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姿态估计1-00：FSA-Net(头部姿态估算)-目录-史上最新无死角讲解
分析前言我相信，大家跟到这里来，说明你以及看完论文了，既然如此，我们在来看看training_and_testing/run_fsanet_train.sh文件，这是我们训练的脚本内容如下：
.......
是的，我这里是空的，因为我觉得复制出来太臃肿，显得我的博客不够帅气，所以就不复制出来了，你看自己源码的即可。该文件可以看到如下字样的注释：
# Train on protocal 1 # SSRNET_MT # FSANET_Capsule # FSANET_Netvlad # FSANET_Metric # Train on protocal 2 # SSRNET_MT # FSANET_Capsule # FSANET_Netvlad # FSANET_Metric # Fine-tuned on BIWI with synhead pre-trained model
看完了论文的朋友应该就比较熟悉了，因为在论文中存在如下图示：
也就是说，作者需要执行多次训练脚本，全是为了为了完成实验的对比。那么我们在分析的时候，当然是选择效果最好的哪个进行分析。说得简单，但是现在我也没办法一眼就知道哪个效果最好，不过通过观察可以知道，他们主要的差别就是在于–model_type的参数不一样，其可以选择1到10之间。那么在分析源码的时候，着重分析其处理过程即可。
源码注释：
import os import sys
sys.path.append('..') import logging import argparse import pandas as pd import numpy as np from lib.FSANET_model import * from lib.SSRNET_model import * import TYY_callbacks from TYY_generators import * from keras.utils import np_utils from keras.utils import plot_model from keras.optimizers import SGD, Adam from keras.preprocessing.image import ImageDataGenerator from keras.callbacks import LearningRateScheduler, ModelCheckpoint
logging.basicConfig(level=logging.DEBUG) def load_data_npz(npz_path): d = np.load(npz_path) return d["image"], d["pose"] def mk_dir(dir): try: os.mkdir( dir ) except OSError: pass def get_args(): parser = argparse.ArgumentParser(description="This script trains the CNN model for head pose estimation.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--batch_size", type=int, default=16, help="batch size") parser.add_argument("--nb_epochs", type=int, default=90, help="number of epochs") parser.add_argument("--validation_split", type=float, default=0.2, help="validation split ratio") parser.add_argument("--model_type", type=int, default=3, help="type of model") parser.add_argument("--db_name", type=str, default='300W_LP', help="type of model") args = parser.parse_args() return args def main(): # 解析并且赋值相关参数 args = get_args() db_name = args.db_name
batch_size = args.batch_size
nb_epochs = args.nb_epochs
validation_split = args.validation_split
model_type = args.model_type
image_size = 64 logging.debug("Loading data...") # 如果训练的数据集为300W_LP if db_name == '300W_LP': # 获得对应的npz文件 db_list = ['AFW.npz','AFW_Flip.npz','HELEN.npz','HELEN_Flip.npz','IBUG.npz','IBUG_Flip.npz','LFPW.npz','LFPW_Flip.npz'] # 用于保存像素 image = [] # 用于保存姿态 pose = [] # 循环加入所有的图片像素，以及对应的姿态 for i in range(0,len(db_list)): image_temp, pose_temp = load_data_npz('../data/type1/'+db_list[i]) image.append(image_temp) pose.append(pose_temp) # 把链表转化为np数组格式. # 加载完数据之后为[122450, 64, 64, 3] image = np.concatenate(image,0) # 加载完数据之后为[122450, 3] pose = np.concatenate(pose,0) # 对于其角度不在[-99,99]之间的数据，全部剔除掉 # we only care the angle between [-99,99] and filter other angles x_data = [] y_data = [] print(image.shape) print(pose.shape) for i in range(0,pose.shape[0]): temp_pose = pose[i,:] if np.max(temp_pose)<=99.0 and np.min(temp_pose)>=-99.0: x_data.append(image[i,:,:,:]) y_data.append(pose[i,:]) x_data = np.array(x_data) y_data = np.array(y_data) print(x_data.shape) print(y_data.shape) elif db_name == 'synhead_noBIWI': image, pose = load_data_npz('../data/synhead/media/jinweig/Data2/synhead2_release/synhead_noBIWI.npz') x_data = image
y_data = pose # 如果训练的数据集为BIWI elif db_name == 'BIWI': image, pose = load_data_npz('../data/BIWI_train.npz') x_train = image
y_train = pose
image_test, pose_test = load_data_npz('../data/BIWI_test.npz') x_test = image_test
y_test = pose_test else: print('db_name is wrong!!!') return # 训练到30ep和60ep会进行学习率衰减 start_decay_epoch = [30,60] #优化器 optMethod = Adam() # 论文中Stage的数目 stage_num = [3,3,3] lambda_d = 1 # 输出姿态为yaw, pitch, roll num_classes = 3 # 是否使用最好的方法 isFine = False #根据model_type参数 进行模型构建 if model_type == 0: model = SSR_net_ori_MT(image_size, num_classes, stage_num, lambda_d)() save_name = 'ssrnet_ori_mt' elif model_type == 1: model = SSR_net_MT(image_size, num_classes, stage_num, lambda_d)() save_name = 'ssrnet_mt' elif model_type == 2: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 