def addClassDir(projectname, dirname):
train_dir = PathProxy.getProjectTrainDir(projectname)
PathProxy.mkdir(train_dir + dirname)
test_dir = PathProxy.getProjectTestDir(projectname)
PathProxy.mkdir(test_dir + dirname)
pass
def getClasses(projectname):
train_path = PathProxy.getProjectTrainDir(projectname)
for maindir, pdir, etcfile in os.walk(train_path):
if maindir == train_path:
rtn = {}
for p in pdir:
"""
ps = p.split('_')
if len(ps) == 2:
try:
mark = int(ps[0])
rtn[ps[0]] = ps[1]
except Exception as error:
a = 0
"""
rtn[str(len(rtn))] = p
return rtn
return {}
def initTrain(self, projectname, tag):
param = CNNDivParam(projectname, tag)
modeltype = param.Type()
modelpath = PathProxy.getModelTagDir(projectname, tag)
if modeltype == "cnn-div":
train_dir = PathProxy.getProjectTrainDir(projectname)
train_images, train_labels = CoachProxy.getInput(
modeltype, param, train_dir)
train_images_batch, train_labels_batch = CoachProxy.getBatch(
modeltype, param, train_images, train_labels)
x = tf.placeholder(shape=train_images_batch.shape,
dtype=train_images_batch.dtype,
name="data_batch")
y = tf.placeholder(shape=train_labels_batch.shape,
dtype=train_labels_batch.dtype,
name="label_batch")
train_logit = CoachProxy.getLogit(modeltype, param, x)
train_loss = CoachProxy.getLoss(modeltype, param, train_logit, y)
train_op = CoachProxy.getTrain(modeltype, param, train_loss)
train_acc = CoachProxy.getEnvaluation(modeltype, param,
train_logit, y)
self.train_images_batch = train_images_batch
self.train_labels_batch = train_labels_batch
self.train_logit = train_logit
self.train_loss = train_loss
self.train_op = train_op
self.train_acc = train_acc
test_dir = PathProxy.getProjectTestDir(projectname)
test_images, test_labels = CoachProxy.getInput(
modeltype, param, test_dir)
self.test_dir = test_dir
self.test_images = test_images
if len(test_images) > 0:
test_images_batch, test_labels_batch = CoachProxy.getBatch(
modeltype, param, test_images, test_labels)
self.test_images_batch = test_images_batch
self.test_labels_batch = test_labels_batch
#self.summary_op = tf.summary.merge([scalar_tra_loss,scalar_tra_acc,scalar_test_loss,scalar_test_acc])
self.dict_summary = {
"train/loss": 0.0,
"train/accuracy": 0.0,
"test/loss": 0.0,
"test/accuracy": 0.0
}
list_merge = []
for k in self.dict_summary:
sh = tf.placeholder(shape=[], dtype=tf.float32, name=k)
sc = tf.summary.scalar(k, sh)
list_merge.append(sc)
self.summary_op = tf.summary.merge(list_merge)
self.savedir = os.path.join(modelpath, "saver")
self.logdir = os.path.join(modelpath, "log")
self.saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(self.savedir)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
self.sess.graph.as_default()
if ckpt and ckpt.model_checkpoint_path:
model_name = ckpt.model_checkpoint_path.split('\\')[-1]
global_step = model_name.split('-')[-1]
saverpath = os.path.join(self.savedir, model_name)
self.saver.restore(self.sess, saverpath)
self.curstep = int(global_step)
print('Loading success, global_step is %s' % global_step)
else:
PathProxy.mkdir(self.savedir)
PathProxy.mkdir(self.logdir)
self.curstep = 0
print("Can not load ckpt")
self.coord = tf.train.Coordinator()
self.threads = tf.train.start_queue_runners(sess=self.sess,
coord=self.coord)
self.period = param.Period()
self.saveperiod = param.SavePeriod()
self.maxperiod = param.MaxPeriod()
self.runflag = True
self.writer = tf.summary.FileWriter(self.logdir, self.sess.graph)
#print(CoachProxy.getInput(modeltype,param,train_dir))
return True