转载：https://blog.csdn.net/u011311291/article/details/81121519
https://blog.csdn.net/qq_34564612/article/details/79138876
版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/u011311291/article/details/81121519
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
2.faster RCNN(keras版本)代码讲解(2)-数据准备
3.faster RCNN(keras版本)代码讲解(3)-训练流程详情
4.faster RCNN(keras版本)代码讲解(4)-共享卷积层详情
5.faster RCNN(keras版本)代码讲解(5)-RPN层详情
6.faster RCNN(keras版本)代码讲解(6)-ROI Pooling层详情
一.整体流程概述
1.输入参数，其实输入1个就行了（D: empFileVOCdevkit），另外一个resnet权重只是为了加快训练，如图:
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.')