fix: remove eval() python method (#265)

* fix: remove eval() python method * fix: dreamer_v3 doapp configs
Eclectic-Sheep · Apr 15, 2024 · 31a8e9b · 31a8e9b
1 parent 8ca2a60
commit 31a8e9b
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Showing 17 changed files with 109 additions and 81 deletions.
diff --git a/howto/learn_in_diambra.md b/howto/learn_in_diambra.md
@@ -86,7 +86,7 @@ env:
     diambra_settings:
         characters: Kasumi
         step_ratio: 5
-        role: diambra.arena.Roles.P1
+        role: P1
     diambra_wrappers:
         normalize_reward: True
         normalization_factor: 0.3

diff --git a/howto/register_external_algorithm.md b/howto/register_external_algorithm.md
@@ -96,6 +96,7 @@ from math import prod
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 
 import gymnasium
+import hydra
 import torch
 import torch.nn as nn
 from lightning import Fabric
@@ -198,7 +199,7 @@ class PPOAgent(nn.Module):
                 mlp_keys,
                 encoder_cfg.dense_units,
                 encoder_cfg.mlp_layers,
-                eval(encoder_cfg.dense_act),
+                hydra.utils.get_class(encoder_cfg.dense_act),
                 encoder_cfg.layer_norm,
             )
             if mlp_keys is not None and len(mlp_keys) > 0
@@ -210,7 +211,7 @@ class PPOAgent(nn.Module):
             input_dims=features_dim,
             output_dim=1,
             hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-            activation=eval(critic_cfg.dense_act),
+            activation=hydra.utils.get_class(critic_cfg.dense_act),
             norm_layer=[nn.LayerNorm for _ in range(critic_cfg.mlp_layers)] if critic_cfg.layer_norm else None,
             norm_args=(
                 [{"normalized_shape": critic_cfg.dense_units} for _ in range(critic_cfg.mlp_layers)]
@@ -223,7 +224,7 @@ class PPOAgent(nn.Module):
                 input_dims=features_dim,
                 output_dim=None,
                 hidden_sizes=[actor_cfg.dense_units] * actor_cfg.mlp_layers,
-                activation=eval(actor_cfg.dense_act),
+                activation=hydra.utils.get_class(actor_cfg.dense_act),
                 flatten_dim=None,
                 norm_layer=[nn.LayerNorm] * actor_cfg.mlp_layers if actor_cfg.layer_norm else None,
                 norm_args=(

diff --git a/howto/register_new_algorithm.md b/howto/register_new_algorithm.md
@@ -95,6 +95,7 @@ from math import prod
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 
 import gymnasium
+import hydra
 import torch
 import torch.nn as nn
 from lightning import Fabric
@@ -197,7 +198,7 @@ class PPOAgent(nn.Module):
                 mlp_keys,
                 encoder_cfg.dense_units,
                 encoder_cfg.mlp_layers,
-                eval(encoder_cfg.dense_act),
+                hydra.utils.get_class(encoder_cfg.dense_act),
                 encoder_cfg.layer_norm,
             )
             if mlp_keys is not None and len(mlp_keys) > 0
@@ -209,7 +210,7 @@ class PPOAgent(nn.Module):
             input_dims=features_dim,
             output_dim=1,
             hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-            activation=eval(critic_cfg.dense_act),
+            activation=hydra.utils.get_class(critic_cfg.dense_act),
             norm_layer=[nn.LayerNorm for _ in range(critic_cfg.mlp_layers)] if critic_cfg.layer_norm else None,
             norm_args=(
                 [{"normalized_shape": critic_cfg.dense_units} for _ in range(critic_cfg.mlp_layers)]
@@ -222,7 +223,7 @@ class PPOAgent(nn.Module):
                 input_dims=features_dim,
                 output_dim=None,
                 hidden_sizes=[actor_cfg.dense_units] * actor_cfg.mlp_layers,
-                activation=eval(actor_cfg.dense_act),
+                activation=hydra.utils.get_class(actor_cfg.dense_act),
                 flatten_dim=None,
                 norm_layer=[nn.LayerNorm] * actor_cfg.mlp_layers if actor_cfg.layer_norm else None,
                 norm_args=(

diff --git a/sheeprl/algos/a2c/agent.py b/sheeprl/algos/a2c/agent.py
@@ -4,6 +4,7 @@
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 
 import gymnasium
+import hydra
 import torch
 import torch.nn as nn
 from lightning import Fabric
@@ -72,7 +73,7 @@ def __init__(
                 mlp_keys,
                 encoder_cfg.dense_units,
                 encoder_cfg.mlp_layers,
-                eval(encoder_cfg.dense_act),
+                hydra.utils.get_class(encoder_cfg.dense_act),
                 encoder_cfg.layer_norm,
             )
             if mlp_keys is not None and len(mlp_keys) > 0
@@ -85,7 +86,7 @@ def __init__(
             input_dims=features_dim,
             output_dim=1,
             hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-            activation=eval(critic_cfg.dense_act),
+            activation=hydra.utils.get_class(critic_cfg.dense_act),
             norm_layer=[nn.LayerNorm for _ in range(critic_cfg.mlp_layers)] if critic_cfg.layer_norm else None,
             norm_args=(
                 [{"normalized_shape": critic_cfg.dense_units} for _ in range(critic_cfg.mlp_layers)]
@@ -99,7 +100,7 @@ def __init__(
             input_dims=features_dim,
             output_dim=None,
             hidden_sizes=[actor_cfg.dense_units] * actor_cfg.mlp_layers,
-            activation=eval(actor_cfg.dense_act),
+            activation=hydra.utils.get_class(actor_cfg.dense_act),
             flatten_dim=None,
             norm_layer=[nn.LayerNorm] * actor_cfg.mlp_layers if actor_cfg.layer_norm else None,
             norm_args=(

diff --git a/sheeprl/algos/dreamer_v1/agent.py b/sheeprl/algos/dreamer_v1/agent.py
@@ -373,7 +373,7 @@ def build_agent(
             image_size=obs_space[cfg.algo.cnn_keys.encoder[0]].shape[-2:],
             channels_multiplier=world_model_cfg.encoder.cnn_channels_multiplier,
             layer_norm=False,
-            activation=eval(world_model_cfg.encoder.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.cnn_act),
         )
         if cfg.algo.cnn_keys.encoder is not None and len(cfg.algo.cnn_keys.encoder) > 0
         else None
@@ -384,7 +384,7 @@ def build_agent(
             input_dims=[obs_space[k].shape[0] for k in cfg.algo.mlp_keys.encoder],
             mlp_layers=world_model_cfg.encoder.mlp_layers,
             dense_units=world_model_cfg.encoder.dense_units,
-            activation=eval(world_model_cfg.encoder.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.dense_act),
             layer_norm=False,
         )
         if cfg.algo.mlp_keys.encoder is not None and len(cfg.algo.mlp_keys.encoder) > 0
@@ -394,22 +394,22 @@ def build_agent(
     recurrent_model = RecurrentModel(
         input_size=sum(actions_dim) + world_model_cfg.stochastic_size,
         recurrent_state_size=world_model_cfg.recurrent_model.recurrent_state_size,
-        activation=eval(world_model_cfg.recurrent_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.recurrent_model.dense_act),
     )
     representation_model = MLP(
         input_dims=(
             world_model_cfg.recurrent_model.recurrent_state_size + encoder.cnn_output_dim + encoder.mlp_output_dim
         ),
         output_dim=world_model_cfg.stochastic_size * 2,
         hidden_sizes=[world_model_cfg.representation_model.hidden_size],
-        activation=eval(world_model_cfg.representation_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.representation_model.dense_act),
         flatten_dim=None,
     )
     transition_model = MLP(
         input_dims=world_model_cfg.recurrent_model.recurrent_state_size,
         output_dim=world_model_cfg.stochastic_size * 2,
         hidden_sizes=[world_model_cfg.transition_model.hidden_size],
-        activation=eval(world_model_cfg.transition_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.transition_model.dense_act),
         flatten_dim=None,
     )
     rssm = RSSM(
@@ -427,7 +427,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             cnn_encoder_output_dim=cnn_encoder.output_dim,
             image_size=obs_space[cfg.algo.cnn_keys.decoder[0]].shape[-2:],
-            activation=eval(world_model_cfg.observation_model.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.cnn_act),
             layer_norm=False,
         )
         if cfg.algo.cnn_keys.decoder is not None and len(cfg.algo.cnn_keys.decoder) > 0
@@ -440,7 +440,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             mlp_layers=world_model_cfg.observation_model.mlp_layers,
             dense_units=world_model_cfg.observation_model.dense_units,
-            activation=eval(world_model_cfg.observation_model.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.dense_act),
             layer_norm=False,
         )
         if cfg.algo.mlp_keys.decoder is not None and len(cfg.algo.mlp_keys.decoder) > 0
@@ -451,15 +451,15 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=1,
         hidden_sizes=[world_model_cfg.reward_model.dense_units] * world_model_cfg.reward_model.mlp_layers,
-        activation=eval(world_model_cfg.reward_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.reward_model.dense_act),
         flatten_dim=None,
     )
     if world_model_cfg.use_continues:
         continue_model = MLP(
             input_dims=latent_state_size,
             output_dim=1,
             hidden_sizes=[world_model_cfg.discount_model.dense_units] * world_model_cfg.discount_model.mlp_layers,
-            activation=eval(world_model_cfg.discount_model.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.discount_model.dense_act),
             flatten_dim=None,
         )
     world_model = WorldModel(
@@ -478,7 +478,7 @@ def build_agent(
         min_std=actor_cfg.min_std,
         mlp_layers=actor_cfg.mlp_layers,
         dense_units=actor_cfg.dense_units,
-        activation=eval(actor_cfg.dense_act),
+        activation=hydra.utils.get_class(actor_cfg.dense_act),
         distribution_cfg=cfg.distribution,
         layer_norm=False,
         expl_amount=actor_cfg.expl_amount,
@@ -489,7 +489,7 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=1,
         hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-        activation=eval(critic_cfg.dense_act),
+        activation=hydra.utils.get_class(critic_cfg.dense_act),
         flatten_dim=None,
     )
     actor.apply(init_weights)

diff --git a/sheeprl/algos/dreamer_v2/agent.py b/sheeprl/algos/dreamer_v2/agent.py
@@ -883,7 +883,7 @@ def build_agent(
             image_size=obs_space[cfg.algo.cnn_keys.encoder[0]].shape[-2:],
             channels_multiplier=world_model_cfg.encoder.cnn_channels_multiplier,
             layer_norm=world_model_cfg.encoder.layer_norm,
-            activation=eval(world_model_cfg.encoder.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.cnn_act),
         )
         if cfg.algo.cnn_keys.encoder is not None and len(cfg.algo.cnn_keys.encoder) > 0
         else None
@@ -894,7 +894,7 @@ def build_agent(
             input_dims=[obs_space[k].shape[0] for k in cfg.algo.mlp_keys.encoder],
             mlp_layers=world_model_cfg.encoder.mlp_layers,
             dense_units=world_model_cfg.encoder.dense_units,
-            activation=eval(world_model_cfg.encoder.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.dense_act),
             layer_norm=world_model_cfg.encoder.layer_norm,
         )
         if cfg.algo.mlp_keys.encoder is not None and len(cfg.algo.mlp_keys.encoder) > 0
@@ -911,7 +911,7 @@ def build_agent(
         ),
         output_dim=stochastic_size,
         hidden_sizes=[world_model_cfg.representation_model.hidden_size],
-        activation=eval(world_model_cfg.representation_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.representation_model.dense_act),
         flatten_dim=None,
         norm_layer=[nn.LayerNorm] if world_model_cfg.representation_model.layer_norm else None,
         norm_args=(
@@ -924,7 +924,7 @@ def build_agent(
         input_dims=world_model_cfg.recurrent_model.recurrent_state_size,
         output_dim=stochastic_size,
         hidden_sizes=[world_model_cfg.transition_model.hidden_size],
