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9c74dfe | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import numpy as np
import torch
from omegaconf import OmegaConf
from eval.run_reveal_benchmark import load_model, _resolve_checkpoint_from_config
from sim_reveal.dataset import collect_teacher_dataset, save_teacher_dataset
from sim_reveal.procedural_envs import render_views_from_state
from train.dataset_build_utils import dataset_version_with_suffix, output_dataset_path
def _render_history(
proxy_name: str,
history_render_states: list[dict[str, Any]],
resolution: int,
) -> tuple[list[np.ndarray], list[np.ndarray], list[np.ndarray]]:
history_images: list[np.ndarray] = []
history_depths: list[np.ndarray] = []
history_depth_valid: list[np.ndarray] = []
for render_state in history_render_states:
rendered = render_views_from_state(
proxy_name=proxy_name,
render_state=render_state,
resolution=resolution,
include_depth=True,
)
history_images.append(
np.stack([rendered["front"], rendered["wrist_left"], rendered["wrist_right"]], axis=0).astype(np.uint8)
)
history_depths.append(
np.stack([rendered["front_depth"], rendered["wrist_left_depth"], rendered["wrist_right_depth"]], axis=0)[:, None, :, :].astype(np.float32)
)
history_depth_valid.append(
np.stack(
[rendered["front_depth_valid"], rendered["wrist_left_depth_valid"], rendered["wrist_right_depth_valid"]],
axis=0,
)[:, None, :, :].astype(np.float32)
)
return history_images, history_depths, history_depth_valid
def _prepare_model_inputs(
observation: dict[str, Any],
sample: dict[str, Any],
device: torch.device,
resolution: int,
) -> dict[str, Any]:
history_render_states = list(sample.get("history_render_states", []))
history_images, history_depths, history_depth_valid = _render_history(
proxy_name=str(sample["proxy_name"]),
history_render_states=history_render_states,
resolution=resolution,
)
if history_images:
history_images_tensor = torch.from_numpy(np.stack(history_images, axis=0)).permute(0, 1, 4, 2, 3).unsqueeze(0).float() / 255.0
history_depths_tensor = torch.from_numpy(np.stack(history_depths, axis=0)).unsqueeze(0).float()
history_depth_valid_tensor = torch.from_numpy(np.stack(history_depth_valid, axis=0)).unsqueeze(0).float()
else:
history_images_tensor = torch.zeros((1, 0, 3, 3, resolution, resolution), dtype=torch.float32)
history_depths_tensor = torch.zeros((1, 0, 3, 1, resolution, resolution), dtype=torch.float32)
history_depth_valid_tensor = torch.zeros_like(history_depths_tensor)
proprio_dim = observation["proprio"].shape[0]
return {
"images": torch.from_numpy(observation["images"]).permute(0, 3, 1, 2).unsqueeze(0).float().to(device) / 255.0,
"depths": torch.from_numpy(observation["depths"]).unsqueeze(0).float().to(device),
"depth_valid": torch.from_numpy(observation["depth_valid"]).unsqueeze(0).float().to(device),
"camera_intrinsics": torch.from_numpy(observation["camera_intrinsics"]).unsqueeze(0).float().to(device),
"camera_extrinsics": torch.from_numpy(observation["camera_extrinsics"]).unsqueeze(0).float().to(device),
"proprio": torch.from_numpy(observation["proprio"]).unsqueeze(0).float().to(device),
"texts": [str(observation["text"])],
"task_names": [str(sample["task_name"])],
"task_ids": torch.as_tensor([int(sample["task_id"])], dtype=torch.long, device=device),
"history_images": history_images_tensor.to(device),
"history_depths": history_depths_tensor.to(device),
"history_depth_valid": history_depth_valid_tensor.to(device),
"history_camera_intrinsics": torch.from_numpy(
sample.get("history_camera_intrinsics", np.zeros((0, 3, 3, 3), dtype=np.float32))
).unsqueeze(0).float().to(device),
"history_camera_extrinsics": torch.from_numpy(
sample.get("history_camera_extrinsics", np.zeros((0, 3, 4, 4), dtype=np.float32))
).unsqueeze(0).float().to(device),
"history_camera_valid_mask": torch.from_numpy(
sample.get("history_camera_valid_mask", np.zeros((0, 3), dtype=np.float32))
).unsqueeze(0).float().to(device),
"history_proprio": torch.from_numpy(
