more refactoring

src/lerobot/policies/factory.py

@@ -337,7 +337,7 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
     elif isinstance(policy_cfg, XVLAConfig):
-        from lerobot.policies.xvla.processing_xvla import make_xvla_pre_post_processors
+        from lerobot.policies.xvla.processor_xvla import make_xvla_pre_post_processors
 
         processors = make_xvla_pre_post_processors(
             config=policy_cfg,

src/lerobot/scripts/lerobot_eval.py

@@ -45,17 +45,6 @@ Note that in both examples, the repo/folder should contain at least `config.json
 
 You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py
 """
-import sys
-import os
-
-# ABSOLUTE PATH TO YOUR PROJECT ROOT
-PROJECT_ROOT = "/home/jade_choghari/robot/lerobot"
-
-# Add root to sys.path BEFORE any imports
-if PROJECT_ROOT not in sys.path:
-    sys.path.insert(0, PROJECT_ROOT)
-from xvla.models.modeling_xvla import XVLA
-from xvla.models.processing_xvla import XVLAProcessor
 import concurrent.futures as cf
 import json
 import logging
@@ -166,13 +155,6 @@ def rollout(
         leave=False,
     )
 
-    model = XVLA.from_pretrained("/raid/jade/models/xvla-libero")
-    model.eval()
-    model.to("cuda")
-    processor = XVLAProcessor.from_pretrained("/raid/jade/models/xvla-libero", num_views=2)
-
-    from collections import deque
-    action_queue = deque(maxlen=30)
     check_env_attributes_and_types(env)
     while not np.all(done) and step < max_steps:
         # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
@@ -188,50 +170,15 @@ def rollout(
         # inputs = processor([observation[f"observation.images.image"], observation[f"observation.images.image2"]], observation["task"])
         observation = preprocessor(observation)
         observation["domain_id"] = torch.tensor([int(3)], dtype=torch.long).to("cuda")
-        # inputs_1 = policy._build_model_inputs(observation)
-        # for k in inputs.keys() & inputs_1.keys():   # intersection of keys
-        #     a = inputs[k].to("cuda")
-        #     b = inputs_1[k].to("cuda")
 
-        #     print(f"\n🔎 Key: {k}")
-
-        #     # Check shape
-        #     print("  shape:", a.shape, b.shape)
-
-        #     # Check if close
-        #     if torch.allclose(a, b, atol=1e-5, rtol=1e-5):
-        #         print("  ✔️ tensors are equal (allclose)")
-        #     else:
-        #         diff = torch.abs(a - b)
-        #         print("  ❌ tensors differ")
-        #         print("  max diff:", diff.max().item())
-        #         print("  mean diff:", diff.mean().item())
-        # breakpoint()
         with torch.inference_mode():
             action = policy.select_action(observation).to("cpu").numpy()
-            # if len(action_queue) == 0:
-            #     action = model.generate_actions(**inputs_1, steps=10)     # shape (1, 30, 20)
-            #     actions_np = action.detach().cpu().numpy()
-            #     # add each timestep as (1, 20)
-            #     for t in range(actions_np.shape[1]):
-            #         act_t = actions_np[:, t, :] 
-            #         action_queue.append(act_t)
-            #     action = action_queue.popleft()
-            # else:
-            #     action = action_queue.popleft()
         # action = postprocessor(action)
-        # breakpoint()
-        # .to("cpu").numpy()
         target_eef = action[:, :3]
         target_axis = Rotate6D_to_AxisAngle(action[:, 3:9])
         target_act = action[:, 9:10]
         action_numpy = np.concatenate([target_eef, target_axis, target_act], axis=-1)
 
-        # target_eef_1 = action_1[:, :3]
-        # target_axis_1 = Rotate6D_to_AxisAngle(action_1[:, 3:9])
-        # target_act_1 = action_1[:, 9:10]
-        # action_numpy_1 = np.concatenate([target_eef_1, target_axis_1, target_act_1], axis=-1)
-
         # Convert to CPU / numpy.
         # action_numpy: np.ndarray = action.to("cpu").numpy()
         assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)"

test_2.py

@@ -1,4 +1,4 @@
-from xvla.models.processing_xvla import XVLAProcessor
+from xvla.models.processor_xvla import XVLAProcessor
 from xvla.models.modeling_xvla import XVLA
 from xvla.models.configuration_xvla import XVLAConfig
 import torch