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de0a2a7 041aacc de0a2a7 | 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 | """
Public VoxelMind inference — no Model.py / Affectors.py required.
Load the TorchScript artifact exported with scripts/export_scripted.py:
python scripts/export_scripted.py
"""
from __future__ import annotations
import argparse
from pathlib import Path
from typing import Sequence
import cv2
import numpy as np
import torch
import torch.nn.functional as F
TARGET_FPS = 22
TARGET_SIZE = (64, 64)
CLASS_NAMES: list[str] = [
"s",
"d",
"idle",
"mouse_up",
"jump",
"mouse_right",
"mouse_down",
"w",
"a",
"mouse_left",
"drop",
]
def frame_to_gray(frame_bgr: np.ndarray, target_size: tuple[int, int] = TARGET_SIZE) -> np.ndarray:
resized = cv2.resize(frame_bgr, target_size, interpolation=cv2.INTER_AREA)
return cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
def read_video_gray(path: str | Path, target_size: tuple[int, int] = TARGET_SIZE) -> list[np.ndarray]:
cap = cv2.VideoCapture(str(path))
frames: list[np.ndarray] = []
while cap.isOpened():
ok, frame = cap.read()
if not ok:
break
frames.append(frame_to_gray(frame, target_size))
cap.release()
if not frames:
raise ValueError(f"no frames in video: {path}")
return frames
def crop_temporal(frames: Sequence[np.ndarray], target_fps: int = TARGET_FPS) -> list[np.ndarray]:
n = len(frames)
if n >= target_fps:
start = (n - target_fps) // 2
return list(frames[start : start + target_fps])
padded = list(frames)
while len(padded) < target_fps:
padded.append(frames[-1])
return padded[:target_fps]
def frames_to_tensor(
frames_gray: Sequence[np.ndarray],
target_fps: int = TARGET_FPS,
target_size: tuple[int, int] = TARGET_SIZE,
) -> torch.Tensor:
video = torch.stack([torch.from_numpy(f) for f in frames_gray]).float() / 255.0
video = video.unsqueeze(0).unsqueeze(0)
video = F.interpolate(
video,
size=(len(frames_gray), target_size[0], target_size[1]),
mode="trilinear",
align_corners=False,
)
return video.squeeze(0)
def load_model(path: str | Path, device: str | torch.device = "cpu") -> torch.jit.ScriptModule:
model = torch.jit.load(str(path), map_location=device)
model.eval()
return model
def predict_tensor(
model: torch.jit.ScriptModule,
clip: torch.Tensor,
device: str | torch.device = "cpu",
topk: int = 3,
) -> list[tuple[str, float]]:
"""clip: [1, 22, 64, 64] or [1, 1, 22, 64, 64]"""
if clip.dim() == 4:
clip = clip.unsqueeze(0)
clip = clip.to(device)
with torch.inference_mode():
logits = model(clip)
probs = torch.softmax(logits, dim=-1).squeeze(0)
k = min(topk, probs.numel())
values, indices = probs.topk(k)
return [(CLASS_NAMES[i], float(v)) for v, i in zip(values, indices)]
def predict_video(
model: torch.jit.ScriptModule,
video_path: str | Path,
device: str | torch.device = "cpu",
topk: int = 3,
) -> list[tuple[str, float]]:
frames = crop_temporal(read_video_gray(video_path))
clip = frames_to_tensor(frames)
return predict_tensor(model, clip, device=device, topk=topk)
def main() -> None:
p = argparse.ArgumentParser(description="VoxelMind inference (TorchScript, no source model code)")
p.add_argument("--model", default="models/voxel_scripted.pt", help="TorchScript artifact")
p.add_argument("--video", required=True, help="Input .mp4 clip")
p.add_argument("--device", default="cpu", help="cpu | cuda")
p.add_argument("--topk", type=int, default=3)
args = p.parse_args()
model = load_model(args.model, args.device)
preds = predict_video(model, args.video, device=args.device, topk=args.topk)
for name, prob in preds:
print(f"{name:12s} {prob * 100:5.1f}%")
if __name__ == "__main__":
main()
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