sample.get("history_proprio", np.zeros((0, proprio_dim), dtype=np.float32))
).unsqueeze(0).float().to(device),
"history_actions": torch.from_numpy(
sample.get("history_actions", np.zeros((0, sample["action_chunk"].shape[-1]), dtype=np.float32))
).unsqueeze(0).float().to(device),
}
def _proposal_target_builder(model: torch.nn.Module, device: torch.device, resolution: int):
def _build(env: Any, observation: dict[str, Any], sample: dict[str, Any]) -> dict[str, Any]:
with torch.inference_mode():
outputs = model(
**_prepare_model_inputs(observation, sample, device, resolution),
plan=False,
use_planner=False,
use_world_model=False,
use_proposal_candidates=True,
)
proposal_candidates = outputs["proposal_candidates"][0].detach().float().cpu().numpy().astype(np.float32)
outcomes = [env.evaluate_action_chunk(candidate, rollout_horizon=env.rollout_horizon) for candidate in proposal_candidates]
proposal_target_retrieval_success = np.asarray([item["retrieval_success"] for item in outcomes], dtype=np.float32)
proposal_target_risk = np.clip(
np.asarray([item["final_disturbance_cost"] + item["reocclusion_rate"] for item in outcomes], dtype=np.float32),
0.0,
1.0,
).astype(np.float32)
proposal_target_utility = np.asarray(
[float(env.candidate_outcome_utility(item)) for item in outcomes],
dtype=np.float32,
)
return {
"proposal_target_action_chunks": proposal_candidates,
"proposal_target_retrieval_success": proposal_target_retrieval_success,
"proposal_target_risk": proposal_target_risk,
"proposal_target_utility": proposal_target_utility,
"proposal_target_mode_names": list(outputs.get("proposal_mode_names", [["unknown"]])[0]),
}
return _build
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True)
parser.add_argument("--checkpoint", default=None)
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--train-output", default=None)
parser.add_argument("--val-output", default=None)
parser.add_argument("--dataset-suffix", default="selector_align")
args = parser.parse_args()
cfg = OmegaConf.load(args.config)
checkpoint_path = Path(args.checkpoint) if args.checkpoint else _resolve_checkpoint_from_config(args.config)
device = torch.device(args.device)
model, _ = load_model(checkpoint_path, device)
model.eval()
resolution = int(cfg.data.resolution)
builder = _proposal_target_builder(model, device, resolution)
dataset_version = dataset_version_with_suffix(
str(cfg.data.get("dataset_version", "reveal_proxy_v6")),
args.dataset_suffix,
)
train_output = Path(args.train_output) if args.train_output else output_dataset_path(cfg.data.train_dataset_path, args.dataset_suffix)
val_output = Path(args.val_output) if args.val_output else output_dataset_path(cfg.data.val_dataset_path, args.dataset_suffix)
bundles: dict[str, dict[str, Any]] = {}
for split, episodes_per_proxy, seed_offset, output_path in (
("train", int(cfg.data.train_episodes_per_proxy), 0, train_output),
("val", int(cfg.data.val_episodes_per_proxy), 10_000, val_output),
):
bundle = collect_teacher_dataset(
proxy_names=OmegaConf.to_container(cfg.data.proxies, resolve=True),
episodes_per_proxy=episodes_per_proxy,
resolution=resolution,
seed=int(cfg.data.seed) + seed_offset,
chunk_horizon=int(cfg.data.chunk_horizon),
rollout_horizon=int(cfg.data.rollout_horizon),
history_steps=int(cfg.data.get("history_steps", 2)),
planner_candidates=int(cfg.data.get("planner_candidates", 4)),
dataset_version=dataset_version,
proposal_target_builder=builder,
)
save_teacher_dataset(output_path, bundle)
bundles[split] = {
"output_path": str(output_path),
"samples": len(bundle["samples"]),
"dataset_version": dataset_version,
}
print(json.dumps({"phase": "dataset_saved", "split": split, **bundles[split]}), flush=True)
summary = {
"checkpoint": str(checkpoint_path),
"device": str(device),
"dataset_suffix": args.dataset_suffix,
"train": bundles["train"],
"val": bundles["val"],
}
summary_path = train_output.parent / f"proposal_dataset_build_{args.dataset_suffix}.json"
summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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