(rpn['refined_anchors'][0], gt_bbox, iou_threshold)
print("Refined Anchors ({:5})   Recall: {:.3f}  Positive anchors: {}".format(
    rpn['refined_anchors'].shape[1], recall, len(positive_anchor_ids)))

recall, positive_anchor_ids = utils.compute_recall(proposals, gt_bbox, iou_threshold)
print("Post NMS Anchors ({:5})  Recall: {:.3f}  Positive anchors: {}".format(
    proposals.shape[0], recall, len(positive_anchor_ids)))

All Anchors (65472)       Recall: 0.400  Positive anchors: 8
Refined Anchors (10000)   Recall: 0.900  Positive anchors: 65
Post NMS Anchors (   50)  Recall: 0.800  Positive anchors: 9

7.2 Proposal分类

7.2.1 Proposal Classification

运行分类器以产生分类概率和bounding box回归。

#获取classifier和mask的输入和输出.
mrcnn = model.run_graph([image], [
    ("proposals", model.keras_model.get_layer("ROI").output),
    ("probs", model.keras_model.get_layer("mrcnn_class").output),
    ("deltas", model.keras_model.get_layer("mrcnn_bbox").output),
    ("masks", model.keras_model.get_layer("mrcnn_mask").output),
    ("detections", model.keras_model.get_layer("mrcnn_detection").output),
])

#获取检测的class IDs.修剪zero padding.
det_class_ids = mrcnn['detections'][0, :, 4].astype(np.int32)
det_count = np.where(det_class_ids == 0)[0][0]
det_class_ids = det_class_ids[:det_count]
detections = mrcnn['detections'][0, :det_count]

print("{} detections: {}".format(
    det_count, np.array(dataset.class_names)[det_class_ids]))

captions = ["{} {:.3f}".format(dataset.class_names[int(c)], s) if c > 0 else ""
            for c, s in zip(detections[:, 4], detections[:, 5])]
visualize.draw_boxes(
    image, 
    refined_boxes=utils.denorm_boxes(detections[:, :4], image.shape[:2]),
    visibilities=[2] * len(detections),
    captions=captions, title="Detections",
    ax=get_ax())

7.2.2 检测的步骤

# Proposals的坐标是规范化的坐标. 将它们缩放到图像坐标.
h, w = config.IMAGE_SHAPE[:2]
proposals = np.around(mrcnn["proposals"][0] * np.array([h, w, h, w])).astype(np.int32)

# 每个proposal的Class ID, score, and mask
roi_class_ids = np.argmax(mrcnn["probs"][0], axis=1)
roi_scores = mrcnn["probs"][0, np.arange(roi_class_ids.shape[0]), roi_class_ids]
roi_class_names = np.array(dataset.class_names)[roi_class_ids]
roi_positive_ixs = np.where(roi_class_ids > 0)[0]

#有多少ROIs和空行?
print("{} Valid proposals out of {}".format(np.sum(np.any(proposals, axis=1)), proposals.shape[0]))
print("{} Positive ROIs".format(len(roi_positive_ixs)))

# Class数量
print(list(zip(*np.unique(roi_class_names, return_counts=True))))

#显示一个随机样本的proposals.
#分类为背景的Proposals是点，其他的显示它们的类名和置信分数.
limit = 200
ixs = np.random.randint(0, proposals.shape[0], limit)
captions = ["{} {:.3f}".format(dataset.class_names[c], s) if c > 0 else ""
            for c, s in zip(roi_class_ids[ixs], roi_scores[ixs])]
visualize.draw_boxes(image, boxes=proposals[ixs],
                     visibilities=np.where(roi_class_ids[ixs] > 0, 2, 1),
                     captions=captions, title="ROIs Before Refinement",
                     ax=get_ax())

应用bounding box优化。

#指定类别的bounding box偏移.
roi_bbox_specific = mrcnn["deltas"][0, np.arange(proposals.shape[0]), roi_class_ids]
log("roi_bbox_specific", roi_bbox_specific)

#应用bounding box变换
#形状: [N, (y1, x1, y2, x2)]
refined_proposals = utils.apply_box_deltas(
    proposals, roi_bbox_specific * config.BBOX_STD_DEV).astype(np.int32)
log("refined_proposals", refined_proposals)

#显示positive proposals
# ids = np.arange(roi_boxes.shape[0])  #显示所有
limit = 5
ids = np.random.randint(0, len(roi_positive_ixs), limit)  #随机显示样本
captions = ["{} {:.3f}".format(dataset.class_names[c], s) if c > 0 else ""
            for c, s in zip(roi_class_ids[roi_positive_ixs][ids], roi_scores[roi_positive_ixs][ids])]
visualize.draw_boxes(image, boxes=proposals[roi_positive_ixs][ids],
                     refined_boxes=refined_proposals[roi_positive_ixs][ids],
                     visibilities=np.where(roi_class_ids[roi_positive_ixs][ids] > 0, 1, 0),
                     captions=captions, title="ROIs After Refinement",
                     ax=get_ax())

roi_bbox_specific        shape: (1000, 4)             min:   -2.44748  max:    2.94838
refined_proposals        shape: (1000, 4)             min:   -8.00000  max: 1028.00000

