Add files using upload-large-folder tool

policy/EVA/checkpoints/peg_insertion_eva/dataset_stats.pkl

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policy/EVA/imitate_episodes.py

@@ -82,10 +82,8 @@ def main(args):
     }
 
     if is_eval:
-        ckpt_name = "policy_best.ckpt"
-        print(f"Evaluating {ckpt_name}...")
-        # TODO: implement eval loop for EVA
-        print("EVA eval not yet implemented — use ACT eval with tactile wrapper")
+        print("Use tactile_tasks/eval_eva.py for evaluation:")
+        print(f"  python tactile_tasks/eval_eva.py --task {task_name.split('-')[1]}")
         return
 
     train_dataloader, val_dataloader, stats, _ = load_data(

policy/EVA/process_data.py

@@ -52,11 +52,12 @@ def process_episode(raw_path, save_path):
     gripper_state = gripper_qpos[:, 0:1]  # [T, 1]
     qpos = np.concatenate([joint_pos, gripper_state], axis=1).astype(np.float32)  # [T, 8]
 
-    # action: same as qpos (next-step joint target), following ACT convention
-    # Use qpos[1:] as action for qpos[:-1], pad last with repeat
-    action = np.zeros_like(qpos)
-    action[:-1] = qpos[1:]
-    action[-1] = qpos[-1]
+    # action: raw OSC_POSE delta commands [dx,dy,dz,drx,dry,drz,gripper] = 7D
+    # Pad gripper command as 8th dim to match state_dim
+    action = np.concatenate([
+        actions_raw,                                    # [T, 7] OSC deltas
+        np.sign(actions_raw[:, -1:]),                   # [T, 1] gripper: +1=close, -1=open
+    ], axis=1).astype(np.float32)                       # [T, 8]
 