-        activation=eval(world_model_cfg.transition_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.transition_model.dense_act),
         flatten_dim=None,
         norm_layer=[nn.LayerNorm] if world_model_cfg.transition_model.layer_norm else None,
         norm_args=(
@@ -948,7 +948,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             cnn_encoder_output_dim=cnn_encoder.output_dim,
             image_size=obs_space[cfg.algo.cnn_keys.decoder[0]].shape[-2:],
-            activation=eval(world_model_cfg.observation_model.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.cnn_act),
             layer_norm=world_model_cfg.observation_model.layer_norm,
         )
         if cfg.algo.cnn_keys.decoder is not None and len(cfg.algo.cnn_keys.decoder) > 0
@@ -961,7 +961,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             mlp_layers=world_model_cfg.observation_model.mlp_layers,
             dense_units=world_model_cfg.observation_model.dense_units,
-            activation=eval(world_model_cfg.observation_model.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.dense_act),
             layer_norm=world_model_cfg.observation_model.layer_norm,
         )
         if cfg.algo.mlp_keys.decoder is not None and len(cfg.algo.mlp_keys.decoder) > 0
@@ -972,7 +972,7 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=1,
         hidden_sizes=[world_model_cfg.reward_model.dense_units] * world_model_cfg.reward_model.mlp_layers,
-        activation=eval(world_model_cfg.reward_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.reward_model.dense_act),
         flatten_dim=None,
         norm_layer=(
             [nn.LayerNorm for _ in range(world_model_cfg.reward_model.mlp_layers)]
@@ -993,7 +993,7 @@ def build_agent(
             input_dims=latent_state_size,
             output_dim=1,
             hidden_sizes=[world_model_cfg.discount_model.dense_units] * world_model_cfg.discount_model.mlp_layers,
-            activation=eval(world_model_cfg.discount_model.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.discount_model.dense_act),
             flatten_dim=None,
             norm_layer=(
                 [nn.LayerNorm for _ in range(world_model_cfg.discount_model.mlp_layers)]
@@ -1025,15 +1025,15 @@ def build_agent(
         min_std=actor_cfg.min_std,
         mlp_layers=actor_cfg.mlp_layers,
         dense_units=actor_cfg.dense_units,
-        activation=eval(actor_cfg.dense_act),
+        activation=hydra.utils.get_class(actor_cfg.dense_act),
         distribution_cfg=cfg.distribution,
         layer_norm=actor_cfg.layer_norm,
     )
     critic = MLP(
         input_dims=latent_state_size,
         output_dim=1,
         hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-        activation=eval(critic_cfg.dense_act),
+        activation=hydra.utils.get_class(critic_cfg.dense_act),
         flatten_dim=None,
         norm_layer=[nn.LayerNorm for _ in range(critic_cfg.mlp_layers)] if critic_cfg.layer_norm else None,
         norm_args=(

diff --git a/sheeprl/algos/dreamer_v3/agent.py b/sheeprl/algos/dreamer_v3/agent.py
@@ -986,7 +986,7 @@ def build_agent(
             channels_multiplier=world_model_cfg.encoder.cnn_channels_multiplier,
             layer_norm_cls=hydra.utils.get_class(world_model_cfg.encoder.cnn_layer_norm.cls),
             layer_norm_kw=world_model_cfg.encoder.cnn_layer_norm.kw,
-            activation=eval(world_model_cfg.encoder.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.cnn_act),
             stages=cnn_stages,
         )
         if cfg.algo.cnn_keys.encoder is not None and len(cfg.algo.cnn_keys.encoder) > 0
@@ -998,7 +998,7 @@ def build_agent(
             input_dims=[obs_space[k].shape[0] for k in cfg.algo.mlp_keys.encoder],
             mlp_layers=world_model_cfg.encoder.mlp_layers,
             dense_units=world_model_cfg.encoder.dense_units,
-            activation=eval(world_model_cfg.encoder.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.encoder.dense_act),
