README.md

---
license: apache-2.0
base_model:
- nvidia/Alpamayo-R1-10B
---
nvidia/Alpamayo-R1-10B 4bit Model.

이모델은 자율주행 중 수집된  데이터로 이벤트를 예측하는 용도로 활용할 수 있습니다.
자율주행을 하는게 아니라 자율주행 중  특정 상황이 발생할 것을 알려주는 기능을 합니다.


```Runinfo
model download  ./Alpamayo-R1-10B-4bit 
 
GPU 12G/16G Memory Run able

12G Memory is  num_frames is 1 ~ 8, over OOM
 
Transformers is 4.57.5 ( 5.0.0rc not run)

nvidia/Alpamayo-R1-10B 이 대용량 메모리를 요구하고 4bit 로 로딩하여 저장한 모델입니다.
12G 에서도 실행가능해졌습니다만  주어지는 프레임수는 1~8정도, 그 이상이면 OOM이 떨어집니다.
트랜스포머 버전 5.0.0rc에서는 동작하지 않습니다.

git clone https://github.com/NVlabs/alpamayo 하고
cd alpamayo
pip install .  로 설치해야 합니다만

pyproject.toml을 수정하는게 좋습니다.
python 3.13을 사용하면 requires-python = "==3.13.*"
transformers 와 torch를 라인을 제거하고 설치하면 설치된 버전이 교체되지 않습니다.
```
-----------------------------------
```python
import torch
import numpy as np
from alpamayo_r1.models.alpamayo_r1 import AlpamayoR1
from alpamayo_r1.load_physical_aiavdataset import load_physical_aiavdataset
from alpamayo_r1 import helper

model_path = "Alpamayo-R1-10B-4bit"
model = AlpamayoR1.from_pretrained(model_path, dtype=torch.bfloat16).to("cuda")

processor = helper.get_processor(model.tokenizer)

clip_id = "030c760c-ae38-49aa-9ad8-f5650a545d26"
print(f"Loading dataset for clip_id: {clip_id}...")
#need set access token or huggingface-cli login...
data = load_physical_aiavdataset(clip_id, t0_us=15_100_000,num_frames=1)
print("Dataset loaded.")

messages = helper.create_message(data["image_frames"].flatten(0, 1))

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=False,
    continue_final_message=True,
    return_dict=True,
    return_tensors="pt",
)

model_inputs = {
    "tokenized_data": inputs,
    "ego_history_xyz": data["ego_history_xyz"],
    "ego_history_rot": data["ego_history_rot"],
}

model_inputs = helper.to_device(model_inputs, "cuda")
torch.cuda.manual_seed_all(42)
with torch.autocast("cuda", dtype=torch.bfloat16):
    pred_xyz, pred_rot, extra = model.sample_trajectories_from_data_with_vlm_rollout(
        data=model_inputs,
        top_p=0.98,
        temperature=0.6,
        num_traj_samples=1,  # Feel free to raise this for more output trajectories and CoC traces.
        max_generation_length=256,
        return_extra=True,
    )

 
print("Chain-of-Causation (per trajectory):\n", extra["cot"][0])
gt_xy = data["ego_future_xyz"].cpu()[0, 0, :, :2].T.numpy()
pred_xy = pred_xyz.cpu().numpy()[0, 0, :, :, :2].transpose(0, 2, 1)
diff = np.linalg.norm(pred_xy - gt_xy[None, ...], axis=1).mean(-1)
min_ade = diff.min()
print("minADE:", min_ade, "meters")
print(
    "Note: VLA-reasoning models produce nondeterministic outputs due to trajectory sampling, "
    "hardware differences, etc. With num_traj_samples=1 (set for GPU memory compatibility), "
    "variance in minADE is expected. For visual sanity checks, see notebooks/inference.ipynb"
)
```
--------------------
```Result:


Chain-of-Causation (per trajectory):
[['Nudge to the left to pass the stopped truck encroaching into the lane.']]
minADE: 1.7749525 meters
Note: VLA-reasoning models produce nondeterministic outputs due to trajectory sampling, hardware differences, etc. With num_traj_samples=1 (set for GPU memory compatibility), variance in minADE is expected. For visual sanity checks, see notebooks/inference.ipynb
```


