本文对21个项目第三章图像识别模型的主要源码进行解析，已在代码中注释。一共三个文件：
1、创建自己的数据集 satellite.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from datasets import dataset_utils
slim = tf.contrib.slim
# 数据集文件模式
_FILE_PATTERN = 'satellite_%s_*.tfrecord'
# 划分训练集和验证集
SPLITS_TO_SIZES = {'train': 4800, 'validation': 1200}
# 类别数
_NUM_CLASSES = 6
# 数据和标签的描述
_ITEMS_TO_DESCRIPTIONS = {
'image': 'A color image of varying size.',
'label': 'A single integer between 0 and 4',
}
# 函数功能：使用Slim读取TFrecord文件
# split_name ：标明训练集/验证集
# dataset_dir ：数据集基本路径
# file_pattern ：文件匹配模式
# reader ：TensorFlow reader类型，用来读取tfrecord文件的容器
# 返回值：返回数据集相关参数
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
# 只能匹配train/validation
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
# 若没有指定file_pattern参数，使用'satellite_%s_*.tfrecord'模式
if not file_pattern:
file_pattern = _FILE_PATTERN
# 拼接数据集文件名
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# 若没有指定reader参数，使用tf.TFRecordReader读取TFRecords文件的记录
if reader is None:
reader = tf.TFRecordReader
# 定义解析特征的字典，tf.FixedLenFeature()函数处理定长的特征
keys_to_features = {
# image/encoded 关键字的值为图像的二进制值
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
# image/format 图像的格式
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpg'),
'image/class/label': tf.FixedLenFeature(
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
# 把items映射为ItemHandler实例
items_to_handlers = {
'image': slim.tfexample_decoder.Image(),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
# 设置解码器
# key_to_features 这个字典需要和TFrecord文件中定义的字典项匹配
# items_to_handlers 中的关键字可以是任意值，但是它的handler的初始化参数必须要来自于keys_to_features中的关键字。
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
# 读取数据集文件
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
# 包装、返回数据集参数
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
2、数据集处理 dataset_factory.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datasets import cifar10
from datasets import flowers
from datasets import imagenet
from datasets import mnist
from datasets import visualwakewords
from datasets import satellite
datasets_map = {
'cifar10': cifar10,
'flowers': flowers,
'imagenet': imagenet,
'mnist': mnist,
'visualwakewords': visualwakewords,
'satellite': satellite,
}
def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None):
if name not in datasets_map:
raise ValueError('Name of dataset unknown %s' % name)
return datasets_map[name].get_split(
split_name,
dataset_dir,
file_pattern,
reader)
3、用给定数据集训练模型 train_image_classifier.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from datasets import dataset_factory
from deployment import model_deploy
from nets import nets_factory
from preprocessing import preprocessing_factory
slim = tf.contrib.slim
# 设定一些命令行参数
tf.app.flags.DEFINE_string(
'master', '', 'The address of the TensorFlow master to use.')
tf.app.flags.DEFINE_string(
'train_dir', '/tmp/tfmodel/',
'Directory where checkpoints and event logs are written to.')
tf.app.flags.DEFINE_integer('num_clones', 1,
'Number of model clones to deploy. Note For '
'historical reasons loss from all clones averaged '
'out and learning rate decay happen per clone '
'epochs')
tf.app.flags.DEFINE_boolean('clone_on_cpu', True,
'Use CPUs to deploy clones.')
tf.app.flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas.')
tf.app.flags.DEFINE_integer(
'num_ps_tasks', 0,
'The number of parameter servers. If the value is 0, then the parameters '
'are handled locally by the worker.')
tf.app.flags.DEFINE_integer(
'num_readers', 4,
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 4,
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
'save_summaries_secs', 600,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_interval_secs', 600,
'The frequency with which the model is saved, in seconds.')
tf.app.flags.DEFINE_integer(
'task', 0, 'Task id of the replica running the training.')