过滤掉低置信度的检测结果。

#去掉那些被分类为背景的boxes
keep = np.where(roi_class_ids > 0)[0]
print("Keep {} detections:\n{}".format(keep.shape[0], keep))

#去掉低置信度的检测结果
keep = np.intersect1d(keep, np.where(roi_scores >= config.DETECTION_MIN_CONFIDENCE)[0])
print("Remove boxes below {} confidence. Keep {}:\n{}".format(
    config.DETECTION_MIN_CONFIDENCE, keep.shape[0], keep))

为每一个类别做NMS。

#为每一个类别做NMS
pre_nms_boxes = refined_proposals[keep]
pre_nms_scores = roi_scores[keep]
pre_nms_class_ids = roi_class_ids[keep]

nms_keep = []
for class_id in np.unique(pre_nms_class_ids):
    #选择该类的检测结果
    ixs = np.where(pre_nms_class_ids == class_id)[0]
    #做NMS
    class_keep = utils.non_max_suppression(pre_nms_boxes[ixs], 
                                            pre_nms_scores[ixs],
                                            config.DETECTION_NMS_THRESHOLD)
    #映射索引
    class_keep = keep[ixs[class_keep]]
    nms_keep = np.union1d(nms_keep, class_keep)
    print("{:22}: {} -> {}".format(dataset.class_names[class_id][:20], 
                                   keep[ixs], class_keep))

keep = np.intersect1d(keep, nms_keep).astype(np.int32)
print("\nKept after per-class NMS: {}\n{}".format(keep.shape[0], keep))

#显示最终的检测结果
ixs = np.arange(len(keep))  # Display all
# ixs = np.random.randint(0, len(keep), 10)  # Display random sample
captions = ["{} {:.3f}".format(dataset.class_names[c], s) if c > 0 else ""
            for c, s in zip(roi_class_ids[keep][ixs], roi_scores[keep][ixs])]
visualize.draw_boxes(
    image, boxes=proposals[keep][ixs],
    refined_boxes=refined_proposals[keep][ixs],
    visibilities=np.where(roi_class_ids[keep][ixs] > 0, 1, 0),
    captions=captions, title="Detections after NMS",
    ax=get_ax())

7.3 生成masks

这一步从上一层获取检测结果，并且运行mask分支来生成每一个instance的分割masks。

7.3.1 Mask Targets

display_images(np.transpose(gt_mask, [2, 0, 1]), cmap="Blues")

7.3.2 预测的masks

#获取mask分支的预测结果
mrcnn = model.run_graph([image], [
    ("detections", model.keras_model.get_layer("mrcnn_detection").output),
    ("masks", model.keras_model.get_layer("mrcnn_mask").output),
])

#获取检测结果的class IDs.修剪zero padding.
det_class_ids = mrcnn['detections'][0, :, 4].astype(np.int32)
det_count = np.where(det_class_ids == 0)[0][0]
det_class_ids = det_class_ids[:det_count]

print("{} detections: {}".format(
    det_count, np.array(dataset.class_names)[det_class_ids]))

# Masks
det_boxes = utils.denorm_boxes(mrcnn["detections"][0, :, :4], image.shape[:2])
det_mask_specific = np.array([mrcnn["masks"][0, i, :, :, c] 
                              for i, c in enumerate(det_class_ids)])
det_masks = np.array([utils.unmold_mask(m, det_boxes[i], image.shape)
                      for i, m in enumerate(det_mask_specific)])
log("det_mask_specific", det_mask_specific)
log("det_masks", det_masks)

display_images(det_mask_specific[:4] * 255, cmap="Blues", interpolation="none")

display_images(det_masks[:4] * 255, cmap="Blues", interpolation="none")

可视化Activations。有助于观察不同layers。

#获取一些示例层的activations
activations = model.run_graph([image], [
    ("input_image",        model.keras_model.get_layer("input_image").output),
    ("res4w_out",          model.keras_model.get_layer("res4w_out").output),  # for resnet100
    ("rpn_bbox",           model.keras_model.get_layer("rpn_bbox").output),
    ("roi",                model.keras_model.get_layer("ROI").output),
])

#输入图像 (规范化的)
_ = plt.imshow(modellib.unmold_image(activations["input_image"][0],config))

# Backbone feature map
display_images(np.transpose(activations["res4w_out"][0,:,:,:4], [2, 0, 1]))

#显示RPN bounding box deltas的直方图
plt.figure(figsize=(12, 3))
plt.subplot(1, 4, 1)
plt.title("dy")
_ = plt.hist(activations["rpn_bbox"][0,:,0], 50)
plt.subplot(1, 4, 2)
plt.title("dx")
_ = plt.hist(activations["rpn_bbox"][0,:,1], 50)
plt.subplot(1, 4, 3)
plt.title("dw")
_ = plt.hist(activations["rpn_bbox"][0,:,2], 50)
plt.subplot(1, 4, 4)
plt.title("dh")
_ = plt.hist(activations["rpn_bbox"][0,:,3], 50)

# 显示生成的proposals的y，x坐标的分布
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.title("y1, x1")
plt.scatter(activations["roi"][0,:,0], activations["roi"][0,:,1])
plt.subplot(1, 2, 2)
plt.title("y2, x2")
plt.scatter(activations["roi"][0,:,2], activations["roi"][0,:,3])
plt.show()