def extract_features(self, inputs):
with tf.variable_scope('fcn_16s'):
with slim.arg_scope(
vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
fc8_logits, end_points = vgg.vgg_16(
inputs=inputs,
num_classes=self._num_classes,
is_training=self._is_training,
spatial_squeeze=self._spatial_squeeze,
fc_conv_padding='SAME',
global_pool=self._global_pool)
upsampled_fc8_logits = slim.conv2d_transpose(
fc8_logits, self._num_classes, kernel_size=[4, 4],
stride=[2, 2], padding='SAME', activation_fn=None,
normalizer_fn=None, scope='deconv1')
pool4 = end_points['fcn_16s/vgg_16/pool4']
pool4_logits = slim.conv2d(
pool4, self._num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='pool4_conv')
fused_logits = tf.add(pool4_logits, upsampled_fc8_logits,
name='fused_logits')
logits = tf.image.resize_bilinear(
fused_logits, tf.shape(inputs)[1:3], align_corners=True)
return logits
def predict(models_path, image_dir, labels_filename, labels_nums, data_format):
[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')
with slim.arg_scope(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(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
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, '*.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 = sess.run([out], feed_dict={input_images: im})
print "image_path:{},pre_score:{}".format(image_path, pre_score)
sess.close()
def extract_features(self, inputs):
with slim.arg_scope(
vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
with tf.variable_scope('fcn_32s'):
fc8_logits, end_points = vgg.vgg_16(
inputs=inputs,
num_classes=self._num_classes,
is_training=self._is_training,
spatial_squeeze=self._spatial_squeeze,
fc_conv_padding='SAME',
global_pool=self._global_pool)
logits = tf.image.resize_bilinear(
fc8_logits, tf.shape(inputs)[1:3], align_corners=True)
return logits
def predict(models_path, image_dir, labels_filename, labels_nums, data_format):
[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(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(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)
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})
max_score = pre_score[0, pre_label]
print("{} is: pre labels:{},name:{} score: {}".format(
image_path, pre_label,
list(labels_filename.keys())[list(
labels_filename.values()).index(pre_label)], max_score))
sess.close()
def train(train_filename, train_images_dir, train_log_step, train_param,
val_filename, val_images_dir, 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
# # 从record中读取图片和labels数据
tf_image, tf_labels = read_images(train_filename,
train_images_dir,
data_shape,
shuffle=True,
type='normalization')
train_images_batch, train_labels_batch = get_batch_images(
tf_image,
tf_labels,
batch_size=batch_size,
labels_nums=labels_nums,
one_hot=False,
shuffle=True)
# Define the model:
with slim.arg_scope(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(inputs=input_images,
num_classes=labels_nums,
dropout_keep_prob=keep_prob,
is_training=is_training)
loss = tf.reduce_sum(tf.squared_difference(x=out, y=input_labels))
# loss1=tf.squared_difference(x=out,y=input_labels)
# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=y))
train_op = tf.train.AdamOptimizer(learning_rate=base_lr).minimize(loss)
# tf.losses.add_loss(loss1)
# # 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)
# # 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)
saver = tf.train.Saver(max_to_keep=4)
max_acc = 0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(max_steps + 1):
batch_input_images, batch_input_labels = sess.run(
[train_images_batch, train_labels_batch])
_, train_loss = sess.run(
[train_op, loss],
feed_dict={
input_images: batch_input_images,
input_labels: batch_input_labels,
keep_prob: 0.5,
is_training: True
})
if i % train_log_step == 0:
print("%s: Step [%d] train Loss : %f" %
(datetime.now(), i, train_loss))
# # train测试(这里仅测试训练集的一个batch)
# if i%train_log_step == 0:
# train_acc = sess.run(accuracy, feed_dict={input_images:batch_input_images,
# input_labels: batch_input_labels,
# keep_prob:1.0, is_training: False})
# print "%s: Step [%d] train Loss : %f, training accuracy : %g" % (datetime.now(), i, train_loss, train_acc)
#
# # val测试(测试全部val数据)
# if i%val_log_step == 0:
# _, train_loss = sess.run([train_step, loss], feed_dict={input_images: batch_input_images,
# input_labels: batch_input_labels,
# keep_prob: 1.0, is_training: False})
# print "%s: Step [%d] val Loss : %f, val accuracy : %g" % (datetime.now(), i, mean_loss, mean_acc)
#
# 模型保存:每迭代snapshot次或者最后一次保存模型
if (i % snapshot == 0 and i > 0) or i == max_steps:
print('-----save:{}-{}'.format(snapshot_prefix, i))
saver.save(sess, snapshot_prefix, global_step=i)
# # 保存val准确率最高的模型
# if mean_acc>max_acc and mean_acc>0.5:
# max_acc=mean_acc
# path = os.path.dirname(snapshot_prefix)
# best_models=os.path.join(path,'best_models_{}_{:.4f}.ckpt'.format(i,max_acc))
# print('------save:{}'.format(best_models))
# saver.save(sess, best_models)
coord.request_stop()
coord.join(threads)
def train(data_csv_path_train, train_log_step, train_param, data_csv_path_val,
val_log_step, labels_nums, data_shape, snapshot, snapshot_prefix):
'''
:param data_csv_path_train: 训练的csv文件
:param train_log_step: 显示训练过程log信息间隔
:param train_param: train参数
:param data_csv_path_val: 验证的val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
with open(dataset_csv_path_train, 'r') as f:
train_nums = len(f.readlines())
with open(dataset_csv_path_val, 'r') as v:
val_nums = len(v.readlines())
print('train nums:%d,val nums:%d' % (train_nums, val_nums))
train_batch = data_loader.load_data(data_csv_path_train,
image_type=args.image_type,
image_size_before_crop=resize_height,
labels_nums=labels_nums)
train_images_batch = train_batch['image']
train_labels_batch = train_batch['label']
# print('......................................................')
# print(train_images_batch)
# print(train_labels_batch)
# val数据,验证数据可以不需要打乱数据
val_batch = data_loader.load_data(data_csv_path_val,
image_type=args.image_type,
image_size_before_crop=resize_height,
labels_nums=labels_nums,
do_shuffle=False)
val_images_batch = val_batch['image']
val_labels_batch = val_batch['label']
# Define the model:
with slim.arg_scope(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(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=False) #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=base_lr,momentum= 0.9)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)
# # 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)
#################### 改这里 ########################################
#导入模型
from slim.nets.vgg import vgg_16
from slim.nets.vgg import vgg_arg_scope
#要测试的如数尺寸
input_size = [224, 224, 3]
# 给模型的输入口
inputs = tf.placeholder(tf.float32,
shape=(1, input_size[0], input_size[1], input_size[2]))
num_class = 32
#建造模型
with slim.arg_scope(vgg_arg_scope()):
net, end_points = vgg_16(inputs, num_class)
#只要有logits就可以评估了，中间过程layers_end_points有的话就显示，没有就不显示
logits = net
layers_end_points = end_points
#################### end ###########################################
def show_parament_numbers():
from functools import reduce
from operator import mul
def get_num_params():
num_params = 0
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(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(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)
# 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 = 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_op = optimizer.minimize(loss, global_step)
# train_op = slim.learning.create_train_op(loss, optimizer,global_step=global_step)
saver = tf.train.Saver()
max_acc = 0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(max_steps + 1):
batch_input_images, batch_input_labels = sess.run(
[train_images_batch, train_labels_batch])
_, train_loss = sess.run(
[train_op, loss],
feed_dict={
input_images: batch_input_images,
input_labels: batch_input_labels,
keep_prob: 0.5,
is_training: True
})
# train测试(这里仅测试训练集的一个batch)
if i % train_log_step == 0:
train_acc = sess.run(accuracy,
feed_dict={
input_images: batch_input_images,
input_labels: batch_input_labels,
keep_prob: 1.0,
is_training: False
})
print(
"%s: Step [%d] train Loss : %f, training accuracy : %g" %
(datetime.now(), i, train_loss, train_acc))
# val测试(测试全部val数据)
if i % val_log_step == 0:
mean_loss, mean_acc = net_evaluation(sess, loss, accuracy,
val_images_batch,
val_labels_batch,
val_nums)
print("%s: Step [%d] val Loss : %f, val accuracy : %g" %
(datetime.now(), i, mean_loss, mean_acc))
# 模型保存:每迭代snapshot次或者最后一次保存模型
if (i % snapshot == 0 and i > 0) or i == max_steps:
print('-----save:{}-{}'.format(snapshot_prefix, i))
saver.save(sess, snapshot_prefix, global_step=i)
# 保存val准确率最高的模型
if mean_acc > max_acc and mean_acc > 0.5:
max_acc = mean_acc
path = os.path.dirname(snapshot_prefix)
best_models = os.path.join(
path, 'best_models_{}_{:.4f}.ckpt'.format(i, max_acc))
print('------save:{}'.format(best_models))
saver.save(sess, best_models)
coord.request_stop()
coord.join(threads)