######################
# Optimization Flags #
######################
tf.app.flags.DEFINE_float(
'weight_decay', 0.00004, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_string(
'optimizer', 'rmsprop',
'The name of the optimizer, one of "adadelta", "adagrad", "adam",'
'"ftrl", "momentum", "sgd" or "rmsprop".')
tf.app.flags.DEFINE_float(
'adadelta_rho', 0.95,
'The decay rate for adadelta.')
tf.app.flags.DEFINE_float(
'adagrad_initial_accumulator_value', 0.1,
'Starting value for the AdaGrad accumulators.')
tf.app.flags.DEFINE_float(
'adam_beta1', 0.9,
'The exponential decay rate for the 1st moment estimates.')
tf.app.flags.DEFINE_float(
'adam_beta2', 0.999,
'The exponential decay rate for the 2nd moment estimates.')
tf.app.flags.DEFINE_float('opt_epsilon', 1.0, 'Epsilon term for the optimizer.')
tf.app.flags.DEFINE_float('ftrl_learning_rate_power', -0.5,
'The learning rate power.')
tf.app.flags.DEFINE_float(
'ftrl_initial_accumulator_value', 0.1,
'Starting value for the FTRL accumulators.')
tf.app.flags.DEFINE_float(
'ftrl_l1', 0.0, 'The FTRL l1 regularization strength.')
tf.app.flags.DEFINE_float(
'ftrl_l2', 0.0, 'The FTRL l2 regularization strength.')
tf.app.flags.DEFINE_float(
'momentum', 0.9,
'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('rmsprop_momentum', 0.9, 'Momentum.')
tf.app.flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')
tf.app.flags.DEFINE_integer(
'quantize_delay', -1,
'Number of steps to start quantized training. Set to -1 would disable '
'quantized training.')
#######################
# Learning Rate Flags #
#######################
tf.app.flags.DEFINE_string(
'learning_rate_decay_type',
'exponential',
'Specifies how the learning rate is decayed. One of "fixed", "exponential",'
' or "polynomial"')
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
'end_learning_rate', 0.0001,
'The minimal end learning rate used by a polynomial decay learning rate.')
tf.app.flags.DEFINE_float(
'label_smoothing', 0.0, 'The amount of label smoothing.')
tf.app.flags.DEFINE_float(
'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')
tf.app.flags.DEFINE_float(
'num_epochs_per_decay', 2.0,
'Number of epochs after which learning rate decays. Note: this flag counts '
'epochs per clone but aggregates per sync replicas. So 1.0 means that '
'each clone will go over full epoch individually, but replicas will go '
'once across all replicas.')
tf.app.flags.DEFINE_bool(
'sync_replicas', False,
'Whether or not to synchronize the replicas during training.')
tf.app.flags.DEFINE_integer(
'replicas_to_aggregate', 1,
'The Number of gradients to collect before updating params.')
tf.app.flags.DEFINE_float(
'moving_average_decay', None,
'The decay to use for the moving average.'
'If left as None, then moving averages are not used.')
#######################
# Dataset Flags #
#######################
tf.app.flags.DEFINE_string(
'dataset_name', 'imagenet', 'The name of the dataset to load.')
tf.app.flags.DEFINE_string(
'dataset_split_name', 'train', 'The name of the train/test split.')
tf.app.flags.DEFINE_string(
'dataset_dir', None, 'The directory where the dataset files are stored.')
tf.app.flags.DEFINE_integer(
'labels_offset', 0,
'An offset for the labels in the dataset. This flag is primarily used to '
'evaluate the VGG and ResNet architectures which do not use a background '
'class for the ImageNet dataset.')
tf.app.flags.DEFINE_string(
'model_name', 'inception_v3', 'The name of the architecture to train.')
tf.app.flags.DEFINE_string(
'preprocessing_name', None, 'The name of the preprocessing to use. If left '
'as `None`, then the model_name flag is used.')
tf.app.flags.DEFINE_integer(
'batch_size', 32, 'The number of samples in each batch.')
tf.app.flags.DEFINE_integer(
'train_image_size', None, 'Train image size')
tf.app.flags.DEFINE_integer('max_number_of_steps', None,
'The maximum number of training steps.')
