File size: 7,301 Bytes
411f238 | 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 | from __future__ import annotations
import hashlib
import json
import math
import re
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
VARIANTS = {
"small_cnn": {"width": 32, "depth": 3, "chunk": 1, "dropout": 0.05},
"base_cnn": {"width": 64, "depth": 4, "chunk": 1, "dropout": 0.10},
"chunk_cnn": {"width": 48, "depth": 4, "chunk": 8, "dropout": 0.10},
"ensemble_cnn": {"width": 64, "depth": 5, "chunk": 8, "dropout": 0.15},
}
class VisionBackbone(nn.Module):
def __init__(self, width, depth):
super().__init__()
layers = [nn.Conv2d(3, width, 5, 2, 2), nn.GroupNorm(max(1, width // 8), width), nn.SiLU()]
channels = width
for index in range(depth - 1):
next_channels = min(width * (2 ** min(index + 1, 2)), 256)
layers += [nn.Conv2d(channels, next_channels, 3, 2, 1),
nn.GroupNorm(max(1, next_channels // 8), next_channels), nn.SiLU()]
channels = next_channels
layers += [nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(), nn.Linear(channels, 384), nn.SiLU()]
self.net = nn.Sequential(*layers)
def forward(self, images):
return self.net(images)
class PolicyNet(nn.Module):
def __init__(self, config):
super().__init__()
variant = config["variant"]
cfg = VARIANTS[variant]
self.chunk = int(cfg["chunk"])
self.vision = VisionBackbone(int(cfg["width"]), int(cfg["depth"]))
self.proprio = nn.Sequential(nn.Linear(int(config["proprio_dim"]), 128), nn.LayerNorm(128), nn.SiLU())
self.text = nn.Sequential(nn.Linear(int(config["text_dim"]), 128), nn.LayerNorm(128), nn.SiLU())
self.task = nn.Embedding(int(config["task_count"]), 32)
self.difficulty = nn.Embedding(int(config["difficulty_count"]), 16)
self.head = nn.Sequential(
nn.Linear(384 + 128 + 128 + 32 + 16, 512), nn.LayerNorm(512), nn.SiLU(),
nn.Dropout(float(cfg["dropout"])), nn.Linear(512, 256), nn.SiLU(),
nn.Linear(256, self.chunk * 7))
def forward(self, images, proprio, text, task_id, difficulty_id):
if images.ndim != 4:
raise ValueError("image batch must have four dimensions")
if images.shape[-1] == 3:
images = images.permute(0, 3, 1, 2)
images = images.float()
if images.max() > 2:
images = images / 255.0
images = F.interpolate(images, size=(96, 96), mode="bilinear", align_corners=False)
fused = torch.cat([
self.vision(images), self.proprio(proprio.float()), self.text(text.float()),
self.task(task_id.clamp(0, self.task.num_embeddings - 1)),
self.difficulty(difficulty_id.clamp(0, self.difficulty.num_embeddings - 1)),
], dim=-1)
return torch.tanh(self.head(fused)).reshape(-1, self.chunk, 7)
def _text_vector(text, dim):
result = np.zeros(dim, dtype=np.float32)
for token in re.findall(r"[a-z0-9_]+", str(text).lower()):
value = int.from_bytes(hashlib.blake2b(token.encode(), digest_size=8).digest(), "little")
result[value % dim] += 1.0 if value & 1 else -1.0
norm = float(np.linalg.norm(result))
return result / norm if norm else result
def _proprio(obs, config):
value = np.asarray(obs.get("proprio", np.zeros(25, dtype=np.float32)), dtype=np.float32).reshape(-1)
if value.size == 21:
value = np.pad(value, (0, 4))
if value.size != 25:
raise ValueError(f"proprio must have 25 values, got {value.size}")
step = float(obs.get("step", 0))
horizon = float(obs.get("horizon", 320) or 320)
value = np.concatenate([value, np.asarray([step / max(horizon, 1.0)], dtype=np.float32)])
if value.size < int(config["proprio_dim"]):
value = np.pad(value, (0, int(config["proprio_dim"]) - value.size))
value = value[: int(config["proprio_dim"])]
mean = np.asarray(config["proprio_mean"], dtype=np.float32)
std = np.asarray(config["proprio_std"], dtype=np.float32)
return ((value - mean) / np.maximum(std, 1e-4)).astype(np.float32)
class Policy:
def __init__(self, model, config, device):
self.model, self.config, self.device = model, config, device
self.chunk = int(config["chunk"])
self.last_step = None
self.chunks = {}
self.last_action = np.zeros(7, dtype=np.float32)
self.task_to_id = config["task_to_id"]
self.difficulty_to_id = config["difficulty_to_id"]
@torch.inference_mode()
def act(self, obs):
image = np.asarray(obs.get("image", np.zeros((96, 96, 3), dtype=np.uint8)))
if image.ndim == 2:
image = np.repeat(image[..., None], 3, axis=-1)
if image.shape[-1] == 4:
image = image[..., :3]
if image.shape[-1] != 3:
raise ValueError("image must have 3 or 4 channels")
step = int(obs.get("step", 0))
if self.last_step is None or step == 0 or step != self.last_step + 1:
self.chunks = {}
if step == self.last_step:
return self.last_action.copy()
task = str(obs.get("task", ""))
difficulty = str(obs.get("difficulty", "") or "")
text = f"task {task} difficulty {difficulty} instruction {obs.get('instruction', '')}"
text_feature = _text_vector(text, int(self.config["text_dim"]))
proprio = _proprio(obs, self.config)
task_id = self.task_to_id.get(task, len(self.task_to_id))
difficulty_id = self.difficulty_to_id.get(difficulty, len(self.difficulty_to_id))
raw = self.model(
torch.from_numpy(image).unsqueeze(0).to(self.device),
torch.from_numpy(proprio).unsqueeze(0).to(self.device),
torch.from_numpy(text_feature).unsqueeze(0).to(self.device),
torch.tensor([task_id], device=self.device),
torch.tensor([difficulty_id], device=self.device),
)[0].float().cpu().numpy()
self.chunks[step] = raw
candidates, weights = [], []
for start, chunk in list(self.chunks.items()):
offset = step - start
if 0 <= offset < self.chunk:
candidates.append(chunk[offset])
weights.append(math.exp(-0.55 * offset))
else:
del self.chunks[start]
action = np.average(np.asarray(candidates), axis=0, weights=np.asarray(weights))
action = np.clip(action, -1.0, 1.0).astype(np.float32)
if action[6] > 0.12:
action[6] = 1.0
elif action[6] < -0.12:
action[6] = -1.0
self.last_step, self.last_action = step, action.copy()
return action
def load_policy(model_dir: str, device: str, dtype: str):
root = Path(model_dir)
config = json.loads((root / "vla_config.json").read_text())
selected = torch.device(device if str(device).startswith("cuda") and torch.cuda.is_available() else "cpu")
model = PolicyNet(config).to(selected)
checkpoint = torch.load(root / "model.pt", map_location=selected, weights_only=True)
state = checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
model.load_state_dict(state, strict=True)
model.eval()
return Policy(model, config, selected)
|