You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hello! I've found a performance issue in your project:
tf.Session being defined repeatedly leads to incremental overhead.
You can make your program more efficient by fixing this bug. Here is the Stack Overflow post to support it.
Below is detailed description about tf.Session being defined repeatedly:
tensorflow_/tensorflowcv/models/resnext.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/shufflenetv2b.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/zfnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/igcv3.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/densenet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/squeezenet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/mobilenet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/menet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/darknet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/seresnext.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/seresnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/mobilenetv2.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/sepreresnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/resnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/shufflenetv2.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/vgg.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/preresnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/channelnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/darknet53.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/shufflenet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/senet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/alexnet.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tensorflow_/tensorflowcv/models/squeezenext.py: with tf.Session() as sess(here) is repeatedly called in the loop for model in models(here).
tests/convert_gl2tf_batchnorm.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_conv1x1.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_maxpool2d.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_gconv2d.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_conv2d.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_avgpool2d.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_dwconv2d.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tests/convert_gl2tf_dense.py: with tf.Session() as sess(here) is repeatedly called in the loop for i in range(10)(here).
tf.Session being defined repeatedly could lead to incremental overhead. If you define tf.Session out of the loop and pass tf.Session as a parameter to the loop, your program would be much more efficient.
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
The text was updated successfully, but these errors were encountered:
Hello! I've found a performance issue in your project:
tf.Session
being defined repeatedly leads to incremental overhead.You can make your program more efficient by fixing this bug. Here is the Stack Overflow post to support it.
Below is detailed description about tf.Session being defined repeatedly:
with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor model in models
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).with tf.Session() as sess
(here) is repeatedly called in the loopfor i in range(10)
(here).tf.Session
being defined repeatedly could lead to incremental overhead. If you definetf.Session
out of the loop and passtf.Session
as a parameter to the loop, your program would be much more efficient.Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
The text was updated successfully, but these errors were encountered: