i have two gaussian_conditional ,when update model, why one update,another one dont update

[battlepeace]

firstly,thank you for your work!
i have two gaussian_conditional ,when update model, why one update,another one dont update
self.gaussian_conditional = GaussianConditional(None)
self.gaussian_conditional_z = GaussianConditional(None)

[YodaEmbedding]

Which version/commit of CompressAI is this? The latest version should handle all these things automatically.

CompressAI/compressai/models/base.py

Lines 62 to 113 in ee91d53

	class CompressionModel(nn.Module):
	"""Base class for constructing an auto-encoder with any number of
	EntropyBottleneck or GaussianConditional modules.
	"""
	def load_state_dict(self, state_dict, strict=True):
	for name, module in self.named_modules():
	if not any(x.startswith(name) for x in state_dict.keys()):
	continue
	if isinstance(module, EntropyBottleneck):
	update_registered_buffers(
	module,
	name,
	["_quantized_cdf", "_offset", "_cdf_length"],
	state_dict,
	)
	if isinstance(module, GaussianConditional):
	update_registered_buffers(
	module,
	name,
	["_quantized_cdf", "_offset", "_cdf_length", "scale_table"],
	state_dict,
	)
	return nn.Module.load_state_dict(self, state_dict, strict=strict)
	def update(self, scale_table=None, force=False):
	"""Updates EntropyBottleneck and GaussianConditional CDFs.
	Needs to be called once after training to be able to later perform the
	evaluation with an actual entropy coder.
	Args:
	scale_table (torch.Tensor): table of scales (i.e. stdev)
	for initializing the Gaussian distributions
	(default: 64 logarithmically spaced scales from 0.11 to 256)
	force (bool): overwrite previous values (default: False)
	Returns:
	updated (bool): True if at least one of the modules was updated.
	"""
	if scale_table is None:
	scale_table = get_scale_table()
	updated = False
	for _, module in self.named_modules():
	if isinstance(module, EntropyBottleneck):
	updated |= module.update(force=force)
	if isinstance(module, GaussianConditional):
	updated |= module.update_scale_table(scale_table, force=force)
	return updated

But if you want to figure out what's going on without git pulling the latest changes:

• Add a print("This function runs.") statement when the function containing the update_registered_buffers is called.
• Ensure the correct load_state_dict is called. super() sometimes may not refer to what you are expecting.

[battlepeace]

my compressai version is 1.2.0,you say the new version can handle all these things automatically,I want to know if there are any other function updates in the new version?

[YodaEmbedding]

Automatic definition of load_state_dict and update

from compressai.models.base import CompressionModel


class ExampleModel(CompressionModel):
    def __init__(self, N, M):
        super().__init__()

        self.entropy_bottleneck_1 = EntropyBottleneck(M)
        self.entropy_bottleneck_2 = EntropyBottleneck(M)
        ...

        self.gaussian_conditional_1 = GaussianConditional()
        self.gaussian_conditional_2 = GaussianConditional()
        ...

    # def load_state_dict(...) should automatically work. No need to write your own!

    # def update(...) should automatically work. No need to write your own!

No other significant breaking changes from 1.2.0 to 1.2.4, so upgrading should be easy.

---

---

Most users can stop reading here. The section below is for more "advanced usage".

---

---

Different scale tables

A single instance of GaussianConditional does effectively the same thing as multiple GaussianConditionals if they all use the same scale table. If you intend on initializing them with different scale tables, then update() does need to be overridden:

    def update(self, scale_table=None, force=False):
        updated = False

        for _, module in self.named_modules():
            if isinstance(module, EntropyBottleneck):
                updated |= module.update(force=force)

        assert scale_table is None  # We're using our own scale_tables.
        scale_table_1 = ...
        scale_table_2 = ...
        updated |= self.gaussian_conditional_1.update_scale_table(scale_table_1, force=force)
        updated |= self.gaussian_conditional_2.update_scale_table(scale_table_2, force=force)

        return updated

For load_state_dict, we can just inherit the default implementation in CompressionModel.load_state_dict.

[battlepeace]

I'm sorry I don't quite understand GaussianCondition .you means scale_table Is it a default table that won't change as the input image changes？
I mean, my two different Gaussian Conditions, one for modeling z and one for modeling y, have the same parameters for Gaussian Conditions. Is this normal?Their parameters both are shown below.

[YodaEmbedding]

Yes, their non-trainable parameters should all have the same default initialized values. This is fine.

The section I wrote about "Different scale tables" was just in case you wanted to customize it further.

[battlepeace]

Thank you very much for your answer. I have another question. There is a parameter in the code that is quality.For example, in the following model.

His quality parameters are as follows:

I want to know what this parameter means. Is it N and M in the network model? Do you write this to facilitate selecting different N and M for experiments?

[YodaEmbedding]

Yes, these are N, M, and are used when calling the model's __init__:

CompressAI/compressai/models/google.py

Line 242 in ee91d53

def __init__(self, N, M, **kwargs):

The quality parameter refers to different image quality targets that each separate model was trained on. This is done by changing the $\lambda$ tradeoff hyperparameter in the loss function, $L = R + \lambda D$. The lambdas used for each quality are listed here: https://interdigitalinc.github.io/CompressAI/zoo.html#training

[battlepeace]

I don't quite understand what you mean. What you mean is that the qualities of 1-8 are λ Are there different values for?

[YodaEmbedding]

The qualities are just a convenient way to number the different trained models which produce small files (quality=1) or large files (quality=8).

The lambdas shown were used to train these models. But you can train with any lambda you want.

[battlepeace]

oh,you mean that the specific training lambda is determined when I input training instructions, and the quality I choose will not affect the specific training process. For example, if I specify lambda=0.02, even if I choose quality=1, the lambda during training is still 0.02?

[YodaEmbedding]

Yes, that is correct.

[battlepeace]

ok，tooo much thanks to you

[battlepeace]

sorry,i have a another question.What exactly does aux_loss represent? When I train 10000 images with a batch size of 16 for 100 epochs, in the training output report, The value of aux_ loss is always high, ranging from around 15000 during initial training to around 5500 after 100 epochs. How can it be minimized?

[YodaEmbedding]

aux_loss is described here:

• https://interdigitalinc.github.io/CompressAI/models.html#compressai.models.CompressionModel.aux_loss
• About Auxiliary loss optimization #167

Usually, it goes down to ~30 after the first few epochs. It's unusual that it stays large in your case.

Possible scenarios:

• Perhaps aux_optimizer.zero_grad(), aux_loss.backward(), and aux_optimizer.step() aren't called in the correct order. Is your train_one_epoch the same as the original from examples/train.py?
• Perhaps the aux_optimizer doesn't have access to every instance of the EntropyBottleneck.quantiles parameters. Do you have multiple EntropyBottleneck instances in your model?

Also, what is the value of the standard RD loss after 100 epochs? Could you also check to see if the bpp from running eval_model matches the bpp_loss? (Run both on the same test image.)

[battlepeace]

my train_one_epoch is same as original from examples/train.py.
what is s means?EntropyBottleneck() this function is entropy encoded for data, right? However, for example, the ScaleHyperprior model from the h_a obtained z ,z should be modeled using the Factorized Entropy Model. Has EntropyBottleneck() completed this operation?
i use mse to eval model,RD loss is mse loss?this is my output
output.txt