faster RCNN(keras版本)代码讲解(3)-训练流程详情

电玩女神 2022-05-19 03:34 216阅读 0赞

faster RCNN(keras版本)代码讲解博客索引:  
[1.faster RCNN(keras版本)代码讲解(1)-概述][1.faster RCNN_keras_1_-]  
[2.faster RCNN(keras版本)代码讲解(2)-数据准备][2.faster RCNN_keras_2_-]  
[3.faster RCNN(keras版本)代码讲解(3)-训练流程详情][3.faster RCNN_keras_3_-]  
[4.faster RCNN(keras版本)代码讲解(4)-共享卷积层详情][4.faster RCNN_keras_4_-]  
[5.faster RCNN(keras版本)代码讲解(5)-RPN层详情][5.faster RCNN_keras_5_-RPN]  
[6.faster RCNN(keras版本)代码讲解(6)-ROI Pooling层详情][6.faster RCNN_keras_6_-ROI Pooling]

一.整体流程概述  
1.输入参数，其实输入1个就行了（D:\\tempFile\\VOCdevkit），另外一个resnet权重只是为了加快训练，如图:  
![这里写图片描述][70]  
2.从VOC2007数据集中读取数据，变成想要的数据格式  
3.定义生成数据的迭代器  
4.定义3个网络，一个是resnet共享卷积层，一个rpn层，一个分类器层  
5.进入迭代，每次只训练一张图片  
6.是否要进行图片增强  
7.根据特征图和定义框的比例，IOU等计算出y\_train值，作为网络的label  
8.训练rpn层，输出物体，和物体框的坐标  
9.然后再进行分类器层层的训练

二.代码(关键部位已经给出注释)

from __future__ import division
    import random
    import pprint
    import sys
    import time
    import numpy as np
    from optparse import OptionParser
    import pickle
    
    from keras import backend as K
    from keras.optimizers import Adam, SGD, RMSprop
    from keras.layers import Input
    from keras.models import Model
    from keras_frcnn import config, data_generators
    from keras_frcnn import losses as losses
    import keras_frcnn.roi_helpers as roi_helpers
    from keras.utils import generic_utils
    
    sys.setrecursionlimit(40000)
    
    parser = OptionParser()
    
    parser.add_option("-p", "--path", dest="train_path", help="Path to training data.")
    parser.add_option("-o", "--parser", dest="parser", help="Parser to use. One of simple or pascal_voc",
                    default="pascal_voc")
    parser.add_option("-n", "--num_rois", type="int", dest="num_rois", help="Number of RoIs to process at once.", default=32)
    parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.", default='resnet50')
    parser.add_option("--hf", dest="horizontal_flips", help="Augment with horizontal flips in training. (Default=false).", action="store_true", default=False)
    parser.add_option("--vf", dest="vertical_flips", help="Augment with vertical flips in training. (Default=false).", action="store_true", default=False)
    parser.add_option("--rot", "--rot_90", dest="rot_90", help="Augment with 90 degree rotations in training. (Default=false).",
                      action="store_true", default=False)
    parser.add_option("--num_epochs", type="int", dest="num_epochs", help="Number of epochs.", default=2000)
    parser.add_option("--config_filename", dest="config_filename", help=
                    "Location to store all the metadata related to the training (to be used when testing).",
                    default="config.pickle")
    parser.add_option("--output_weight_path", dest="output_weight_path", help="Output path for weights.", default='./model_frcnn.hdf5')
    parser.add_option("--input_weight_path", dest="input_weight_path", help="Input path for weights. If not specified, will try to load default weights provided by keras.")
    
    (options, args) = parser.parse_args()
    
    if not options.train_path:   # if filename is not given
        parser.error('Error: path to training data must be specified. Pass --path to command line')
    
    if options.parser == 'pascal_voc':
        from keras_frcnn.pascal_voc_parser import get_data
    elif options.parser == 'simple':
        from keras_frcnn.simple_parser import get_data
    else:
        raise ValueError("Command line option parser must be one of 'pascal_voc' or 'simple'")
    
    # pass the settings from the command line, and persist them in the config object
    C = config.Config()
    
    C.use_horizontal_flips = bool(options.horizontal_flips)
    C.use_vertical_flips = bool(options.vertical_flips)
    C.rot_90 = bool(options.rot_90)
    
    C.model_path = options.output_weight_path
    C.num_rois = int(options.num_rois)
    
    #有基于VGG和resnet两种网络模型
    if options.network == 'vgg':
        C.network = 'vgg'
        from keras_frcnn import vgg as nn
    elif options.network == 'resnet50':
        from keras_frcnn import resnet as nn
        C.network = 'resnet50'
    else:
        print('Not a valid model')
        raise ValueError
    
    
    # check if weight path was passed via command line
    if options.input_weight_path:
        C.base_net_weights = options.input_weight_path
    else:
        # set the path to weights based on backend and model
        C.base_net_weights = nn.get_weight_path()
    
    all_imgs, classes_count, class_mapping = get_data(options.train_path)
    print(len(all_imgs)) #所有图片的信息，图片名称，位置等
    print(len(classes_count)) #dict,类别:数量，例如'chair': 1432
    print(len(class_mapping)) #dict,各个类别对应的标签向量,0-19,例如chair:0,car:1
    
    #再加入'背景'这个类别
    if 'bg' not in classes_count:
        classes_count['bg'] = 0
        class_mapping['bg'] = len(class_mapping)
    C.class_mapping = class_mapping
    
    # 将class_mapping中的key和value对调
    inv_map = {v: k for k, v in class_mapping.items()}
    
    print('Training images per class:')
    pprint.pprint(classes_count)
    print('Num classes (including bg) = {}'.format(len(classes_count)))
    
    config_output_filename = options.config_filename
    
    with open(config_output_filename, 'wb') as config_f:
        pickle.dump(C,config_f)
        print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(config_output_filename))
    
    # shuffle数据
    random.shuffle(all_imgs)
    
    num_imgs = len(all_imgs)
    # 将all_imgs分为训练集和测试集
    train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
    val_imgs = [s for s in all_imgs if s['imageset'] == 'test']
    
    print('Num train samples {}'.format(len(train_imgs)))
    print('Num val samples {}'.format(len(val_imgs)))
    # 生成anchor
    data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train')
    # data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train')
    data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length,K.image_dim_ordering(), mode='val')
    
    #查看后端是th还是tf，纠正输入方式
    if K.image_dim_ordering() == 'th':
        input_shape_img = (3, None, None)
    else:
        input_shape_img = (None, None, 3)
    
    img_input = Input(shape=input_shape_img)
    roi_input = Input(shape=(None, 4))
    
    # define the base network (resnet here, can be VGG, Inception, etc)
    #定义nn的输入层，还有faster rcnn共享卷积层
    shared_layers = nn.nn_base(img_input, trainable=True)
    print("shared_layers",shared_layers.shape)
    
    # define the RPN, built on the base layers
    #获取anchor的个数，3重基准大小快，3种比例框，3*3=9
    num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
    #定义rpn层，return [x_class, x_regr, base_layers]
    rpn = nn.rpn(shared_layers, num_anchors)
    
    classifier = nn.classifier(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True)
    
    #定义rpn模型的输入和输出一个框2分类（最后使用的sigmod而不是softmax）和框的回归
    model_rpn = Model(img_input, rpn[:2])
    #定义classifier的输入和输出
    model_classifier = Model([img_input, roi_input], classifier)
    
