def __init__(self,net,data,tf_records=False):
self.net = net
self.data = data
self.tf_records = tf_records
self.batch_size = cfg.BATCH_SIZE
self.clr = CLR_EXP_RANGE()
self.log_dir = cfg.LOG_DIR
self.model_cls_dir = cfg.CLS_MODEL_DIR
self.model_det_dir = cfg.DET_MODEL_DIR
self.learning_rate = tf.placeholder(tf.float32)
self.re_train = True
tf.summary.scalar('learning_rate',self.learning_rate)
self.optimizer = self.optimizer_bn(lr=self.learning_rate,loss=self.net.total_loss)
if isHasGpu():
gpu_option = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True,gpu_options=gpu_option)
else:
config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.summary_op = tf.summary.merge_all()
n_time = time.strftime("%Y-%m-%d %H-%M", time.localtime())
self.writer = tf.summary.FileWriter(os.path.join(self.log_dir, n_time),self.sess.graph)
self.saver = tf.train.Saver(max_to_keep=4)
def __init__(self, net):
self.net = net
self.model_cls_dir = cfg.CLS_MODEL_DIR
if isHasGpu():
gpu_option = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_option)
else:
config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=4)
def __init__(self, net):
self.net = net
self.image_size = cfg.DET_IMAGE_SIZE
self.model_det_dir = cfg.DET_MODEL_DIR
self.cell_size = cfg.CELL_SIZE
self.boxes_per_cell = cfg.PER_CELL_CHECK_BOXES
self.classes = cfg.VOC07_CLASS
self.num_class = len(self.classes)
self.threshold = cfg.THRESHOLD
self.iou_threshold = cfg.IOU_THRESHOLD
self.boundary1 = self.cell_size * self.cell_size * self.num_class
self.boundary2 = self.boundary1 + self.cell_size * self.cell_size * self.boxes_per_cell
if isHasGpu():
gpu_option = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_option)
else:
config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=4)
def run():
model_dir = ''
logdir = ''
img_prob = [224, 224, 3]
num_cls = 10
is_train = False
is_load_model = False
is_stop_test_eval = True
BATCH_SIZE = 100
EPOCH_NUM = 150
ITER_NUM = 500 # 50000 / 100
LEARNING_RATE_VAL = 0.001
if utils.isLinuxSys():
logdir = r''
model_dir = r''
else:
model_dir = r'D:\DataSets\cifar\cifar\model_flie\inceptionv2'
logdir = r'D:\DataSets\cifar\cifar\logs\train\inceptionv2'
if is_train:
train_img_batch, train_label_batch = pre_pro.get_cifar10_batch(is_train = True, batch_size=BATCH_SIZE, num_cls=num_cls,img_prob=[224,224,3])
test_img_batch, test_label_batch = pre_pro.get_cifar10_batch(is_train=False,batch_size=BATCH_SIZE,num_cls=num_cls,img_prob=[224,224,3])
inputs = tf.placeholder(tf.float32,[None, img_prob[0], img_prob[1], img_prob[2]])
labels = tf.placeholder(tf.float32,[None, num_cls])
is_training = tf.placeholder(tf.bool)
LEARNING_RATE = tf.placeholder(tf.float32)
calc_lr = LR_Tools.CLR_EXP_RANGE()
# layer_batch_norm_params = {
# 'training': is_training
# }
logits = InceptionV2.V2_slim(inputs, num_cls, is_training=is_training)
train_loss = coms.loss(logits,labels)
train_optim = coms.optimizer_bn(lr=LEARNING_RATE,loss=train_loss)
train_eval = coms.evaluation(logits,labels)
saver = tf.train.Saver(max_to_keep=4)
max_acc = 0.
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
if utils.isHasGpu():
dev = '/gpu:0'
else:
dev = '/cpu:0'
with tf.device(dev):
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess= sess, coord=coord)
try:
if is_train:
if is_load_model:
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
print('model load successful ...')
else:
print('model load failed ...')
return
n_time = time.strftime("%Y-%m-%d %H-%M", time.localtime())
logdir = os.path.join(logdir, n_time)
writer = tf.summary.FileWriter(logdir, sess.graph)
for epoch in range(EPOCH_NUM):
if coord.should_stop():
print('coord should stop ...')
break
for step in range(1,ITER_NUM+1):
if coord.should_stop():
print('coord should stop ...')
break
script_kv = utils.readFile('train_script')
if script_kv != None:
if 'iter' in script_kv.keys():
# 找到对应step，准备保存模型参数，或者修改学习速率
if int(script_kv['iter']) == step:
if 'lr' in script_kv.keys():
LEARNING_RATE_VAL = float(script_kv['lr'])
print('read train_scrpit file and update lr to {}'.format(LEARNING_RATE_VAL))
if 'save' in script_kv.keys():
saver.save(sess,model_dir + '/' + 'cifar10_{}_step_{}.ckpt'.format(str(epoch),str(step)),global_step=step)
print('read train_script file and save model successful ...')
