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68442cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | #!/usr/bin/env python
"""
Evaluate DoVLA-Hybrid checkpoint with DIRECT selection.
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from dovla_cil.data.datasets import CILDataset
from dovla_cil.models.dovla_hybrid import DoVLAHybrid
from dovla_cil.utils.io import write_json
def evaluate_hybrid_checkpoint(
checkpoint_path: str | Path,
dataset_dir: str | Path,
output_path: str | Path | None = None,
device: str = "auto",
) -> dict:
"""Evaluate Hybrid with direct selection."""
resolved_device = (
"cuda" if device == "auto" and torch.cuda.is_available() else "cpu"
if device == "auto"
else device
)
checkpoint = torch.load(checkpoint_path, map_location=resolved_device, weights_only=False)
args = checkpoint["args"]
model = DoVLAHybrid(
obs_dim=70,
action_dim=32,
lang_dim=0,
d_model=args["d_model"],
n_heads=args["n_heads"],
n_layers=args["n_layers"],
d_ff=args["d_ff"],
dropout=0.1
).to(resolved_device)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
dataset = CILDataset(dataset_dir)
val_group_ids = list(dataset.group_ids)
selected_success = 0
oracle_success = 0
top1_correct = 0
def pad(vec, target):
if len(vec) >= target:
return vec[:target]
return vec + [0.0] * (target - len(vec))
with torch.no_grad():
for group_id in val_group_ids:
records = dataset.get_group(group_id)
if not records:
continue
obs_list = []
for r in records:
obs_data = r.observation_inline
if "state" in obs_data:
obs = list(obs_data["state"])
else:
obs = []
for v in obs_data.values():
if isinstance(v, list):
obs.extend(v)
elif isinstance(v, (int, float)):
obs.append(v)
obs = pad([float(x) for x in obs], 70)
obs_list.append(obs)
obs = torch.tensor([obs_list[0]], dtype=torch.float32, device=resolved_device)
actions = torch.tensor(
[pad(r.action_chunk.flat_values, 32) for r in records],
dtype=torch.float32,
device=resolved_device
).unsqueeze(0)
# DIRECT prediction
pred_rewards, pred_success_probs = model(obs, actions, lang=None)
# Hybrid selection
hybrid_scores = (pred_success_probs * pred_rewards).squeeze(0).cpu().tolist()
selected = max(range(len(records)), key=lambda i: hybrid_scores[i])
utilities = [r.reward.score for r in records]
best_utility = max(utilities)
is_selected_success = int(records[selected].reward.terminal_success)
has_oracle_success = int(any(r.reward.terminal_success for r in records))
is_top1 = int(abs(utilities[selected] - best_utility) < 1e-6)
selected_success += is_selected_success
oracle_success += has_oracle_success
top1_correct += is_top1
group_count = len(val_group_ids)
result = {
"checkpoint": str(checkpoint_path),
"dataset": str(dataset_dir),
"num_groups": group_count,
"selected_success_rate": selected_success / group_count,
"oracle_success_rate": oracle_success / group_count,
"top1_action_selection": top1_correct / group_count,
"approach": "hybrid_direct_scoring"
}
if output_path:
write_json(result, output_path)
return result
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--dataset", type=Path, required=True)
parser.add_argument("--out", type=Path, required=True)
parser.add_argument("--device", default="auto")
args = parser.parse_args(argv)
result = evaluate_hybrid_checkpoint(
args.checkpoint, args.dataset, args.out, args.device
)
print(f"Selected success rate: {result['selected_success_rate']:.4f}")
print(f"Top-1 selection: {result['top1_action_selection']:.4f}")
print(f"Oracle success: {result['oracle_success_rate']:.4f}")
return 0
if __name__ == "__main__":
sys.exit(main())
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