나는 1장의 이미지로 판독하는 것을 테스트 하려고 아래와 같은 예제를 만들었다.
데이터 로딩 없이 기본 초기화를 하여 시작점에서 시작하는 것에서 시작한다.
구동하기 위해서 최소 12G 이상인 GPU카드를 사용해야 하고, 응답속도 도 꽤 지연이 걸려
실제 자동차에 적용하기에 무리인것 같다.

```python
#ZeroTime init Base Image(1 photo on load image)
import torch
import numpy as np
from PIL import Image
from alpamayo_r1.models.alpamayo_r1 import AlpamayoR1
from alpamayo_r1.load_physical_aiavdataset import load_physical_aiavdataset
from alpamayo_r1 import helper
 
num_history_steps = 16   # 과거 스텝 수
num_future_steps = 64    # 미래 스텝 수

# 더미 위치 데이터 (xyz 좌표)
ego_history_xyz = torch.zeros((1, 1, num_history_steps, 3))   # (batch, agent, steps, xyz)
ego_future_xyz  = torch.zeros((1, 1, num_future_steps, 3))

# 더미 회전 데이터 (3x3 회전행렬)
ego_history_rot = torch.eye(3).repeat(1, 1, num_history_steps, 1, 1)  # (1,1,steps,3,3)
ego_future_rot  = torch.eye(3).repeat(1, 1, num_future_steps, 1, 1)

print("ego_history_xyz:", ego_history_xyz.shape)
print("ego_future_xyz:", ego_future_xyz.shape)
print("ego_history_rot:", ego_history_rot.shape)
print("ego_future_rot:", ego_future_rot.shape)
N_cameras = 1 
camera_indices = torch.arange(N_cameras, dtype=torch.long)  # (N_cameras,) - long 타입 명시

data={
        "camera_indices": camera_indices,  # (N_cameras,)
        "ego_history_xyz": ego_history_xyz,  # (1, 1, num_history_steps, 3)
        "ego_history_rot": ego_history_rot,  # (1, 1, num_history_steps, 3, 3)
        "ego_future_xyz": ego_future_xyz,  # (1, 1, num_future_steps, 3)
        "ego_future_rot": ego_future_rot,  # (1, 1, num_future_steps, 3, 3)
#        "relative_timestamps": relative_timestamps,  # (N_cameras, num_frames)
#        "absolute_timestamps": absolute_timestamps # (N_cameras, num_frames)
}
img_path = "IMG_20260116_065921.jpg" 
# 예측하고 싶은 JPG 파일 경로
image = Image.open(img_path).convert("RGB") 
# helper.create_message는 tensor 입력을 기대하므로 변환
# PIL Image를 numpy array로 변환 후 float32로 변환
image_array = np.array(image).astype(np.float32) / 255.0  # 0-1 범위로 정규화
image_tensor = torch.from_numpy(image_array).unsqueeze(0)  # [batch, H, W, C]
# 메시지 생성 
messages = helper.create_message(image_tensor)
 
# Example clip ID
model_path = "Alpamayo-R1-10B-4bit"
model = AlpamayoR1.from_pretrained(model_path, dtype=torch.bfloat16).to("cuda")
processor = helper.get_processor(model.tokenizer)
 
 

# 설정값

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=False,
    continue_final_message=True,
    return_dict=True,
    return_tensors="pt",
)

model_inputs = {
    "tokenized_data": inputs,
    "ego_history_xyz": data["ego_history_xyz"],
    "ego_history_rot": data["ego_history_rot"],
}

model_inputs = helper.to_device(model_inputs, "cuda")

torch.cuda.manual_seed_all(42)
with torch.autocast("cuda", dtype=torch.bfloat16):
    pred_xyz, pred_rot, extra = model.sample_trajectories_from_data_with_vlm_rollout(
        data=model_inputs,
        top_p=0.98,
        temperature=0.6,
        num_traj_samples=1,  # Feel free to raise this for more output trajectories and CoC traces.
        max_generation_length=256,
        return_extra=True,
    )

# the size is [batch_size, num_traj_sets, num_traj_samples]
print("Chain-of-Causation (per trajectory):\n", extra["cot"][0])

gt_xy = data["ego_future_xyz"].cpu()[0, 0, :, :2].T.numpy()
pred_xy = pred_xyz.cpu().numpy()[0, 0, :, :, :2].transpose(0, 2, 1)
diff = np.linalg.norm(pred_xy - gt_xy[None, ...], axis=1).mean(-1)
min_ade = diff.min()
print("minADE:", min_ade, "meters")
print(
    "Note: VLA-reasoning models produce nondeterministic outputs due to trajectory sampling, "
    "hardware differences, etc. With num_traj_samples=1 (set for GPU memory compatibility), "
    "variance in minADE is expected. For visual sanity checks, see notebooks/inference.ipynb"
)
```

```output

Chain-of-Causation (per trajectory):
 [['Keep lane to continue driving since the lane ahead is clear.']]
minADE: 0.55852604 meters
Note: VLA-reasoning models produce nondeterministic outputs due to trajectory sampling, hardware differences, etc. With num_traj_samples=1 (set for GPU memory compatibility), variance in minADE is expected. For visual sanity checks, see notebooks/inference.ipynb

```