LEARNING_RATE_VAL = calc_lr.calc_lr(step,ITER_NUM,0.001,0.01,gamma=0.9998)
# LEARNING_RATE_VAL = coms.clr(step,2*ITER_NUM,0.001,0.006)
batch_train_img, batch_train_label = sess.run([train_img_batch,train_label_batch])
_, batch_train_loss, batch_train_acc = sess.run([train_optim,train_loss,train_eval],feed_dict={inputs:batch_train_img,
labels:batch_train_label,
LEARNING_RATE:LEARNING_RATE_VAL,
is_training:is_train})
global_step = int(epoch * ITER_NUM + step + 1)
print("epoch %d , step %d train end ,loss is : %f ,accuracy is %f ... ..." % (epoch, step, batch_train_loss, batch_train_acc))
train_summary = tf.Summary(value=[tf.Summary.Value(tag='train_loss',simple_value=batch_train_loss)
,tf.Summary.Value(tag='train_batch_accuracy',simple_value=batch_train_acc)
,tf.Summary.Value(tag='learning_rate',simple_value=LEARNING_RATE_VAL)])
writer.add_summary(train_summary,global_step)
writer.flush()
if is_stop_test_eval:
if not is_load_model:
if epoch < 3:
continue
if step % 100 == 0:
print('test sets evaluation start ...')
ac_iter = int(10000/BATCH_SIZE) # cifar-10测试集数量10000张
ac_sum = 0.
loss_sum = 0.
for ac_count in range(ac_iter):
batch_test_img, batch_test_label = sess.run([test_img_batch,test_label_batch])
test_loss, test_accuracy = sess.run([train_loss,train_eval],feed_dict={inputs:batch_test_img,
labels:batch_test_label,
is_training:False})
ac_sum += test_accuracy
loss_sum += test_loss
ac_mean = ac_sum / ac_iter
loss_mean = loss_sum / ac_iter
print('epoch {} , step {} , accuracy is {}'.format(str(epoch),str(step),str(ac_mean)))
test_summary = tf.Summary(
value=[tf.Summary.Value(tag='test_loss', simple_value=loss_mean)
, tf.Summary.Value(tag='test_accuracy', simple_value=ac_mean)])
writer.add_summary(test_summary,global_step=global_step)
writer.flush()
if ac_mean >= max_acc:
max_acc = ac_mean
saver.save(sess, model_dir + '/' + 'cifar10_{}_step_{}.ckpt'.format(str(epoch),str(step)),global_step=step)
print('max accuracy has reaching ,save model successful ...')
# print('saving last model ...')
# saver.save(sess, model_dir + '/' + 'cifar10_last.ckpt')
print('train network task was run over')
else:
model_file = tf.train.latest_checkpoint(model_dir)
saver.restore(sess, model_file)
cls_list = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',
'truck']
for i in range(1, 11):
name = str(i) + '.jpg'
img = cv2.imread(name)
img = cv2.resize(img, (32, 32))
img = cv2.resize(img, (224, 224))
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img / 255.
img = np.array([img])
res = sess.run(logits, feed_dict={inputs:img , is_training:False})
# print(res)
print('{}.jpg detect result is : '.format(str(i)) + cls_list[np.argmax(res)] )
