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857c2e9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | #!/usr/bin/env python3
"""
Run RBM inference locally: load a checkpoint from HuggingFace and compute per-frame progress
and success for a video (or .npy/.npz frames) and task instruction. Writes rewards .npy,
success-probs .npy, and a progress/success plot. Requires the robometer package.
Example:
python scripts/example_inference_local.py \\
--model-path aliangdw/qwen4b_pref_prog_succ_8_frames_all_part2 \\
--video /path/to/video.mp4 \\
--task "Pick up the red block and place it in the bin"
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import torch
from robometer.data.dataset_types import ProgressSample, Trajectory
from robometer.evals.eval_server import compute_batch_outputs
from robometer.evals.eval_viz_utils import create_combined_progress_success_plot, extract_frames
from robometer.utils.save import load_model_from_hf
from robometer.utils.setup_utils import setup_batch_collator
def load_frames_input(
video_or_array_path: str,
*,
fps: float = 1.0,
max_frames: int = 512,
) -> np.ndarray:
"""Load frames from a video path/URL or .npy/.npz file. Returns uint8 (T, H, W, C)."""
if video_or_array_path.endswith(".npy"):
frames_array = np.load(video_or_array_path)
elif video_or_array_path.endswith(".npz"):
with np.load(video_or_array_path, allow_pickle=False) as npz:
if "frames" in npz:
frames_array = npz["frames"].copy()
elif "arr_0" in npz:
frames_array = npz["arr_0"].copy()
else:
frames_array = next(iter(npz.values())).copy()
else:
frames_array = extract_frames(video_or_array_path, fps=fps, max_frames=max_frames)
if frames_array is None or frames_array.size == 0:
raise RuntimeError("Could not extract frames from video.")
if frames_array.dtype != np.uint8:
frames_array = np.clip(frames_array, 0, 255).astype(np.uint8)
if frames_array.ndim == 4 and frames_array.shape[1] in (1, 3) and frames_array.shape[-1] not in (1, 3):
frames_array = frames_array.transpose(0, 2, 3, 1)
return frames_array
def compute_rewards_per_frame_local(
model_path: str,
video_frames: np.ndarray,
task: str,
device: Optional[torch.device] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""Load RBM from HuggingFace and run inference; return per-frame progress and success arrays."""
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
exp_config, tokenizer, processor, reward_model = load_model_from_hf(
model_path=model_path,
device=device,
)
reward_model.eval()
batch_collator = setup_batch_collator(processor, tokenizer, exp_config, is_eval=True)
T = int(video_frames.shape[0])
traj = Trajectory(
frames=video_frames,
frames_shape=tuple(video_frames.shape),
task=task,
id="0",
metadata={"subsequence_length": T},
video_embeddings=None,
)
progress_sample = ProgressSample(trajectory=traj, sample_type="progress")
batch = batch_collator([progress_sample])
progress_inputs = batch["progress_inputs"]
for key, value in progress_inputs.items():
if hasattr(value, "to"):
progress_inputs[key] = value.to(device)
loss_config = getattr(exp_config, "loss", None)
is_discrete = (
getattr(loss_config, "progress_loss_type", "l2").lower() == "discrete"
if loss_config else False
)
num_bins = (
getattr(loss_config, "progress_discrete_bins", None)
or getattr(exp_config.model, "progress_discrete_bins", 10)
)
results = compute_batch_outputs(
reward_model,
tokenizer,
progress_inputs,
sample_type="progress",
is_discrete_mode=is_discrete,
num_bins=num_bins,
)
progress_pred = results.get("progress_pred", [])
progress_array = (
np.array(progress_pred[0], dtype=np.float32)
if progress_pred and len(progress_pred) > 0
else np.array([], dtype=np.float32)
)
outputs_success = results.get("outputs_success", {})
success_probs = outputs_success.get("success_probs", []) if outputs_success else []
success_array = (
np.array(success_probs[0], dtype=np.float32)
if success_probs and len(success_probs) > 0
else np.array([], dtype=np.float32)
)
return progress_array, success_array
def main() -> None:
parser = argparse.ArgumentParser(
description="Run RBM inference locally: load model from HuggingFace and compute per-frame progress and success.",
epilog="Outputs: <out>.npy (rewards), <out>_success_probs.npy, <out>_progress_success.png",
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument("--model-path", required=True, help="HuggingFace model id or local checkpoint path")
parser.add_argument("--video", required=True, help="Video path/URL or .npy/.npz with frames (T,H,W,C)")
parser.add_argument("--task", required=True, help="Task instruction for the trajectory")
parser.add_argument("--fps", type=float, default=1.0, help="FPS when sampling from video (default: 1.0)")
parser.add_argument("--max-frames", type=int, default=512, help="Max frames to extract from video (default: 512)")
parser.add_argument(
"--success-threshold",
type=float,
default=0.5,
help="Threshold for binary success in plot (default: 0.5)",
)
parser.add_argument("--out", default=None, help="Output path for rewards .npy (default: <video_stem>_rewards.npy)")
args = parser.parse_args()
video_path = Path(args.video)
out_path = Path(args.out) if args.out is not None else video_path.with_name(video_path.stem + "_rewards.npy")
frames = load_frames_input(
str(args.video),
fps=float(args.fps),
max_frames=int(args.max_frames),
)
rewards, success_probs = compute_rewards_per_frame_local(
model_path=args.model_path,
video_frames=frames,
task=args.task,
)
out_path.parent.mkdir(parents=True, exist_ok=True)
np.save(str(out_path), rewards)
success_path = out_path.with_name(out_path.stem + "_success_probs.npy")
np.save(str(success_path), success_probs)
show_success = success_probs.size > 0 and success_probs.size == rewards.size
success_binary = (success_probs > float(args.success_threshold)).astype(np.int32) if show_success else None
fig = create_combined_progress_success_plot(
progress_pred=rewards,
num_frames=int(frames.shape[0]),
success_binary=success_binary,
success_probs=success_probs if show_success else None,
success_labels=None,
title=f"Progress/Success — {video_path.name}",
)
plot_path = out_path.with_name(out_path.stem + "_progress_success.png")
fig.savefig(str(plot_path), dpi=200)
plt.close(fig)
summary = {
"video": str(video_path),
"num_frames": int(frames.shape[0]),
"model_path": args.model_path,
"out_rewards": str(out_path),
"out_success_probs": str(success_path),
"out_plot": str(plot_path),
"reward_min": float(np.min(rewards)) if rewards.size else None,
"reward_max": float(np.max(rewards)) if rewards.size else None,
"reward_mean": float(np.mean(rewards)) if rewards.size else None,
}
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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