nip.image_classification.agents.ImageClassificationRandomAgentPolicyHead#

class nip.image_classification.agents.ImageClassificationRandomAgentPolicyHead(*args, **kwargs)[source]#

Policy head for the image classification task yielding a uniform distribution.

Shapes

Input:

“image_level_repr” (… d_representation): The output image-level representations.
“latent_pixel_level_repr” (… latent_height latent_width d_representation): The output latent-pixel-level representations.

Output:

“latent_pixel_selected_logits” (… channel position latent_height*latent_width): A logit for each latent pixel, indicating the probability that this latent pixel should be sent as a message to the verifier.
“decision_logits” (… 3): A logit for each of the three options: guess a classification one way or the other, or continue exchanging messages. Set to zeros when the decider is not present.
“linear_message_selected_logits” (… channel position d_linear_message_space) (optional): A logit for each linear message, indicating the probability that this linear message should be sent as a message to the verifier.

Methods Summary

`__init__`(hyper_params, settings, agent_name, ...)	Initialize internal Module state, shared by both nn.Module and ScriptModule.
`_init_weights`()	Initialise the module weights.
`_run_recorder_hook`(hooks, hook_name, output)
`forward`(body_output)	Output a uniform distribution.
`get_state_dict`()	Get the state of the agent part as a dict.
`set_state`(checkpoint)	Set the state of the agent from a checkpoint.
`to`(device)	Move the agent to the given device.

Attributes

`T_destination`
`agent_id`	The ID of the agent.
`agent_level_in_keys`
`agent_level_out_keys`	The agent-level output keys.
`call_super_init`
`dump_patches`
`env_level_in_keys`
`env_level_out_keys`
`has_decider`	Whether the policy head has an output yielding a decision.
`in_keys`	The keys required by the module.
`is_prover`	Whether the agent is a prover.
`is_verifier`	Whether the agent is a verifier.
`max_message_rounds`	The maximum number of message rounds in the protocol.
`num_visible_message_channels`	The number of message channels visible to the agent.
`out_keys`	The keys produced by the module.
`out_keys_source`
`required_pretrained_models`	The pretrained models used by the agent.
`visible_message_channel_indices`	The indices of the message channels visible to the agent.
`visible_message_channel_mask`	The mask for the message channels visible to the agent.
`visible_message_channel_names`	The names of the message channels visible to the agent.
`agent_params`
`training`

Methods

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, agent_name: str, protocol_handler: ProtocolHandler)[source]#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

_init_weights()[source]#

Initialise the module weights.

Should be called at the end of __init__

_run_recorder_hook(hooks: AgentHooks | None, hook_name: str, output: Tensor | None)[source]#

Output a uniform distribution.

Parameters:: body_output (TensorDict) – The output of the body module.
Returns:: out (TensorDict) – A tensor dict with all zero outputs.

get_state_dict() → dict[source]#

Get the state of the agent part as a dict.

This method should be implemented by subclasses capable of saving their state.

set_state(checkpoint: AgentState)[source]#

Set the state of the agent from a checkpoint.

This method should be overridden by subclasses to restore the state of the agent from a checkpoint.

Parameters:: checkpoint (AgentCheckpoint) – The checkpoint to restore the state from.

to(device: device | str | int)[source]#: Move the agent to the given device.

nip.image_classification.agents.ImageClassificationRandomAgentPolicyHead