def inference(self, X, dirr, train_status=False, Not_Realtest=True):
now = datetime.now().isoformat()[11:]
print("------- Testing begin: {} -------\n".format(now), flush=True)
if Not_Realtest:
x_list, y_list, tnum = X.next_batch()
else:
x_list, tnum = X.next_batch(Not_Realtest=False)
y_list = None
rd = 0
pdx = []
yl = []
with tf.Session() as sessb:
# Initialize all global and local variables
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sessb.run(init_op)
# Create a coordinator and run all QueueRunner objects
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sessb)
while True:
try:
if Not_Realtest:
x, y = sessb.run([x_list, y_list])
x = x.astype(np.uint8)
else:
x = sessb.run([x_list])
y = None
x = x[0].astype(np.uint8)
feed_dict = {self.x_in: x, self.is_train: train_status}
fetches = [self.pred, self.net, self.w]
pred, net, w = self.sesh.run(fetches, feed_dict)
if Not_Realtest:
ac.CAM(net, w, pred, x, y, dirr, 'Test', rd)
# else:
# ac.CAM_R(net, w, pred, x, dirr, 'Test', rd)
if rd == 0:
pdx = pred
yl = y
else:
pdx = np.concatenate((pdx, pred), axis=0)
yl = np.concatenate((yl, y), axis=None)
rd += 1
except tf.errors.OutOfRangeError:
# Stop the threads
coord.request_stop()
# Wait for threads to stop
coord.join(threads)
if Not_Realtest:
ac.metrics(pdx, yl, dirr, 'Test')
else:
ac.realout(pdx, dirr, 'Test')
sessb.close()
now = datetime.now().isoformat()[11:]
print("------- Testing end: {} -------\n".format(now), flush=True)
break
def inference(self,
X,
dirr,
testset,
bs,
train_status=False,
Not_Realtest=True):
now = datetime.now().isoformat()[11:]
print("------- Testing begin: {} -------\n".format(now), flush=True)
rd = 0
pdx = []
yl = []
if Not_Realtest:
itr, file, ph = X.data(train=False)
next_element = itr.get_next()
with tf.Session() as sessa:
sessa.run(itr.initializer, feed_dict={ph: file})
while True:
try:
x, y = sessa.run(next_element)
feed_dict = {self.x_in: x, self.is_train: train_status}
fetches = [self.pred, self.net, self.w]
pred, net, w = self.sesh.run(fetches, feed_dict)
# ac.CAM(net, w, pred, x, y, dirr, 'Test', bs, rd)
if rd == 0:
pdx = pred
yl = y
else:
pdx = np.concatenate((pdx, pred), axis=0)
yl = np.concatenate((yl, y), axis=0)
rd += 1
except tf.errors.OutOfRangeError:
ac.metrics(pdx, yl, dirr, 'Test', testset)
break
else:
itr, img, ph = X.data(Not_Realtest=False, train=False)
next_element = itr.get_next()
with tf.Session() as sessa:
sessa.run(itr.initializer, feed_dict={ph: img})
while True:
try:
x = sessa.run(next_element)
feed_dict = {self.x_in: x, self.is_train: train_status}
fetches = [self.pred, self.net, self.w]
pred, net, w = self.sesh.run(fetches, feed_dict)
# ac.CAM_R(net, w, pred, x, dirr, 'Test', bs, rd)
if rd == 0:
pdx = pred
else:
pdx = np.concatenate((pdx, pred), axis=0)
rd += 1
except tf.errors.OutOfRangeError:
ac.realout(pdx, dirr, 'Test')
break
now = datetime.now().isoformat()[11:]
print("------- Testing end: {} -------\n".format(now), flush=True)
def inference(self,
X,
dirr,
testset=None,
pmd=None,
train_status=False,
Not_Realtest=True,
bs=None):
now = datetime.now().isoformat()[11:]
print("------- Testing begin: {} -------\n".format(now))
rd = 0
pdx = []
yl = []
if Not_Realtest:
itr, file, ph = X.data(train=False)
next_element = itr.get_next()
with tf.Session() as sessa:
sessa.run(itr.initializer, feed_dict={ph: file})
while True:
try:
x, y = sessa.run(next_element)
feed_dict = {self.x_in: x, self.is_train: train_status}
fetches = [self.pred, self.net, self.w, self.logits]
pred, net, w, logits = self.sesh.run(
fetches, feed_dict)
# ac.CAM(net, w, pred, x, y, dirr, 'Test', bs, pmd, rd)
net = np.mean(net, axis=(1, 2))
if rd == 0:
pdx = pred
yl = y
netl = net
logitsl = logits
else:
pdx = np.concatenate((pdx, pred), axis=0)
yl = np.concatenate((yl, y), axis=0)
netl = np.concatenate((netl, net), axis=0)
logitsl = np.concatenate((logitsl, logits), axis=0)
rd += 1
except tf.errors.OutOfRangeError:
ac.metrics(pdx, yl, dirr, 'Test', pmd, testset)
ac.logit_agg(logitsl, testset, dirr, "Test")
ac.tSNE_prep(flatnet=netl,
ori_test=testset,
y=yl,
pred=pdx,
path=dirr,
pmd=pmd)
break
else:
itr, file, ph = X.data(Not_Realtest=False, train=False)
next_element = itr.get_next()
with tf.Session() as sessa:
sessa.run(itr.initializer, feed_dict={ph: file})
while True:
try:
x = sessa.run(next_element)
feed_dict = {self.x_in: x, self.is_train: train_status}
fetches = [self.pred, self.net, self.w]
pred, net, w = self.sesh.run(fetches, feed_dict)
# ac.CAM_R(net, w, pred, x, dirr, 'Test', bs, rd)
if rd == 0:
pdx = pred
else:
pdx = np.concatenate((pdx, pred), axis=0)
rd += 1
except tf.errors.OutOfRangeError:
ac.realout(pdx, dirr, 'Test', pmd)
break
now = datetime.now().isoformat()[11:]
print("------- Testing end: {} -------\n".format(now), flush=True)