7*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_capsule'+str_S_set elif model_type == 3: # num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 7*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_Var_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_var_capsule'+str_S_set elif model_type == 4: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 8*8*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_noS_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_noS_capsule'+str_S_set elif model_type == 5: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 7*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_NetVLAD(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_netvlad'+str_S_set elif model_type == 6: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 7*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_Var_NetVLAD(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_var_netvlad'+str_S_set elif model_type == 7: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 8*8*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_noS_NetVLAD(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_noS_netvlad'+str_S_set elif model_type == 8: # 论文中 num_capsule = 3 # 论文中的c’=16 dim_capsule = 16 # 论文中的stream数目 routings = 2 # 论文中的n'=7， num_primcaps = 7*3 # 论文中的m=5 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_Metric(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_metric'+str_S_set elif model_type == 9: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 7*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_Var_Metric(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_var_metric'+str_S_set elif model_type == 10: num_capsule = 3 dim_capsule = 16 routings = 2 num_primcaps = 8*8*3 m_dim = 5 S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim] str_S_set = ''.join('_'+str(x) for x in S_set) model = FSA_net_noS_Metric(image_size, num_classes, stage_num, lambda_d, S_set)() save_name = 'fsanet_noS_metric'+str_S_set # 指定模型的优化方法，以及loss（均值绝对误差）计算方式， model.compile(optimizer=optMethod, loss=["mae"],loss_weights=[1]) logging.debug("Model summary...") # 计算模型参数，打印模型结构 model.count_params() model.summary() logging.debug("Saving model...") # 创建必要的目录，如保存模型的路径等等 mk_dir(db_name+"_models") mk_dir(db_name+"_models/"+save_name) mk_dir(db_name+"_checkpoints") # 把模型绘画成图，便于分析（总体结构） plot_model(model, to_file=db_name+"_models/"+save_name+"/"+save_name+".png") # 绘画网络模型的细致结构 for i_L,layer in enumerate(model.layers): if i_L >0 and i_L< len(model.layers)-1: if 'pred' not in layer.name and 'caps' != layer.name and 'merge' not in layer.name and 'model' in layer.name: plot_model(layer, to_file=db_name+"_models/"+save_name+"/"+layer.name+".png") # 迭代到指定次数，进行学习率衰减 decaylearningrate = TYY_callbacks.DecayLearningRate(start_decay_epoch) # 查看指定路径下的模型知否存在，存在则自动加载该目录下的模型 callbacks = [ModelCheckpoint(db_name+"_checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5", monitor="val_loss", verbose=1, save_best_only=True, mode="auto"), decaylearningrate ] logging.debug("Running training...") # 如果为'BIWI'数据集,则进行测试集和训练集的划分 if db_name != 'BIWI': data_num = len(x_data) indexes = np.arange(data_num) np.random.shuffle(indexes) x_data = x_data[indexes] y_data = y_data[indexes] train_num = int(data_num * (1 - validation_split)) x_train = x_data[:train_num] x_test = x_data[train_num:] y_train = y_data[:train_num] y_test = y_data[train_num:] elif db_name == 'BIWI': train_num = np.shape(x_train)[0] # 为模型绑定训练数据，测试数据，并且进行训练（真的是个讨厌的框架，用了pytorch之后，其他的框架越看越难受） hist = model.fit_generator(generator=data_generator_pose(X=x_train, Y=y_train, batch_size=batch_size), steps_per_epoch=train_num // batch_size, validation_data=(x_test, y_test), epochs=nb_epochs, verbose=1, callbacks=callbacks) logging.debug("Saving weights...") model.save_weights(os.path.join(db_name+"_models/"+save_name, save_name+'.h5'), overwrite=True) pd.DataFrame(hist.history).to_hdf(os.path.join(db_name+"_models/"+save_name, 'history_'+save_name+'.h5'), "history") if __name__ == '__main__': main()
360行，行行出状元，总得先有钱。三千大道归一大法，无非就是，加载数据，构建模型，训练数据，保存模型。没了，就这么通俗的总结一下。
通过代码的浏览，可以清楚地知道其复杂的地方，是模型的构建过程，具体细节下篇博客进行讲解。