# img = cv2.imread("16_dog.png")
# img = cv2.resize(img, (224, 224))
# # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = img / 255.
# img = np.array([img])
# res = sess.run(logits, feed_dict={inputs: img, is_training: False})
# print(res)
# print('{}.jpg detect result is : '.format(str('16_dog')) + cls_list[np.argmax(res)])
#
# img = cv2.imread("5_frog.png")
# img = cv2.resize(img, (224, 224))
# # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = img / 255.
# img = np.array([img])
# res = sess.run(logits, feed_dict={inputs: img, is_training: False})
# print(res)
# print('{}.jpg detect result is : '.format(str('5_frog')) + cls_list[np.argmax(res)])
#
# img = cv2.imread("11_truck.png")
# img = cv2.resize(img, (224, 224))
# # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = img / 255.
# img = np.array([img])
# res = sess.run(logits, feed_dict={inputs: img, is_training: False})
# print(res)
# print('{}.jpg detect result is : '.format(str('11_truck')) + cls_list[np.argmax(res)])
except tf.errors.OutOfRangeError:
print('done training -- opoch files run out of ...')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def main():
train_dir = ''
test_dir = ''
model_dir = ''
logdir = ''
num_cls = 2
is_train = True
is_load_model = True
BATCH_SZIE = 100
EPOCH_NUM = 20
ITER_NUM = 20000 # 60000 / 50
KEEP_PROB = 0.5
if utils.isLinuxSys():
train_dir = r'/home/zhuhao/DataSets/CatAndDog/train'
test_dir = r'/home/zhuhao/DataSets/CatAndDog/test'
model_dir = r'/home/zhuhao/DataSets/CatAndDog/model_file'
logdir = r'/home/zhuhao/DataSets/CatAndDog/logs/train'
if utils.isWinSys():
train_dir = r'D:\DataSets\CatAndDog\train'
test_dir = r'D:\DataSets\CatAndDog\test'
model_dir = r'D:\DataSets\CatAndDog\model_file\alexnet'
logdir = r'D:\DataSets\CatAndDog\logs\train'
train_img_list, train_label_list = pre_pro.get_dogcat_img(train_dir)
train_img_batch, train_label_batch = pre_pro.get_batch(
train_img_list,
train_label_list,
227,
227,
batch_size=BATCH_SZIE,
capacity=2000)
train_label_batch = tf.one_hot(train_label_batch, depth=num_cls)
test_img_list, test_label_list = pre_pro.get_dogcat_img(test_dir)
test_img_batch, test_label_batch = pre_pro.get_batch(test_img_list,
test_label_list,
227,
227,
batch_size=BATCH_SZIE,
capacity=2000)
test_label_batch = tf.one_hot(test_label_batch, depth=num_cls)
inputs = tf.placeholder(tf.float32, [None, 227, 227, 3])
labels = tf.placeholder(tf.float32, [None, num_cls])
logits = AlexNet.AlexNetModel_slim(inputs=inputs,
num_cls=num_cls,
keep_prob=KEEP_PROB)
train_loss = coms.loss(logits, labels)
train_eval = coms.evaluation(logits, labels)
train_optim = coms.optimizer(lr=5e-4, loss=train_loss, fun='mm')
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=4)
max_acc = 0.
with tf.Session() as sess:
if utils.isHasGpu():
dev = "/gpu:0"
else:
dev = "/cpu:0"
with tf.device(dev):
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
if is_train:
if is_load_model:
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('model load successful')
else:
print('model load falid')
return
n_time = time.strftime("%Y-%m-%d %H-%M", time.localtime())
logdir = os.path.join(logdir, n_time)
writer = tf.summary.FileWriter(logdir, sess.graph)
for epoch in range(EPOCH_NUM):
if coord.should_stop():
break
for step in range(ITER_NUM):
if coord.should_stop():
break
batch_train_im, batch_train_label = sess.run(
[train_img_batch, train_label_batch])
batch_test_im, batch_test_label = sess.run(
[test_img_batch, test_label_batch])
_, loss, w_summary = sess.run(
[train_optim, train_loss, summary_op],
feed_dict={
inputs: batch_train_im,
labels: batch_train_label
})
writer.add_summary(w_summary,
(epoch * ITER_NUM + step))
print(
"epoch %d , step %d train end ,loss is : %f ... ..."
% (epoch, step, loss))
if epoch == 0 and step < (ITER_NUM / 3):
continue
if step % 200 == 0:
print('evaluation start ... ...')
ac_iter = int(
(len(test_img_list) / BATCH_SZIE))
ac_sum = 0.
for ac_count in range(ac_iter):
accuracy = sess.run(train_eval,
feed_dict={
inputs:
batch_test_im,
labels:
batch_test_label
})
ac_sum = ac_sum + accuracy
ac_mean = ac_sum / ac_iter
print('epoch %d , step %d , accuracy is %f' %
(epoch, step, ac_mean))
if ac_mean >= max_acc:
max_acc = ac_mean
saver.save(sess,
model_dir + '/' + 'dogcat' +
str(epoch) + '_step' +
str(step) + '.ckpt',
global_step=step + 1)
print('saving last model ...')
