Adding onnx inference.

.devcontainer/Dockerfile

@@ -43,6 +43,7 @@ RUN pip3 install warp-lang scikit-learn casadi mujoco pin
 RUN pip install jupyter ipykernel
 RUN pip install cyclonedds pygame 
 RUN pip install pynput pygame 
+RUN pip install onnx onnxruntime
 
 # Set environmental variables required for using ROS 
 RUN echo 'source /opt/ros/humble/setup.bash' >> ~/.bashrc

Go2Py/control/cat.py

@@ -6,6 +6,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+import onnxruntime
+
 class RunningMeanStd(nn.Module):
     def __init__(self, shape = (), epsilon=1e-08):
         super(RunningMeanStd, self).__init__()
@@ -88,16 +90,44 @@ class Agent(nn.Module):
         action_mean = self.actor_mean(x)
         return action_mean
 
+    def forward(self, x):
+        action_mean = self.actor_mean(self.obs_rms(x, update = False))
+        return action_mean
+
 class Policy:
     def __init__(self, checkpoint_path):
         self.agent = Agent()
         actor_sd = torch.load(checkpoint_path, map_location="cpu")
         self.agent.load_state_dict(actor_sd)
+        onnx_file_name = checkpoint_path.replace(".pt", ".onnx")
+
+        dummy_input = torch.randn(1, 45)
+        with torch.no_grad():
+            torch_out = self.agent(dummy_input)
+
+        torch.onnx.export(
+            self.agent,                          # The model being converted
+            dummy_input,                    # An example input for the model
+            onnx_file_name,                 # Output file name
+            export_params=True,             # Store trained parameter weights inside the model file
+            opset_version=11,               # ONNX version (opset) to export to, adjust as needed
+            do_constant_folding=True,       # Whether to perform constant folding optimization
+            input_names=['input'],          # Name of the input in the ONNX graph (can be customized)
+            output_names=['action'],  # Name of the output (assuming get_action and get_value are key outputs)
+        )
+
+        self.ort_session = onnxruntime.InferenceSession(onnx_file_name, providers=["CPUExecutionProvider"])
+        ort_inputs = {'input': dummy_input.numpy()}
+        ort_outs = self.ort_session.run(None, ort_inputs)
+        np.testing.assert_allclose(torch_out.numpy(), ort_outs[0], rtol=1e-03, atol=1e-05)
+        print("Exported model has been tested with ONNXRuntime, and the result looks good!")
 
     def __call__(self, obs, info):
-        with torch.no_grad():
+        #with torch.no_grad():
-            action = self.agent.get_action(self.agent.obs_rms(obs.unsqueeze(0), update = False))
+        #    action = self.agent.get_action(self.agent.obs_rms(obs.unsqueeze(0), update = False))
-        return action
+        ort_inputs = {'input': obs[np.newaxis].astype(np.float32)}
+        ort_outs = self.ort_session.run(None, ort_inputs)
+        return ort_outs[0]
 
 class CommandInterface:
     def __init__(self, limits=None):
@@ -211,6 +241,7 @@ class CaTAgent:
         self.joint_vel_target = np.zeros(12)
         self.torques = np.zeros(12)
         self.contact_state = np.ones(4)
+        self.foot_contact_forces_mag = np.zeros(4)
         self.test = 0
 
     def wait_for_state(self):
@@ -228,6 +259,7 @@ class CaTAgent:
             self.dof_pos = np.array(joint_state['q'])[self.unitree_to_policy_map[:, 1]]
             self.dof_vel = np.array(joint_state['dq'])[self.unitree_to_policy_map[:, 1]]
             self.body_angular_vel = self.robot.getIMU()["gyro"]
+            self.foot_contact_forces_mag = self.robot.getFootContact()
 
         ob = np.concatenate(
             (
@@ -241,12 +273,16 @@ class CaTAgent:
             axis=0,
         )
 
-        return torch.tensor(ob, device=self.device).float()
+        #return torch.tensor(ob, device=self.device).float()
+        return ob
 
     def publish_action(self, action, hard_reset=False):
         # command_for_robot = UnitreeLowCommand()
+        #self.joint_pos_target = (
+        #    action[0, :12].detach().cpu().numpy() * 0.25
+        #).flatten()
         self.joint_pos_target = (
-            action[0, :12].detach().cpu().numpy() * 0.25
+            action[0, :12] * 0.25
         ).flatten()
         self.joint_pos_target += self.default_dof_pos
         self.joint_vel_target = np.zeros(12)
@@ -294,7 +330,8 @@ class CaTAgent:
             "contact_state": self.contact_state[np.newaxis, :],
             "body_linear_vel_cmd": self.commands[np.newaxis, 0:2],
             "body_angular_vel_cmd": self.commands[np.newaxis, 2:],
-            "torques": self.torques
+            "torques": self.torques,
+            "foot_contact_forces_mag": self.foot_contact_forces_mag.copy()
         }
 
         self.timestep += 1