pointnet训练文件pointnet/sem_seg/train.py注释
原文如下：
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
import socket
import importlib
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
import provider
import tf_util
#num_point与所用的数据集有关
#https://blog.csdn.net/chengxuyuanyonghu/article/details/59716405
#解析命令行参数和选项的标准模块
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pointnet_cls', help='Model name: pointnet_cls or pointnet_cls_basic [default: pointnet_cls]')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--max_epoch', type=int, default=250, help='Epoch to run [default: 250]')
parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.8]')
FLAGS = parser.parse_args()
#将定义的参数全部更改为大写字母的形式
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step #计算learning_rate的衰减
DECAY_RATE = FLAGS.decay_rate #计算learning_rate的衰减
#加载网络模型
#https://blog.csdn.net/xie_0723/article/details/78004649
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model+'.py')
#记录训练日志，以及备份model.py和train.py
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
#总共40个语义分类标签#BN_ 开头的4个变量用来计算 Batch Normalization 的Decay参数，即decay参数也随着训练逐渐decay
MAX_NUM_POINT = 2048
NUM_CLASSES = 40
#BN_ 开头的4个变量用来计算 Batch Normalization 的Decay参数，即decay参数也随着训练逐渐decay
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
# ModelNet40 official train/test split
TRAIN_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
TEST_FILES = provider.getDataFiles(\
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'))
#log_string(out_str)函数用来log训练日志
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush() #清空缓存区
print(out_str)
#计算指数衰减的学习率。训练时学习率最好随着训练衰减。
#decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) #此处 global_step = batch * BATCH_SIZE
https://blog.csdn.net/wuguangbin1230/article/details/77658229
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
https://blog.csdn.net/YZXnuaa/article/details/79733700
return learning_rate
#if the argument staircase is True, then global_step /decay_steps is an integer division and the decayed learning rate follows a staircase function.
#计算衰减的Batch Normalization 的 decay。基本同上。
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
#这一段主要是placeholder，以及batch初始化为0。
def train():
with tf.Graph().as_default(): #表示将这个类实例，也就是新生成的图作为整个 tensorflow 运行环境的默认图，如果只有一个主线程不写也没有关系，tensorflow 里面已经存好了一张默认图，可以使用tf.get_default_graph() 来调用（显示这张默认纸），当你有多个线程就可以创造多个tf.Graph()，就是你可以有一个画图本，有很多张图纸，这时候就会有一个默认图的概念了。
with tf.device('/gpu:'+str(GPU_INDEX)): #如果需要切换成CPU运算，可以调用tf.device(device_name)函数，其中device_name格式如/cpu:0其中的0表示设备号，TF不区分CPU的设备号，设置为0即可。GPU区分设备号/gpu:0和/gpu:1表示两张不同的显卡
pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=()) #placeholder()函数是在神经网络构建graph的时候在模型中的占位，此时并没有把要输入的数据传入模型，它只会分配必要的内存
print(is_training_pl)
# Note the global_step=batch parameter to minimize.
#globe_step初始化为0，每次自动加1
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0) #创建一个变量
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
https://blog.csdn.net/heiheiya/article/details/80972322
#预测值为pred，调用model.py 中的 get_model()得到。由get_model()可知，pred的维度为B×N×40，40为Channel数，对应40个分类标签。每个点的这40个值最大的一个的下标即为所预测的分类标签。
# Get model and loss
pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
loss = MODEL.get_loss(pred, labels_pl, end_points)
tf.summary.scalar('loss', loss)
#tf.argmax(pred, 2) 返回pred C 这个维度的最大值索引
#tf.equal() 比较两个张量对应位置是否想等，返回相同维度的bool值矩阵
correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
#压缩求和，用于降维 https://blog.csdn.net/arjick/article/details/78415675
tf.summary.scalar('accuracy', accuracy)
#获得衰减后的学习率，以及选择优化器optimizer。
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
#配置session 运行参数。
# Create a session
config = tf.ConfigProto() #创建sess的时候对sess进行参数配置
config.gpu_options.allow_growth = True # =True是让TensorFlow在运行过程中动态申请显存，避免过多的显存占用。
config.allow_soft_placement = True #当指定的设备不存在时，允许选择一个存在的设备运行。比如gpu不存在，自动降到cpu上运行。
config.log_device_placement = False #在终端打印出各项操作是在哪个设备上运行的
sess = tf.Session(config=config)
# Add summary writers
#merged = tf.merge_all_summaries()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'),
sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'))
#初始化参数，开始训练。
#train_one_epoch 函数用来训练一个epoch，eval_one_epoch函数用来每运行一个epoch后evaluate在测试集的accuracy和loss。每10个epoch保存1次模型。
# Init variables
init = tf.global_variables_initializer()
# To fix the bug introduced in TF 0.12.1 as in
# http://stackoverflow.com/questions/41543774/invalidargumenterror-for-tensor-bool-tensorflow-0-12-1
#sess.run(init)
sess.run(init, {is_training_pl: True})
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch}
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush() #在同一个位置刷新输出
train_one_epoch(sess, ops, train_writer) #train_one_epoch 函数用来训练一个epoch
eval_one_epoch(sess, ops, test_writer) #eval_one_epoch函数用来每运行一个epoch后evaluate在测试集的accuracy和loss
# Save the variables to disk.，每10个epoch保存1次模型
if epoch % 10 == 0:
save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
log_string("Model saved in file: %s" % save_path)
#provider.shuffle_data 函数随机打乱数据，返回打乱后的数据。
#num_batches = file_size // BATCH_SIZE，计算在指定BATCH_SIZE下，训练1个epoch 需要几个mini-batch训练。
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES))
np.random.shuffle(train_file_idxs)
for fn in range(len(TRAIN_FILES)):
log_string('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(TRAIN_FILES[train_file_idxs[fn]])
current_data = current_data[:,0:NUM_POINT,:]
current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
#在一个epoch 中逐个mini-batch训练直至遍历完一遍训练集。计算总分类正确数total_correct和已遍历样本数total_senn，总损失loss_sum.
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
# Augment batched point clouds by rotation and jittering
rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
feed_dict = {ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
#记录平均loss，以及平均accuracy。
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
for fn in range(len(TEST_FILES)):
log_string('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += (loss_val*BATCH_SIZE)
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx] == l)
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
if __name__ == "__main__":
train()
LOG_FOUT.close()
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