saver.save(sess, model_dir + '/cifar10_last.ckpt')
print('train network task was run over')
else:
pass
except tf.errors.OutOfRangeError:
print('done trainning --epoch files run out of')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run():
model_dir = ''
logdir = ''
img_prob = [32, 32, 3]
num_cls = 10
is_train = False
is_load_model = True
is_stop_test_eval = False
BATCH_SIZE = 100
EPOCH_NUM = 50
ITER_NUM = 500 # 50000 / 100
LEARNING_RATE_VAL = 0.001
if utils.isLinuxSys():
logdir = r''
model_dir = r''
else:
model_dir = r'D:\DataSets\cifar\cifar\model_flie\vgg16'
logdir = r'D:\DataSets\cifar\cifar\logs\train'
train_img_batch, train_label_batch = pre_pro.get_cifar10_batch(
is_train=True,
batch_size=BATCH_SIZE,
num_cls=num_cls,
img_prob=[32, 32, 3])
test_img_batch, test_label_batch = pre_pro.get_cifar10_batch(
is_train=False,
batch_size=BATCH_SIZE,
num_cls=num_cls,
img_prob=[32, 32, 3])
inputs = tf.placeholder(tf.float32,
[None, img_prob[0], img_prob[1], img_prob[2]])
labels = tf.placeholder(tf.float32, [None, num_cls])
LEARNING_RATE = tf.placeholder(tf.float32)
logits = VggNet.VGG16_slim(inputs, num_cls, 0)
train_loss = coms.loss(logits, labels)
train_optim = coms.optimizer(lr=LEARNING_RATE, loss=train_loss, fun='mm')
train_eval = coms.evaluation(logits, labels)
saver = tf.train.Saver(max_to_keep=4)
max_acc = 0.
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
if utils.isHasGpu():
dev = '/gpu:0'
else:
dev = '/cpu:0'
with tf.device(dev):
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
if is_train:
if is_load_model:
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('model load successful ...')
else:
print('model load failed ...')
return
n_time = time.strftime("%Y-%m-%d %H-%M", time.localtime())
logdir = os.path.join(logdir, n_time)
writer = tf.summary.FileWriter(logdir, sess.graph)
for epoch in range(EPOCH_NUM):
if coord.should_stop():
print('coord should stop ...')
break
for step in range(1, ITER_NUM + 1):
if coord.should_stop():
print('coord should stop ...')
break
batch_train_img, batch_train_label = sess.run(
[train_img_batch, train_label_batch])
_, batch_train_loss, batch_train_acc = sess.run(
[train_optim, train_loss, train_eval],
feed_dict={
inputs: batch_train_img,
labels: batch_train_label,
LEARNING_RATE: LEARNING_RATE_VAL
})
global_step = int(epoch * ITER_NUM + step + 1)
print(
"epoch %d , step %d train end ,loss is : %f ,accuracy is %f ... ..."
% (epoch, step, batch_train_loss,
batch_train_acc))
train_summary = tf.Summary(value=[
tf.Summary.Value(
tag='train_loss',
simple_value=batch_train_loss),
tf.Summary.Value(tag='train_batch_accuracy',
simple_value=batch_train_acc)
])
writer.add_summary(train_summary, global_step)
writer.flush()
if is_stop_test_eval:
if not is_load_model:
if epoch == 0 and step < (ITER_NUM / 20):
continue
if step % 200 == 0:
print('test sets evaluation start ...')
ac_iter = int(
10000 / BATCH_SIZE) # cifar-10测试集数量10000张
ac_sum = 0.
loss_sum = 0.
for ac_count in range(ac_iter):
batch_test_img, batch_test_label = sess.run(
[test_img_batch, test_label_batch])
test_loss, test_accuracy = sess.run(
[train_loss, train_eval],
feed_dict={
inputs: batch_test_img,
labels: batch_test_label
})
ac_sum += test_accuracy
loss_sum += test_loss
ac_mean = ac_sum / ac_iter
loss_mean = loss_sum / ac_iter
print('epoch {} , step {} , accuracy is {}'.
format(str(epoch), str(step),
str(ac_mean)))
test_summary = tf.Summary(value=[
tf.Summary.Value(tag='test_loss',
simple_value=loss_mean),
tf.Summary.Value(tag='test_accuracy',
simple_value=ac_mean)
])
writer.add_summary(test_summary,
global_step=global_step)
writer.flush()
if ac_mean >= max_acc:
max_acc = ac_mean
saver.save(
sess,
model_dir + '/' +
'cifar10_{}_step_{}.ckpt'.format(
str(epoch), str(step)),
global_step=step)
print(
'max accuracy has reaching ,save model successful ...'
)
print('saving last model ...')
saver.save(sess, model_dir + '/' + 'cifar10_last.ckpt')
print('train network task was run over')
else:
model_file = tf.train.latest_checkpoint(model_dir)
saver.restore(sess, model_file)
cls_list = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',
'frog', 'horse', 'ship', 'truck'
]
for i in range(1, 11):
name = str(i) + '.jpg'
img = cv2.imread(name)
img = cv2.resize(img, (32, 32))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = img / 255.
img = np.array([img])
res = sess.run(logits, feed_dict={inputs: img})
# print(res)
print('{}.jpg detect result is : '.format(str(i)) +
cls_list[np.argmax(res)])
except tf.errors.OutOfRangeError:
print('done training -- opoch files run out of ...')
finally:
coord.request_stop()
coord.join(threads)
sess.close()