Using the estimated models outside of MLWIC2

[vmielecnrs]

Hi,

Could it be possible to use the estimated models (I mean, the
model weights, for instance in architecture ResNet-18) outside of MLWIC2, for those of us who prefer Python for instance, or who have their own codes already running?

I would like, for instance, to reuse your estimated models in my codes
implemented in Keras. Do you think it could be possible?

If this require some code development, I can participate in the effort.

[mikeyEcology]

The weights are stored in the helper files folder, which you can download from Step 3 in the readme. If you open the species_model folder, you can see the snapshots. These models were trained in tensorflow, so you should be able to load them in Keras.

[vmielecnrs]

Thanks for the answer. Not easy to do, by the way.

Is your Python code only working with TensorFlow version 1?

pretrained_loader = tf.train.Saver(var_list= self.exclude())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: module 'tensorflow._api.v2.train' has no attribute 'Saver'

[mikeyEcology]

Yes. There are tons of version issues with Tensorflow. You'll need tensorflow 1.14 and python 3.7.

[vmielecnrs]

It turns out that I get many problems with the "eager mode":

pretrained_loader = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))
WARNING: When executing eagerly variables do not necessarily have unique names

tf.train.import_meta_graph("empty_animal/snapshot-110.meta")
RuntimeError:` Exporting/importing meta graphs is not supported when eager execution is enabled.

Does it mean you used the eager mode? And that the network and its weights are not available in the end?

Thanks in advance