adding discriminators to main folder, utilities to render a video from a saved model

src/discriminator/__init__.py

@@ -0,0 +1 @@
+from src.discriminator.discriminator import *

src/discriminator/discriminator.py

@@ -0,0 +1,101 @@
+import torch
+
+# imitation.rewards.discrim_nets.DiscrimNetGAIL is composed of self.discriminator (nn.Module),
+# which gets called with inputs (state, action) when needed.
+
+class CnnDiscriminator(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels + action_size
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # 5+1 -> 32
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=1, end_dim=-1),
+            torch.nn.LazyLinear(512), # 28224 -> 512
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1), # 512 -> 1
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, _, h, w = state.shape
+        _, a = action.shape
+        act = action.unsqueeze(-1).unsqueeze(-1).expand((b, a, h, w))
+        sa = torch.cat((state, act), -3)
+        return sa
+
+    def forward(self, state, action):
+        sa = self._concatenate(state, action)
+        assert sa.ndim == 4
+        return self.cnn(sa).squeeze(1)
+
+class CnnDiscriminatorFlatAction(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # in_channels -> 32
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=1, end_dim=-1),
+            torch.nn.LazyLinear(128), # 28224 -> 128
+        )
+        self.decoder = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), #128 + 2 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(64), #64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1) #64 -> 1
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, s= state.shape
+        b, a = action.shape
+        sa = torch.cat((state, action), -1)
+        return sa
+
+    def forward(self, state, action):
+        s = self.cnn(state.float())
+        sa = self._concatenate(s, action)
+        assert sa.ndim == 2
+        return self.decoder(sa).squeeze(1)
+
+class MlpDiscriminator(torch.nn.Module):
+    """MLP similar to stable_baselines3.common.policies.ActorCriticPolicy."""
+
+    def __init__(self, env=None):
+        super().__init__()
+        self.flatten = torch.nn.Flatten(start_dim=1, end_dim=-1)
+        self.mlp = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), # 42 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(64), # 64 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(1),  # 64 -> 1
+        )
+    
+    def forward(self, state, action):
+        flat = self.flatten(state)
+        sa = torch.cat((action, flat), -1)
+        assert sa.ndim == 2
+        return self.mlp(sa).squeeze(1)

src/util/__init__.py

@@ -0,0 +1 @@
+from src.util.render_env import *

src/util/render_env.py

@@ -0,0 +1,58 @@
+
+import stable_baselines3 as sb3
+from intersim.envs.intersimple import NRasterized
+
+
+def render_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    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
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+def render_options_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    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
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(render_env)