     # Tactile window: for each control step t, take 5 tactile frames [t*5 : t*5+5]
     T_tac = len(tl)

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tactile_tasks/eval_eva.py

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+#!/usr/bin/env python3
+"""
+Evaluate trained EVA policy in robosuite environment.
+
+Same structure as eval_act.py, but adds tactile observation.
+
+Usage:
+  python tactile_tasks/eval_eva.py --task peg_insertion
+  python tactile_tasks/eval_eva.py --task peg_insertion --temporal_agg
+  python tactile_tasks/eval_eva.py --task peg_insertion --ckpt policy_last.ckpt
+"""
+
+import os
+import sys
+import argparse
+import pickle
+
+import numpy as np
+import torch
+
+# Setup paths
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "policy", "EVA"))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "policy", "EVA", "detr"))
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from eva_policy import EVAPolicy
+from tactile_tasks.uskin_sensor import USkinSensor
+
+
+def load_policy(ckpt_dir, ckpt_name, policy_config):
+    """Load trained EVA policy and dataset stats."""
+    policy = EVAPolicy(policy_config)
+    ckpt_path = os.path.join(ckpt_dir, ckpt_name)
+    loading_status = policy.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
+    print(f"Loaded: {ckpt_path} ({loading_status})")
+    policy.cuda()
+    policy.eval()
+
+    stats_path = os.path.join(ckpt_dir, "dataset_stats.pkl")
+    with open(stats_path, "rb") as f:
+        stats = pickle.load(f)
+
+    return policy, stats
+
+
+def get_obs(env, camera_names, stats):
+    """Extract qpos and images from robosuite env, apply normalization."""
+    robot = env.robots[0]
+
+    # qpos: joint_pos(7) + normalized_gripper(1) = 8D
+    joint_pos = np.array(env.sim.data.qpos[robot._ref_joint_pos_indexes])
+    gripper_idx = robot._ref_gripper_joint_pos_indexes.get("right", [])
+    gripper_val = env.sim.data.qpos[gripper_idx][0] / 0.8 if len(gripper_idx) else 0.0
+    qpos = np.concatenate([joint_pos, [gripper_val]]).astype(np.float32)
+
+    # Normalize qpos
+    qpos_norm = (qpos - stats["qpos_mean"]) / stats["qpos_std"]
+    qpos_tensor = torch.from_numpy(qpos_norm).float().cuda().unsqueeze(0)
+
+    # Images: stack cameras -> (1, num_cams, 3, H, W)
+    obs = env._get_observations()
+    cam_map = {"agentview": "agentview_image", "eye_in_hand": "robot0_eye_in_hand_image"}
+    images = []
+    for cam in camera_names:
+        key = cam_map.get(cam, cam + "_image")
+        img = obs[key]  # (H, W, 3) uint8
+        img = np.transpose(img, (2, 0, 1))  # -> (3, H, W)
+        images.append(img)
+    images = np.stack(images, axis=0)  # (num_cams, 3, H, W)
+    images = torch.from_numpy(images / 255.0).float().cuda().unsqueeze(0)
+
+    return qpos_tensor, images
+
+
+def get_tactile(sensor):
+    """Read one control-step worth of tactile data: [1, 2, 5, 4, 4, 3]."""
+    left_frames = []
+    right_frames = []
+    for _ in range(USkinSensor.FREQ_MULTIPLIER):
+        sensor.update()
+        left_frames.append(sensor._left_forces.copy())
+        right_frames.append(sensor._right_forces.copy())
+    tl = np.stack(left_frames)   # [5, 4, 4, 3]
+    tr = np.stack(right_frames)  # [5, 4, 4, 3]
+    tactile = np.stack([tl, tr])  # [2, 5, 4, 4, 3]
+    return torch.from_numpy(tactile).float().cuda().unsqueeze(0)
+
+
+def run_eval(task, ckpt_dir, ckpt_name, num_rollouts, temporal_agg, camera_names,
+             render=False):
+    """Run evaluation rollouts."""
+    import yaml
+
+    # Load EVA config
+    config_path = os.path.join(os.path.dirname(__file__), "..", "policy", "EVA", "train_config.yaml")
+    with open(config_path, "r") as f:
+        cfg = yaml.safe_load(f)
+
+    state_dim = cfg["state_dim"]
+    chunk_size = cfg["chunk_size"]
+
+    policy_config = {
+        "lr": cfg["lr"],
+        "num_queries": chunk_size,
+        "kl_weight": cfg["kl_weight"],
+        "event_weight": cfg.get("event_weight", 0.1),
+        "hidden_dim": cfg["hidden_dim"],
+        "dim_feedforward": cfg["dim_feedforward"],
+        "lr_backbone": 1e-5,
+        "backbone": "resnet18",
+        "enc_layers": 4,
+        "dec_layers": 7,
+        "nheads": 8,
+        "camera_names": camera_names,
+        "state_dim": state_dim,
+        "chunk_size": chunk_size,
+    }
+
+    policy, stats = load_policy(ckpt_dir, ckpt_name, policy_config)
+    post_process = lambda a: a * stats["action_std"] + stats["action_mean"]
+
+    # Create environment
+    from tactile_tasks.collect_data import create_env, TASK_CONFIGS
+    settle_steps = 50
+    max_timesteps = max(TASK_CONFIGS[task]["horizon"], 900)
+    env = create_env(task, has_renderer=render)
+    env.horizon = max_timesteps + settle_steps
+
+    sensor = USkinSensor(env.sim)
+    query_frequency = 1 if temporal_agg else chunk_size
+
+    successes = []
+    for ep in range(num_rollouts):
+        env.reset()
+        for _ in range(settle_steps):
+            env.step(np.zeros(7))
+
+        if temporal_agg:
+            all_time_actions = torch.zeros(
+                [max_timesteps, max_timesteps + chunk_size, state_dim]
+            ).cuda()
+
+        all_actions = None
+        episode_success = False
+
+        with torch.inference_mode():
+            for t in range(max_timesteps):
+                qpos, images = get_obs(env, camera_names, stats)
+                tactile = get_tactile(sensor)
+
+                # Query policy
+                if t % query_frequency == 0:
+                    all_actions = policy(qpos, images, tactile)  # (1, chunk_size, state_dim)
+
+                if temporal_agg:
+                    all_time_actions[[t], t:t + chunk_size] = all_actions
+                    actions_for_curr_step = all_time_actions[:, t]
+                    actions_populated = torch.all(actions_for_curr_step != 0, axis=1)
+                    actions_for_curr_step = actions_for_curr_step[actions_populated]
+                    k = 0.01
+                    exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step)))
+                    exp_weights = exp_weights / exp_weights.sum()
+                    exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
+                    raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
+                else:
+                    raw_action = all_actions[:, t % query_frequency]
+
+                # Post-process: denormalize, take first 7D (drop padding dim)
+                action = post_process(raw_action.squeeze(0).cpu().numpy())
+                action = action[:7]  # 8D -> 7D OSC_POSE
+
+                env.step(action)
+                if render:
+                    env.render()
+
+                if env._check_success():
+                    episode_success = True
+
+        successes.append(episode_success)
+        status = "SUCCESS" if episode_success else "FAIL"
+        print(f"  [{ep+1}/{num_rollouts}] {status}  (t={t+1})")
+
+    success_rate = sum(successes) / len(successes)
+    print(f"\n{'='*40}")
+    print(f"Task: {task}")
+    print(f"Checkpoint: {ckpt_dir}/{ckpt_name}")
+    print(f"Mode: {'temporal_agg' if temporal_agg else 'chunk'}")
+    print(f"Success: {sum(successes)}/{num_rollouts} ({success_rate*100:.1f}%)")
+    print(f"{'='*40}")
+
+    env.close()
+    return success_rate
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, required=True,
+                        choices=["precision_grasp", "peg_insertion", "gentle_stack"])
+    parser.add_argument("--ckpt_dir", type=str, default=None,
+                        help="checkpoint dir (default: policy/EVA/checkpoints/{task}_eva)")
+    parser.add_argument("--ckpt", type=str, default="policy_best.ckpt")
+    parser.add_argument("--num_rollouts", type=int, default=5)
+    parser.add_argument("--temporal_agg", action="store_true")
+    parser.add_argument("--render", action="store_true")
+    parser.add_argument("--cameras", nargs="+", default=["agentview", "eye_in_hand"])
+    args = parser.parse_args()
+
+    if args.ckpt_dir is None:
+        args.ckpt_dir = os.path.join(
+            os.path.dirname(__file__), "..", "policy", "EVA", "checkpoints", f"{args.task}_eva"
+        )
+
+    run_eval(
+        task=args.task,
+        ckpt_dir=args.ckpt_dir,
+        ckpt_name=args.ckpt,
+        num_rollouts=args.num_rollouts,
+        temporal_agg=args.temporal_agg,
+        camera_names=args.cameras,
+        render=args.render,
+    )