Explicit layer naming about zoo HOT 5 OPEN

After trying to consistently use this in my own code, I am less sure if we should adopt explicit layer naming because I found it did make my code somewhat less readable. To summarize:

Pros:

• Makes it much more intuitive to use models as building blocks in complicated structures (e.g. encoder/decoder nets, student/teacher for KD)
• Makes model summary more readable

Cons:

• Makes code less readable: adds an additional argument to every 'module' function (resnet_block() etc.) and occasionally forces the use of Lambda functions.

Personally, I think readable code strongly outweighs a readable model summary, but the building-block argument is probably decisive here. I would like to adopt a consistent policy for all of zoo. Please share any objections to the above reasoning; otherwise let's proceed with adding this for all models.

from zoo.

I am still very much in favor of this, as I am still running into cases (knowledge distillation) that need this. I had forgotten I was assigned to this. I am a little busy at the moment but since I need this for KD I will make a PR at some point to the models in zoo that do not have this.

from zoo.

As an example of the kind of confusion this might prevent:

The zoo example code that deals with exactly this point uses get layer by name:

outputs=base_model.get_layer("average_pooling2d_8").output

The example code fragment does not actually work in isolation since the the network only has 3 average_pooling2d layers. It does work when following the examples one by one in the same session as this is the third example with the same network, so by creating the previous 2 there are enough average pooling layers to get to number 8.

from zoo.

I thought this would be the standard from now on, but I see QuickNet unfortunately still has layer names like "add_3" and "quant_conv2d_5". Do we or do we not enforce this policy?

from zoo.

Models which currently have explicit layers names, so no auto-generated names, based on the model summaries embedded in the docs:

• sota
  • QuickNet
  • QuickNetLarge
  • QuickNetXL
• literature
  • BinaryAlexNet
  • BiRealNet
  • BinaryResNetE18
  • BinaryDenseNet28
  • BinaryDenseNet37
  • BinaryDenseNet37Dilated
  • BinaryDenseNet45
  • DoReFaNet
  • MeliusNet22
  • RealToBinaryNet
  • XNORNet

from zoo.