def predict(models_path, image_dir, labels_filename, labels_nums, data_format):
"""
加载预测的图片与标签
加载模型
定义softmax
定义预测最大值与下标
回复模型参数W
Session
:param models_path:
:param image_dir:
:param labels_filename:
:param labels_nums:
:param data_format:
:return:
"""
[batch_size, resize_height, resize_width, depths] = data_format
labels = np.loadtxt(labels_filename, str, delimiter='\t')
input_images = tf.placeholder(
dtype=tf.float32,
shape=[None, resize_height, resize_width, depths],
name='input')
# 其他模型预测请修改这里
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
out, end_points = inception_v3.inception_v3(inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=1.0,
is_training=False)
# 将输出结果进行softmax分布,再求最大概率所属类别
score = tf.nn.softmax(out, name='pre')
class_id = tf.argmax(score, 1)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path) #恢复模型
#glob模块是最简单的模块之一，内容非常少。用它可以查找符合特定规则的文件路径名
##获取指定目录下的所有图片
# print glob.glob(r"E:/Picture/*/*.jpg")
#获取上级目录的所有.py文件
# print glob.glob(r'../*.py') #相对路径
#意思就是image_dir路径下的所有。jpg文件名
images_list = glob.glob(os.path.join(image_dir, '*.jpg'))
for image_path in images_list:
im = read_image(image_path,
resize_height,
resize_width,
normalization=True)
im = im[np.newaxis, :]
# pred = sess.run(f_cls, feed_dict={x:im, keep_prob:1.0})
pre_score, pre_label = sess.run([score, class_id],
feed_dict={input_images: im})
#pre_score所有评分， pre_label最大分数的位置
max_score = pre_score[0, pre_label] #最大评分的值
print("{} is: pre labels:{},name:{} score: {}".format(
image_path, pre_label, labels[pre_label], max_score))
sess.close()
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v3.inception_v3_arg_scope()):
inception_v3.inception_v3_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(21802784, total_params)
def predict_single_image(models_path, image_path, labels_filename,
label_id_filename, data_format):
[batch_size, resize_height, resize_width, depths] = data_format
labels = np.loadtxt(labels_filename, str, delimiter='\t', encoding='utf-8')
labels_id = np.loadtxt(label_id_filename,
str,
delimiter='\t',
encoding='utf-8')
labels_nums = labels_id.size
input_images = tf.placeholder(
dtype=tf.float32,
shape=[None, resize_height, resize_width, depths],
name='input')
#其他模型预测请修改这里
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
out, end_points = inception_v3.inception_v3(inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=1.0,
is_training=False)
# 将输出结果进行softmax分布,再求最大概率所属类别
score = tf.nn.softmax(out, name='pre')
class_id = tf.argmax(score, 1)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
im = read_image(image_path,
resize_height,
resize_width,
normalization=True)
im = im[np.newaxis, :]
#pred = sess.run(f_cls, feed_dict={x:im, keep_prob:1.0})
pre_score, pre_label = sess.run([score, class_id],
feed_dict={input_images: im})
# print("pre_score:{}".format(pre_score))
# print("pre_label:{}".format(pre_label))
# pre_sort = np.argsort(-pre_score)
# print("pre_sort:{}".format(pre_sort))
# print(labels[pre_sort[0][0]])
# max_score=pre_score[0,pre_label]
# print("{} is: pre labels:{},name:{} score: {}".format(image_path,pre_label,labels[pre_label], max_score))
result = get_top_result(pre_score, labels, labels_id)
print("{} is result : {}".format(image_path, result))
sess.close()
def predict(models_path, image_dir, labels_nums, data_format, modelselect=0):
[batch_size, resize_height, resize_width, depths] = data_format
#labels = np.loadtxt(labels_filename, str, delimiter='\t')
input_images = tf.placeholder(
dtype=tf.float32,
shape=[None, resize_height, resize_width, depths],
name='input')
if modelselect == 0:
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
out, end_points = inception_v3.inception_v3(
inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=1.0,
is_training=False)
elif modelselect == 1:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
out, end_points = resnet_v2.resnet_v2_101(inputs=input_images,
num_classes=labels_nums,
is_training=False)
# 将输出结果进行softmax分布,再求最大概率所属类别
score = tf.nn.softmax(out, name='pre')
class_id = tf.argmax(score, 1)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
images_list = glob.glob(os.path.join(image_dir, '*.bmp'))
for image_path in images_list:
im = read_image(image_path,
resize_height,
resize_width,
normalization=True)
im = im[np.newaxis, :]
#pred = sess.run(f_cls, feed_dict={x:im, keep_prob:1.0})
pre_score, pre_label = sess.run([score, class_id],
feed_dict={input_images: im})
max_score = pre_score[0, pre_label]
print("{} is: pre labels:{},name:{} score: {}".format(
image_path, pre_label, chineseMJ[pre_label[0]], max_score))
sess.close()
def train(train_record_file, train_log_step, train_param, val_record_file,
val_log_step, labels_nums, data_shape, snapshot, snapshot_prefix):
'''
:param train_record_file: 训练的tfrecord文件
:param train_log_step: 显示训练过程log信息间隔
:param train_param: train参数
:param val_record_file: 验证的tfrecord文件
:param val_log_step: 显示验证过程log信息间隔
:param val_param: val参数
:param labels_nums: labels数
:param data_shape: 输入数据shape
:param snapshot: 保存模型间隔
:param snapshot_prefix: 保存模型文件的前缀名
:return:
'''
[base_lr, max_steps] = train_param
[batch_size, resize_height, resize_width, depths] = data_shape
# 获得训练和测试的样本数
train_nums = get_example_nums(train_record_file)
val_nums = get_example_nums(val_record_file)
print('train nums:%d,val nums:%d' % (train_nums, val_nums))
# 从record中读取图片和labels数据
# train数据,训练数据一般要求打乱顺序shuffle=True
train_images, train_labels = read_records(train_record_file,
resize_height,
resize_width,
type='normalization')
train_images_batch, train_labels_batch = get_batch_images(
train_images,
train_labels,
batch_size=batch_size,
labels_nums=labels_nums,
one_hot=True,
shuffle=True)
# val数据,验证数据可以不需要打乱数据
val_images, val_labels = read_records(val_record_file,
resize_height,
resize_width,
type='normalization')
val_images_batch, val_labels_batch = get_batch_images(
val_images,
val_labels,
batch_size=batch_size,
labels_nums=labels_nums,
one_hot=True,
shuffle=False)
# Define the model:
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
out, end_points = inception_v3.inception_v3(
inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=keep_prob,
is_training=is_training)
# Specify the loss function: tf.losses定义的loss函数都会自动添加到loss函数,不需要add_loss()了
tf.losses.softmax_cross_entropy(onehot_labels=input_labels,
logits=out) #添加交叉熵损失loss=1.6
# slim.losses.add_loss(my_loss)
loss = tf.losses.get_total_loss(
add_regularization_losses=True) #添加正则化损失loss=2.2
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1)),
tf.float32))
# Specify the optimization scheme:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)
# global_step = tf.Variable(0, trainable=False)
# learning_rate = tf.train.exponential_decay(0.05, global_step, 150, 0.9)
#
# optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
# # train_tensor = optimizer.minimize(loss, global_step)
# train_op = slim.learning.create_train_op(loss, optimizer,global_step=global_step)
# 在定义训练的时候, 注意到我们使用了`batch_norm`层时,需要更新每一层的`average`和`variance`参数,
# 更新的过程不包含在正常的训练过程中, 需要我们去手动像下面这样更新
# 通过`tf.get_collection`获得所有需要更新的`op`
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# 使用`tensorflow`的控制流, 先执行更新算子, 再执行训练
with tf.control_dependencies(update_ops):
