From 39f677a7b71c019bafb78f69701327997a6a235d Mon Sep 17 00:00:00 2001
From: edbeeching <edbeeching@gmail.com>
Date: Tue, 23 Jan 2024 22:02:54 +0100
Subject: [PATCH] removes dead custom models

---
 godot_rl/custom_models/__init__.py        |  0
 godot_rl/custom_models/attention_model.py | 96 -----------------------
 2 files changed, 96 deletions(-)
 delete mode 100644 godot_rl/custom_models/__init__.py
 delete mode 100644 godot_rl/custom_models/attention_model.py

diff --git a/godot_rl/custom_models/__init__.py b/godot_rl/custom_models/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/godot_rl/custom_models/attention_model.py b/godot_rl/custom_models/attention_model.py
deleted file mode 100644
index ea3c47bb..00000000
--- a/godot_rl/custom_models/attention_model.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import logging
-
-import gym
-import numpy as np
-from gym.spaces import Box, Discrete, MultiDiscrete
-from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFCNet
-from ray.rllib.models.torch.misc import AppendBiasLayer, SlimFC, normc_initializer
-from ray.rllib.models.torch.modules import GRUGate, RelativeMultiHeadAttention, SkipConnection
-from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
-from ray.rllib.policy.sample_batch import SampleBatch
-from ray.rllib.policy.view_requirement import ViewRequirement
-from ray.rllib.utils.annotations import override
-from ray.rllib.utils.framework import try_import_torch
-from ray.rllib.utils.typing import Dict, List, ModelConfigDict, TensorType
-
-torch, nn = try_import_torch()
-logger = logging.getLogger(__name__)
-
-
-# defines the attention model used in the bullet hell environment
-# first a feed forward to test that observations are being handled correctly
-
-
-class MyAttentionModel(TorchModelV2, nn.Module):
-    """Generic fully connected network."""
-
-    def __init__(
-        self,
-        obs_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        num_outputs: int,
-        model_config: ModelConfigDict,
-        name: str,
-    ):
-        TorchModelV2.__init__(self, obs_space, action_space, num_outputs, model_config, name)
-
-        nn.Module.__init__(self)
-        # simple baseline, fc all inputs and sum then value and policy head
-
-        # if isinstance(action_space, Discrete):
-        #     self.action_dim = action_space.n
-        # elif isinstance(action_space, MultiDiscrete):
-        #     self.action_dim = np.product(action_space.nvec)
-        # elif action_space.shape is not None:
-        #     self.action_dim = int(np.product(action_space.shape))
-        # else:
-        #     self.action_dim = int(len(action_space))
-        # print("action space", action_space, self.action_dim, num_outputs)
-        prev_layer_size = 3  # int(np.product(obs_space.shape))
-        # obs_space["obs"]["max_length"] = 1
-        self.model = TorchFCNet(obs_space, action_space, num_outputs, model_config, name)
-        print(self.model)
-
-        print(obs_space, prev_layer_size, self.num_outputs)
-        self._logits_branch = SlimFC(
-            in_size=prev_layer_size,
-            out_size=self.num_outputs,
-            activation_fn=None,
-            initializer=torch.nn.init.xavier_uniform_,
-        )
-        self._value_branch = SlimFC(
-            in_size=prev_layer_size,
-            out_size=1,
-            activation_fn=None,
-            initializer=torch.nn.init.xavier_uniform_,
-        )
-        # torch.set_printoptions(profile="full")
-
-    @override(TorchModelV2)
-    def forward(
-        self,
-        input_dict: Dict[str, TensorType],
-        state: List[TensorType],
-        seq_lens: TensorType,
-    ) -> (TensorType, List[TensorType]):
-        observations = input_dict[SampleBatch.OBS]
-        # print("unbatch", input_dict["obs"]["obs"].unbatch_all()[0])
-        # print(input_dict["obs"])
-        self._debug_batch_size = len(input_dict["obs"]["obs"].unbatch_all())
-        if not input_dict["obs"]["obs"].unbatch_all()[0]:
-            return (
-                np.zeros((self._debug_batch_size, 4)),
-                [],
-            )
-
-        results = []
-        for obs in input_dict["obs"]["obs"].unbatch_all():
-            batch = torch.cat(obs)
-            out = self.model({"obs": batch})
-            print(out.size())
-
-        return np.zeros((self._debug_batch_size, 4)), state
-
-    @override(TorchModelV2)
-    def value_function(self) -> TensorType:
-        return torch.zeros(self._debug_batch_size)