    # this is a model that holds both the RPN and the classifier, used to load/save weights for the models
    model_all = Model([img_input, roi_input], rpn[:2] + classifier)
    
    try:
        print('loading weights from {}'.format(C.base_net_weights))
        model_rpn.load_weights(C.base_net_weights, by_name=True)
        model_classifier.load_weights(C.base_net_weights, by_name=True)
    except:
        print('Could not load pretrained model weights. Weights can be found in the keras application folder \ https://github.com/fchollet/keras/tree/master/keras/applications')
    
    optimizer = Adam(lr=1e-5)
    optimizer_classifier = Adam(lr=1e-5)
    model_rpn.compile(optimizer=optimizer, loss=[losses.rpn_loss_cls(num_anchors), losses.rpn_loss_regr(num_anchors)])
    model_classifier.compile(optimizer=optimizer_classifier, loss=[losses.class_loss_cls, losses.class_loss_regr(len(classes_count)-1)], metrics={
       'dense_class_{}'.format(len(classes_count)): 'accuracy'})
    model_all.compile(optimizer='sgd', loss='mae')
    
    epoch_length = 1000
    num_epochs = int(options.num_epochs)
    iter_num = 0
    
    losses = np.zeros((epoch_length, 5))
    rpn_accuracy_rpn_monitor = []
    rpn_accuracy_for_epoch = []
    start_time = time.time()
    
    best_loss = np.Inf
    
    class_mapping_inv = {v: k for k, v in class_mapping.items()}
    print('Starting training')
    
    vis = True
    
    for epoch_num in range(num_epochs):
    
        progbar = generic_utils.Progbar(epoch_length)
        print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
    
        while True:
            try:
    
                if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
                    mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
                    rpn_accuracy_rpn_monitor = []
                    print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(mean_overlapping_bboxes, epoch_length))
                    if mean_overlapping_bboxes == 0:
                        print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
                print("生成data_gen_train")
                #X为经过最小边600的比例变换的原始图像，Y为[所有框位置的和类别(正例还是反例)，所有框的前36层为位置和后36层（框和gt的比值）]，img_data增强图像后的图像信息
                #那么RPN的reg输出也是比值
                X, Y, img_data = next(data_gen_train)
                print(X.shape,Y[0].shape,Y[1].shape)
    
                loss_rpn = model_rpn.train_on_batch(X, Y)
                print("loss_rpn",len(loss_rpn))
                print("loss_rpn0",loss_rpn[0])
                print("loss_rpn1",loss_rpn[1])
                print("loss_rpn2",loss_rpn[2])
    
                P_rpn = model_rpn.predict_on_batch(X)
    # print("P_rpn_cls",P_rpn[0].reshape((P_rpn[0].shape[1],P_rpn[0].shape[2],P_rpn[0].shape[3]))[:,:,0])
                print("P_rpn_cls",P_rpn[0].shape)
                print("P_rpn_reg",P_rpn[1].shape)
    
                #获得最终选中的框
                R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)
    
                # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
                #再对回归出来的框进行一次iou的计算，再一次过滤，只保留bg框和物体框
                #X2 from (x1,y1,x2,y2) to (x,y,w,h)
                #Y1为每个框对应类别标签，one-host编码
                #Y2为每个框和gt的比值,(x,x,160)，前80表示框是否正确，后80为20个类别可能的框
                X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)
                print("X2",X2.shape)
    # print("X2_0",X2[0,0,:])
    # print("X2_1",X2[0,1,:])
                print("Y1",Y1.shape)
                print("Y2",Y2.shape)
    
                if X2 is None:
                    rpn_accuracy_rpn_monitor.append(0)
                    rpn_accuracy_for_epoch.append(0)
                    continue
                #选出正例还是反例的index，背景的为反例，物体为正例
                neg_samples = np.where(Y1[0, :, -1] == 1)
                pos_samples = np.where(Y1[0, :, -1] == 0)
                print("neg_samples",len(neg_samples[0]))
                print("pos_samples",len(pos_samples[0]))
    
                if len(neg_samples) > 0:
                    neg_samples = neg_samples[0]
                else:
                    neg_samples = []
    
                if len(pos_samples) > 0:
                    pos_samples = pos_samples[0]
                else:
                    pos_samples = []
    