# create_train_op that ensures that when we evaluate it to get the loss,
# the update_ops are done and the gradient updates are computed.
# train_op = slim.learning.create_train_op(total_loss=loss,optimizer=optimizer)
train_op = slim.learning.create_train_op(total_loss=loss,
optimizer=optimizer)
# 循环迭代过程
step_train(train_op, loss, accuracy, train_images_batch,
train_labels_batch, train_nums, train_log_step,
val_images_batch, val_labels_batch, val_nums, val_log_step,
snapshot_prefix, snapshot)
def train(
train_record_file,
train_log_step,
train_param,
val_record_file,
val_log_step,
labels_nums,
data_shape,
snapshot,
snapshot_prefix,
RESTORE_FROM_CHECKPOINT,
restore_steps,
):
'''
:param train_record_file: 训练的tfrecord文件
:param train_log_step: 显示训练过程log信息间隔
:param train_param: train参数
:param val_record_file: 验证的tfrecord文件
:param val_log_step: 显示验证过程log信息间隔
:param val_param: val参数
:param labels_nums: labels数
:param data_shape: 输入数据shape
:param snapshot: 保存模型间隔
:param snapshot_prefix: 保存模型文件的前缀名
:return:
'''
[base_lr, max_steps] = train_param
[batch_size, resize_height, resize_width, depths] = data_shape
# 获得训练和测试的样本数
# 运行过一遍可以直接拿数据跑 不需要再从tfrecord读
train_nums = get_example_nums(train_record_file)
val_nums = get_example_nums(val_record_file)
# train nums:77268,val nums:32973
# train_nums = 77268
# val_nums = 32973
print('train nums:%d,val nums:%d' % (train_nums, val_nums))
# 从record中读取图片和labels数据
# train数据,训练数据一般要求打乱顺序shuffle=True
train_images, train_labels = read_records(train_record_file,
resize_height,
resize_width,
type='normalization')
train_images_batch, train_labels_batch = get_batch_images(
train_images,
train_labels,
batch_size=batch_size,
labels_nums=labels_nums,
one_hot=True,
shuffle=True)
# val数据,验证数据可以不需要打乱数据
val_images, val_labels = read_records(val_record_file,
resize_height,
resize_width,
type='normalization')
val_images_batch, val_labels_batch = get_batch_images(
val_images,
val_labels,
batch_size=batch_size,
labels_nums=labels_nums,
one_hot=True,
shuffle=False)
# Define the model:
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
out, end_points = inception_v3.inception_v3(
inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=keep_prob,
is_training=is_training)
# Specify the loss function: tf.losses定义的loss函数都会自动添加到loss函数,不需要add_loss()了
tf.losses.softmax_cross_entropy(onehot_labels=input_labels,
logits=out) #添加交叉熵损失loss=1.6
# slim.losses.add_loss(my_loss)
loss = tf.losses.get_total_loss(
add_regularization_losses=True) #添加正则化损失loss=2.2
# Specify the optimization scheme:
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)
LEARNING_RATE_BASE = base_lr # 设置初始学习率为0.1
LEARNING_RATE_DECAY = 0.99 # 设置学习衰减率为0.99
LEARNING_RATE_STEP = 100 # 设置喂入多少轮BATCH_SIZE之后更新一次学习率,一般设置为 总样本数/BATCH_SIZE
global_steps = None
if RESTORE_FROM_CHECKPOINT:
global_steps = tf.Variable(restore_steps, trainable=False)
else:
global_steps = tf.Variable(0, trainable=False)
learing_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_steps,
LEARNING_RATE_STEP,
LEARNING_RATE_DECAY,
staircase=True)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)
optimizer = tf.train.AdamOptimizer(learing_rate, 0.9)
# train_op = slim.learning.create_train_op(loss, optimizer,global_step=global_steps)
# 在定义训练的时候, 注意到我们使用了`batch_norm`层时,需要更新每一层的`average`和`variance`参数,
# 更新的过程不包含在正常的训练过程中, 需要我们去手动像下面这样更新
# 通过`tf.get_collection`获得所有需要更新的`op`
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# 使用`tensorflow`的控制流, 先执行更新算子, 再执行训练
with tf.control_dependencies(update_ops):
# create_train_op that ensures that when we evaluate it to get the loss,
# the update_ops are done and the gradient updates are computed.
train_op = slim.learning.create_train_op(total_loss=loss,
optimizer=optimizer,
global_step=global_steps)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1)),
tf.float32))
step_train(train_op, loss, accuracy, max_steps, train_images_batch,
train_labels_batch, train_nums, train_log_step,
val_images_batch, val_labels_batch, val_nums, val_log_step,
snapshot_prefix, snapshot, RESTORE_FROM_CHECKPOINT,
restore_steps)