             layer_norm_cls=hydra.utils.get_class(world_model_cfg.encoder.mlp_layer_norm.cls),
             layer_norm_kw=world_model_cfg.encoder.mlp_layer_norm.kw,
         )
@@ -1022,7 +1022,7 @@ def build_agent(
         input_dims=represention_model_input_size,
         output_dim=stochastic_size,
         hidden_sizes=[world_model_cfg.representation_model.hidden_size],
-        activation=eval(world_model_cfg.representation_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.representation_model.dense_act),
         layer_args={"bias": representation_ln_cls == nn.Identity},
         flatten_dim=None,
         norm_layer=[representation_ln_cls],
@@ -1038,7 +1038,7 @@ def build_agent(
         input_dims=recurrent_state_size,
         output_dim=stochastic_size,
         hidden_sizes=[world_model_cfg.transition_model.hidden_size],
-        activation=eval(world_model_cfg.transition_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.transition_model.dense_act),
         layer_args={"bias": transition_ln_cls == nn.Identity},
         flatten_dim=None,
         norm_layer=[transition_ln_cls],
@@ -1072,7 +1072,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             cnn_encoder_output_dim=cnn_encoder.output_dim,
             image_size=obs_space[cfg.algo.cnn_keys.decoder[0]].shape[-2:],
-            activation=eval(world_model_cfg.observation_model.cnn_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.cnn_act),
             layer_norm_cls=hydra.utils.get_class(world_model_cfg.observation_model.cnn_layer_norm.cls),
             layer_norm_kw=world_model_cfg.observation_model.mlp_layer_norm.kw,
             stages=cnn_stages,
@@ -1087,7 +1087,7 @@ def build_agent(
             latent_state_size=latent_state_size,
             mlp_layers=world_model_cfg.observation_model.mlp_layers,
             dense_units=world_model_cfg.observation_model.dense_units,
-            activation=eval(world_model_cfg.observation_model.dense_act),
+            activation=hydra.utils.get_class(world_model_cfg.observation_model.dense_act),
             layer_norm_cls=hydra.utils.get_class(world_model_cfg.observation_model.mlp_layer_norm.cls),
             layer_norm_kw=world_model_cfg.observation_model.mlp_layer_norm.kw,
         )
@@ -1101,7 +1101,7 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=world_model_cfg.reward_model.bins,
         hidden_sizes=[world_model_cfg.reward_model.dense_units] * world_model_cfg.reward_model.mlp_layers,
-        activation=eval(world_model_cfg.reward_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.reward_model.dense_act),
         layer_args={"bias": reward_ln_cls == nn.Identity},
         flatten_dim=None,
         norm_layer=reward_ln_cls,
@@ -1116,7 +1116,7 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=1,
         hidden_sizes=[world_model_cfg.discount_model.dense_units] * world_model_cfg.discount_model.mlp_layers,
-        activation=eval(world_model_cfg.discount_model.dense_act),
+        activation=hydra.utils.get_class(world_model_cfg.discount_model.dense_act),
         layer_args={"bias": discount_ln_cls == nn.Identity},
         flatten_dim=None,
         norm_layer=discount_ln_cls,
@@ -1141,7 +1141,7 @@ def build_agent(
         init_std=actor_cfg.init_std,
         min_std=actor_cfg.min_std,
         dense_units=actor_cfg.dense_units,
-        activation=eval(actor_cfg.dense_act),
+        activation=hydra.utils.get_class(actor_cfg.dense_act),
         mlp_layers=actor_cfg.mlp_layers,
         distribution_cfg=cfg.distribution,
         layer_norm_cls=hydra.utils.get_class(actor_cfg.layer_norm.cls),
@@ -1155,7 +1155,7 @@ def build_agent(
         input_dims=latent_state_size,
         output_dim=critic_cfg.bins,
         hidden_sizes=[critic_cfg.dense_units] * critic_cfg.mlp_layers,
-        activation=eval(critic_cfg.dense_act),
+        activation=hydra.utils.get_class(critic_cfg.dense_act),
         layer_args={"bias": critic_ln_cls == nn.Identity},
         flatten_dim=None,
         norm_layer=critic_ln_cls,