                rpn_accuracy_rpn_monitor.append(len(pos_samples))
                rpn_accuracy_for_epoch.append((len(pos_samples)))
                #num_rois=32，正例要求小于num_rois//2，其它全部由反例填充
                if C.num_rois > 1:
                    if len(pos_samples) < C.num_rois//2:
                        selected_pos_samples = pos_samples.tolist()
                        print("selected_pos_samples",len(selected_pos_samples))
                    else:
                        selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()
                        print("selected_pos_samples",len(selected_pos_samples))
                    try:
                        selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
                        print("selected_neg_samples",len(selected_neg_samples))
                    except:
                        selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()
                        print("selected_neg_samples",len(selected_neg_samples))
                    sel_samples = selected_pos_samples + selected_neg_samples
                else:
                    # in the extreme case where num_rois = 1, we pick a random pos or neg sample
                    selected_pos_samples = pos_samples.tolist()
                    selected_neg_samples = neg_samples.tolist()
                    if np.random.randint(0, 2):
                        sel_samples = random.choice(neg_samples)
                    else:
                        sel_samples = random.choice(pos_samples)
    
                print("sel_samples",len(sel_samples))
                print("sel_samples",sel_samples)
                loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
    # P_classifier = model_classifier.predict([X, X2[:, sel_samples, :]])
    # #[out_class, out_regr]
    # print("P_classifier_out_class",P_classifier[0].shape)
    # print("P_classifier_out_regr",P_classifier[1].shape)
    # import cv2
    # cv2.waitKey(0)
                losses[iter_num, 0] = loss_rpn[1]
                losses[iter_num, 1] = loss_rpn[2]
    
                losses[iter_num, 2] = loss_class[1]
                losses[iter_num, 3] = loss_class[2]
                losses[iter_num, 4] = loss_class[3]
    
                iter_num += 1
    
                progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
                                          ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))])
    
                if iter_num == epoch_length:
                    loss_rpn_cls = np.mean(losses[:, 0])
                    loss_rpn_regr = np.mean(losses[:, 1])
                    loss_class_cls = np.mean(losses[:, 2])
                    loss_class_regr = np.mean(losses[:, 3])
                    class_acc = np.mean(losses[:, 4])
    
                    mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
                    rpn_accuracy_for_epoch = []
    
                    if C.verbose:
                        print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
                        print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
                        print('Loss RPN classifier: {}'.format(loss_rpn_cls))
                        print('Loss RPN regression: {}'.format(loss_rpn_regr))
                        print('Loss Detector classifier: {}'.format(loss_class_cls))
                        print('Loss Detector regression: {}'.format(loss_class_regr))
                        print('Elapsed time: {}'.format(time.time() - start_time))
    
                    curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
                    iter_num = 0
                    start_time = time.time()
    
                    if curr_loss < best_loss:
                        if C.verbose:
                            print('Total loss decreased from {} to {}, saving weights'.format(best_loss,curr_loss))
                        best_loss = curr_loss
                        model_all.save_weights(C.model_path)
    
                    break
    
            except Exception as e:
                print('Exception: {}'.format(e))
                continue
    
    print('Training complete, exiting.')

[1.faster RCNN_keras_1_-]: https://blog.csdn.net/u011311291/article/details/81004067
[2.faster RCNN_keras_2_-]: https://blog.csdn.net/u011311291/article/details/81021731
[3.faster RCNN_keras_3_-]: https://blog.csdn.net/u011311291/article/details/81121519
[4.faster RCNN_keras_4_-]: https://blog.csdn.net/u011311291/article/details/81221145
[5.faster RCNN_keras_5_-RPN]: https://blog.csdn.net/u011311291/article/details/81221893
[6.faster RCNN_keras_6_-ROI Pooling]: https://blog.csdn.net/u011311291/article/details/81673460
[70]: /images/20220519/17c77fea2e8e42c59da51ee7fee0e537.png