#####################
# Fine-Tuning Flags #
#####################
tf.app.flags.DEFINE_string(
'checkpoint_path', None,
'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
'checkpoint_exclude_scopes', None,
'Comma-separated list of scopes of variables to exclude when restoring '
'from a checkpoint.')
tf.app.flags.DEFINE_string(
'trainable_scopes', None,
'Comma-separated list of scopes to filter the set of variables to train.'
'By default, None would train all the variables.')
tf.app.flags.DEFINE_boolean(
'ignore_missing_vars', False,
'When restoring a checkpoint would ignore missing variables.')
FLAGS = tf.app.flags.FLAGS
# 学习率衰减算法的选择
def _configure_learning_rate(num_samples_per_epoch, global_step):
"""Configures the learning rate.
Args:
num_samples_per_epoch: The number of samples in each epoch of training.
global_step: The global_step tensor.
Returns:
A `Tensor` representing the learning rate.
Raises:
ValueError: if
"""
# Note: when num_clones is > 1, this will actually have each clone to go
# over each epoch FLAGS.num_epochs_per_decay times. This is different
# behavior from sync replicas and is expected to produce different results.
# 计算学习率衰减次数
decay_steps = int(num_samples_per_epoch * FLAGS.num_epochs_per_decay /
FLAGS.batch_size)
if FLAGS.sync_replicas:
decay_steps /= FLAGS.replicas_to_aggregate
# 指数衰减学习率
if FLAGS.learning_rate_decay_type == 'exponential':
return tf.train.exponential_decay(FLAGS.learning_rate,
global_step,
decay_steps,
FLAGS.learning_rate_decay_factor,
staircase=True,
name='exponential_decay_learning_rate')
# 常量学习率
elif FLAGS.learning_rate_decay_type == 'fixed':
return tf.constant(FLAGS.learning_rate, name='fixed_learning_rate')
# 多项式衰减学习率
elif FLAGS.learning_rate_decay_type == 'polynomial':
return tf.train.polynomial_decay(FLAGS.learning_rate,
global_step,
decay_steps,
FLAGS.end_learning_rate,
power=1.0,
cycle=False,
name='polynomial_decay_learning_rate')
else:
raise ValueError('learning_rate_decay_type [%s] was not recognized' %
FLAGS.learning_rate_decay_type)
# 优化器算法的选择
def _configure_optimizer(learning_rate):
"""Configures the optimizer used for training.
Args:
learning_rate: A scalar or `Tensor` learning rate.
Returns:
An instance of an optimizer.
Raises:
ValueError: if FLAGS.optimizer is not recognized.
"""
if FLAGS.optimizer == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(
learning_rate,
rho=FLAGS.adadelta_rho,
epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(
learning_rate,
initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)
elif FLAGS.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate,
beta1=FLAGS.adam_beta1,
beta2=FLAGS.adam_beta2,
epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'ftrl':
optimizer = tf.train.FtrlOptimizer(
learning_rate,
learning_rate_power=FLAGS.ftrl_learning_rate_power,
initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,
l1_regularization_strength=FLAGS.ftrl_l1,
l2_regularization_strength=FLAGS.ftrl_l2)
elif FLAGS.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(
learning_rate,
momentum=FLAGS.momentum,
name='Momentum')
elif FLAGS.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate,
decay=FLAGS.rmsprop_decay,
momentum=FLAGS.rmsprop_momentum,
epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise ValueError('Optimizer [%s] was not recognized' % FLAGS.optimizer)
return optimizer
# 从checkpoint中恢复已保存的模型继续训练，模型从checkpoint处开始微调
def _get_init_fn():
"""Returns a function run by the chief worker to warm-start the training.
Note that the init_fn is only run when initializing the model during the very
first global step.
Returns:
An init function run by the supervisor.
"""
if FLAGS.checkpoint_path is None:
return None
# 如果checkpoint已存在于train_dir，忽略
if tf.train.latest_checkpoint(FLAGS.train_dir):
tf.logging.info(
'Ignoring --checkpoint_path because a checkpoint already exists in %s'
% FLAGS.train_dir)
return None
# 不需要训练的变量
exclusions = []
if FLAGS.checkpoint_exclude_scopes:
exclusions = [scope.strip()
for scope in FLAGS.checkpoint_exclude_scopes.split(',')]
# 获取需要训练的变量
variables_to_restore = []
for var in slim.get_model_variables():
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
break
else:
variables_to_restore.append(var)
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
# 最后一次保存的断点文件
checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Fine-tuning from %s' % checkpoint_path)
return slim.assign_from_checkpoint_fn(
checkpoint_path,
variables_to_restore,
ignore_missing_vars=FLAGS.ignore_missing_vars)
# 获取需要训练的变量
def _get_variables_to_train():
"""Returns a list of variables to train.
