fixing flataction discriminator to convert to float beforehand, adding necessary forward calls in expert, adding Fire to video creator from model, and trying full run of options gail with new discrimination model

scratch/etienne/intersimple/data/expert.py

@@ -80,6 +80,12 @@ class DummyVecEnvPolicy(BasePolicy):
         states = [p[1] for p in predictions]
         return actions, states
     
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
 def save_video(env, expert):
     env.reset()
     env.render()

scratch/etienne/intersimple/gail/discriminator.py

@@ -75,7 +75,7 @@ class CnnDiscriminatorFlatAction(torch.nn.Module):
         return sa
 
     def forward(self, state, action):
-        s = self.cnn(state)
+        s = self.cnn(state.float())
         sa = self._concatenate(s, action)
         assert sa.ndim == 2
         return self.decoder(sa).squeeze(1)

scratch/etienne/intersimple/gail_options_image.py

@@ -1,5 +1,5 @@
 # %%
-from gail.discriminator import CnnDiscriminator
+from gail.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
 from imitation.algorithms import adversarial
 import stable_baselines3
 import torch.utils.data
@@ -271,7 +271,8 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
     discriminator = adversarial.GAIL(
         expert_data=expert_data,
         expert_batch_size=expert_batch_size,
-        discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
         venv=venv, # unused
         gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
     )
@@ -299,10 +300,11 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
 # %%
 if __name__ == '__main__':
     # %%
+ 
     with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
         trajectories = pickle.load(f)
     transitions = rollout.flatten_trajectories(trajectories)
-    generator = train(transitions, epochs=2, expert_batch_size=2, generator_steps=2)
+    generator = train(transitions)
 
     generator.save(model_name)
 

scratch/etienne/intersimple/render_env_from_model.py

@@ -3,14 +3,22 @@ import stable_baselines3 as sb3
 from intersim.envs.intersimple import NRasterized
 
 
-model_name = 'gail_image_singleagent_nocollision'
+def render_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
-model = sb3.PPO.load(model_name)
+    """
+    Render a video from an model, agent, and environment
+    Args:
+        model_name (str): name of the model
+        agent (int): agent to start the video from
+        environment (gym.Env): gym environment class to render environment on
+    """
 
-env = NRasterized(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=51)
+    model = sb3.PPO.load(model_name)
 
-obs = env.reset()
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
-i=0
+
-while True and i < 600:
+    obs = env.reset()
+    i=0
+    while True and i < 600:
         i+=1
         action, _states = model.predict(obs)
         obs, rewards, done, info = env.step(action)
@@ -18,4 +26,8 @@ while True and i < 600:
         if done:
             break
 
-env.close(filestr='render/'+model_name)
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(render_env)