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dd253d4 | 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 | #!/usr/bin/env python3
"""Minimal Robometer wrapper that resizes frames to fit in memory."""
from __future__ import annotations
import argparse
import os
from pathlib import Path
from typing import Optional, Tuple
import numpy as np
import torch
import cv2
# Must import unsloth first
import unsloth # noqa
from robometer.data.dataset_types import ProgressSample, Trajectory
from robometer.evals.eval_server import compute_batch_outputs
from robometer.evals.eval_viz_utils import extract_frames
from robometer.utils.save import load_model_from_hf
from robometer.utils.setup_utils import setup_batch_collator
def resize_frames(frames: np.ndarray, max_side: int = 256) -> np.ndarray:
"""Resize frames so max side = max_side."""
T, H, W, C = frames.shape
scale = max_side / max(H, W)
if scale < 1:
new_h = (int(H * scale) // 28) * 28 # must be multiple of 28 for qwen-vl
new_w = (int(W * scale) // 28) * 28
out = np.zeros((T, new_h, new_w, C), dtype=np.uint8)
for i in range(T):
out[i] = cv2.resize(frames[i], (new_w, new_h), interpolation=cv2.INTER_AREA)
return out
return frames
_CACHED_MODEL = None
def load_robometer_once(model_path: str = "robometer/Robometer-4B", device: str = "cuda"):
"""Load model once globally."""
global _CACHED_MODEL
if _CACHED_MODEL is None:
device = torch.device(device)
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)
_CACHED_MODEL = dict(
model=reward_model, tokenizer=tokenizer, processor=processor,
batch_collator=batch_collator, exp_config=exp_config, device=device,
)
return _CACHED_MODEL
def compute_rewards(
video_frames: np.ndarray,
task: str,
max_side: int = 256,
state: Optional[dict] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Compute per-frame progress and success probability.
Args:
video_frames: (T, H, W, C) uint8
task: task instruction string
max_side: resize frames so max dim = max_side
Returns:
(progress, success): both arrays of length T
"""
if state is None:
state = load_robometer_once()
frames = resize_frames(video_frames, max_side=max_side)
T = int(frames.shape[0])
traj = Trajectory(
frames=frames,
frames_shape=tuple(frames.shape),
task=task,
id="0",
metadata={"subsequence_length": T},
video_embeddings=None,
)
progress_sample = ProgressSample(trajectory=traj, sample_type="progress")
batch = state["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(state["device"])
loss_config = getattr(state["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(state["exp_config"].model, "progress_discrete_bins", 10)
)
with torch.inference_mode():
results = compute_batch_outputs(
state["model"], state["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 score_video_file(
video_path: str,
task: str,
fps: float = 3.0,
max_frames: int = 8,
max_side: int = 256,
state: Optional[dict] = None,
) -> Tuple[np.ndarray, np.ndarray]:
frames = extract_frames(video_path, fps=fps, max_frames=max_frames)
if frames is None or frames.size == 0:
raise RuntimeError(f"Could not extract frames from {video_path}")
return compute_rewards(frames, task, max_side=max_side, state=state)
if __name__ == "__main__":
p = argparse.ArgumentParser()
p.add_argument("--video", required=True)
p.add_argument("--task", required=True)
p.add_argument("--model-path", default="robometer/Robometer-4B")
p.add_argument("--fps", type=float, default=3.0)
p.add_argument("--max-frames", type=int, default=8)
p.add_argument("--max-side", type=int, default=256)
p.add_argument("--out", default="/tmp/robometer_out")
args = p.parse_args()
print(f"Loading model from {args.model_path}...")
state = load_robometer_once(args.model_path)
print(f"Scoring video: {args.video}")
progress, success = score_video_file(
args.video, args.task,
fps=args.fps, max_frames=args.max_frames, max_side=args.max_side,
state=state,
)
Path(args.out).mkdir(parents=True, exist_ok=True)
np.save(os.path.join(args.out, "progress.npy"), progress)
np.save(os.path.join(args.out, "success.npy"), success)
print(f"Progress: shape={progress.shape}, values={progress.tolist()}")
print(f"Success: shape={success.shape}, values={success.tolist()}")
print(f"Mean progress: {float(np.mean(progress)):.3f}")
print(f"Mean success: {float(np.mean(success)):.3f}")
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