Returns:
A list of variables to train by the optimizer.
"""
if FLAGS.trainable_scopes is None:
return tf.trainable_variables()
else:
scopes = [scope.strip() for scope in FLAGS.trainable_scopes.split(',')]
variables_to_train = []
for scope in scopes:
variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
variables_to_train.extend(variables)
return variables_to_train
def main(_):
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
# 为日志设置入口
tf.logging.set_verbosity(tf.logging.INFO)
# 创建全局默认图
with tf.Graph().as_default():
# 模型的基本参数配置
deploy_config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.worker_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# 用模型参数初始化设备，创建global_step
with tf.device(deploy_config.variables_device()):
global_step = tf.train.create_global_step()
# 指定数据集
dataset = dataset_factory.get_dataset(
FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
# 指定训练网络
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weight_decay=FLAGS.weight_decay,
is_training=True)
# 返回指定name的网络，用来处理给定图片
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=True)
# 从数据集导入数据
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
# 指定图片大小
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
# 用前面指定的网络，处理给定图片
image = image_preprocessing_fn(image, train_image_size, train_image_size)
# 将数据处理成批数据
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
# 将标签进行one-hot编码
labels = slim.one_hot_encoding(
labels, dataset.num_classes - FLAGS.labels_offset)
# 用数据初始化一个队列
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
# 定义模型
def clone_fn(batch_queue):
# 允许创建多个克隆网络，平行处理数据
images, labels = batch_queue.dequeue()
logits, end_points = network_fn(images)
# 指定损失函数
if 'AuxLogits' in end_points:
slim.losses.softmax_cross_entropy(
end_points['AuxLogits'], labels,
label_smoothing=FLAGS.label_smoothing, weights=0.4,
scope='aux_loss')
slim.losses.softmax_cross_entropy(
logits, labels, label_smoothing=FLAGS.label_smoothing, weights=1.0)
return end_points
# 初始化summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# 克隆一个网络并行运行
clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
# 收集克隆网络的参数
first_clone_scope = deploy_config.clone_scope(0)
# tf.GraphKeys.UPDATE_OPS是tensorflow的计算图中内置的一个集合
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
# 为模型配置滑动平均，防止抖动
if FLAGS.moving_average_decay:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
else:
moving_average_variables, variable_averages = None, None
if FLAGS.quantize_delay >= 0:
tf.contrib.quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay)
# 设置优化过程
with tf.device(deploy_config.optimizer_device()):
# 选择学习率衰减算法
learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
# 选择优化器
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.sync_replicas:
# 创建sync_replicas优化器
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
total_num_replicas=FLAGS.worker_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables)
elif FLAGS.moving_average_decay:
# 滑动平均更新update_ops变量
update_ops.append(variable_averages.apply(moving_average_variables))
# 需要训练的变量
variables_to_train = _get_variables_to_train()
# 计算clone网络的损失和梯度
total_loss, clones_gradients = model_deploy.optimize_clones(
clones,
optimizer,
var_list=variables_to_train)
# 将total_loss加入summary中
summaries.add(tf.summary.scalar('total_loss', total_loss))
# 对变量进行梯度更新
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
# tf.control_dependencies([update_op]) 函数保证先完成update_op操作
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
# Merge all summaries
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# 训练模型
slim.learning.train(
train_tensor,
logdir=FLAGS.train_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
init_fn=_get_init_fn(),
summary_op=summary_op,
number_of_steps=FLAGS.max_number_of_steps,
log_every_n_steps=FLAGS.log_every_n_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
sync_optimizer=optimizer if FLAGS.sync_replicas else None)
if __name__ == '__main__':
tf.app.run()
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